# Copyright 2018 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import dataclasses
import gc
import json
import math
import os
import random
import re
import subprocess
import sys
import tempfile
import unittest
from functools import partial
from pathlib import Path
from typing import Any
from unittest.mock import Mock, patch

import numpy as np
import pytest
from huggingface_hub import ModelCard, create_branch, list_repo_commits, list_repo_files
from parameterized import parameterized

from transformers import (
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    BitsAndBytesConfig,
    DataCollatorForLanguageModeling,
    IntervalStrategy,
    PreTrainedConfig,
    TrainerCallback,
    TrainingArguments,
    default_data_collator,
    get_polynomial_decay_schedule_with_warmup,
    is_datasets_available,
    is_torch_available,
    logging,
    set_seed,
)
from transformers.hyperparameter_search import ALL_HYPERPARAMETER_SEARCH_BACKENDS
from transformers.testing_utils import (
    ENDPOINT_STAGING,
    TOKEN,
    USER,
    CaptureLogger,
    LoggingLevel,
    TemporaryHubRepo,
    TestCasePlus,
    backend_device_count,
    backend_empty_cache,
    backend_max_memory_allocated,
    backend_memory_allocated,
    backend_reset_max_memory_allocated,
    evaluate_side_effect_factory,
    execute_subprocess_async,
    get_steps_per_epoch,
    get_tests_dir,
    is_staging_test,
    require_accelerate,
    require_apollo_torch,
    require_bitsandbytes,
    require_deepspeed,
    require_galore_torch,
    require_grokadamw,
    require_liger_kernel,
    require_lomo,
    require_non_hpu,
    require_optuna,
    require_peft,
    require_ray,
    require_schedulefree,
    require_sentencepiece,
    require_tensorboard,
    require_tokenizers,
    require_torch,
    require_torch_accelerator,
    require_torch_bf16,
    require_torch_fp16,
    require_torch_gpu,
    require_torch_multi_accelerator,
    require_torch_non_multi_accelerator,
    require_torch_optimi,
    require_torch_tf32,
    require_torch_up_to_2_accelerators,
    require_vision,
    require_wandb,
    run_first,
    run_test_using_subprocess,
    slow,
    torch_device,
)
from transformers.trainer_utils import (
    PREFIX_CHECKPOINT_DIR,
    HPSearchBackend,
    check_target_module_exists,
    get_last_checkpoint,
)
from transformers.training_args import OptimizerNames
from transformers.utils import (
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_torchao_available,
    is_torchdistx_available,
)
from transformers.utils.hp_naming import TrialShortNamer


if torch_device == "hpu":
    RTOL = 1e-3
    ATOL = 1e-3
else:
    RTOL = 1e-5
    ATOL = 1e-5

if is_torch_available():
    import safetensors.torch
    import torch
    from torch import nn
    from torch.utils.data import IterableDataset

    import transformers.optimization
    from transformers import (
        AutoModelForCausalLM,
        AutoModelForSequenceClassification,
        EarlyStoppingCallback,
        GlueDataset,
        GlueDataTrainingArguments,
        GPT2Config,
        GPT2LMHeadModel,
        LlamaConfig,
        LlamaForCausalLM,
        PreTrainedModel,
        Trainer,
        TrainerState,
    )
    from transformers.trainer_pt_utils import AcceleratorConfig

if is_datasets_available():
    import datasets

# for version specific tests in TrainerIntegrationTest
if is_accelerate_available():
    from accelerate import Accelerator
    from accelerate.state import AcceleratorState


PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"


def get_dataset(file_path, tokenizer, max_len):
    dataset = datasets.load_dataset("text", data_files=file_path)

    # Filter out empty lines
    dataset = dataset.filter(lambda example: len(example["text"].strip()) > 0)

    # Define tokenization function
    def tokenize_function(examples):
        tokenized = tokenizer(examples["text"], add_special_tokens=True, truncation=True, max_length=max_len)
        # Add labels as a copy of input_ids
        tokenized["labels"] = tokenized["input_ids"].copy()
        return tokenized

    # Apply tokenization and remove original text column
    tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"])

    return tokenized_dataset["train"]


class StoreLossCallback(TrainerCallback):
    """
    Simple callback to store the loss.
    """

    def __init__(self):
        self.losses = []

    def on_log(self, args, state, control, logs=None, **kwargs):
        if "loss" in logs:
            self.losses.append(logs["loss"])


class MockCudaOOMCallback(TrainerCallback):
    """
    Simple callback to simulate CUDA OOM error if
    the batch size is >= to `batch_size_limit`.
    """

    def __init__(self, batch_size_limit=16):
        self.batch_size_limit = batch_size_limit

    def on_step_end(self, args, state, control, **kwargs):
        # simulate OOM on the first step
        if state.train_batch_size >= self.batch_size_limit:
            raise RuntimeError("CUDA out of memory.")


def ForCausalLMLoss(logits, labels, vocab_size, num_items_in_batch, disable_num_items_in_batch=False):
    # Upcast to float if we need to compute the loss to avoid potential precision issues
    logits = logits.float()
    # Shift so that tokens < n predict n
    shift_logits = logits[..., :-1, :].contiguous()
    shift_labels = labels[..., 1:].contiguous()

    # Flatten the tokens
    shift_logits = shift_logits.view(-1, vocab_size)
    shift_labels = shift_labels.view(-1)
    # Enable model parallelism
    shift_labels = shift_labels.to(shift_logits.device)
    if num_items_in_batch is None or disable_num_items_in_batch:
        loss = nn.functional.cross_entropy(shift_logits, shift_labels, ignore_index=-100, reduction="mean")
    else:
        loss = nn.functional.cross_entropy(shift_logits, shift_labels, ignore_index=-100, reduction="sum")
        loss = loss / num_items_in_batch
    return loss


class RegressionDataset:
    def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
        np.random.seed(seed)
        self.label_names = ["labels"] if label_names is None else label_names
        self.length = length
        self.x = np.random.normal(size=(length,)).astype(np.float32)
        self.ys = [a * self.x + b + np.random.normal(scale=0.1, size=(length,)) for _ in self.label_names]
        self.ys = [y.astype(np.float32) for y in self.ys]

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        result = {name: y[i] for name, y in zip(self.label_names, self.ys)}
        result["input_x"] = self.x[i]
        return result


# Converting Bytes to Megabytes
def bytes2megabytes(x):
    return int(x / 2**20)


# Copied from accelerate: https://github.com/huggingface/accelerate/blob/ee163b66fb7848892519e804688cb4ae981aacbe/src/accelerate/test_utils/scripts/external_deps/test_peak_memory_usage.py#L40C1-L73C68
class TorchTracemalloc:
    def __enter__(self):
        gc.collect()
        if torch_device in ["cuda", "xpu"]:
            backend_empty_cache(torch_device)
            backend_reset_max_memory_allocated(torch_device)  # reset the peak gauge to zero
            self.begin = backend_memory_allocated(torch_device)
        return self

    def __exit__(self, *exc):
        gc.collect()
        if torch_device in ["cuda", "xpu"]:
            backend_empty_cache(torch_device)
            self.end = backend_memory_allocated(torch_device)
            self.peak = backend_max_memory_allocated(torch_device)
        self.used = bytes2megabytes(self.end - self.begin)
        self.peaked = bytes2megabytes(self.peak - self.begin)


@dataclasses.dataclass
class RegressionTrainingArguments(TrainingArguments):
    a: float = 0.0
    b: float = 0.0
    keep_report_to: bool = False

    def __post_init__(self):
        super().__post_init__()
        # save resources not dealing with reporting unless specified (also avoids the warning when it's not set)
        # can be explicitly disabled via `keep_report_to`
        if not self.keep_report_to:
            self.report_to = []


class RepeatDataset:
    def __init__(self, x, length=64):
        self.x = x
        self.length = length

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        return {"input_ids": self.x, "labels": self.x}


class SequenceClassificationDataset:
    def __init__(self, length=64, vocab_size=100, num_labels=5):
        self.length = length
        self.sequences = [torch.randint(0, vocab_size, (64,)).tolist() for _ in range(length)]
        self.labels = torch.randint(0, num_labels, (length,)).tolist()

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        return {"input_ids": self.sequences[i], "label": self.labels[i]}


class DynamicShapesDataset:
    def __init__(self, length=64, seed=42, batch_size=8):
        self.length = length
        np.random.seed(seed)
        sizes = np.random.randint(1, 20, (length // batch_size,))
        # For easy batching, we make every batch_size consecutive samples the same size.
        self.xs = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]
        self.ys = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        return {"input_x": self.xs[i], "labels": self.ys[i]}


class AlmostAccuracy:
    def __init__(self, thresh=0.25):
        self.thresh = thresh

    def __call__(self, eval_pred):
        predictions, labels = eval_pred
        true = np.abs(predictions - labels) <= self.thresh
        return {"accuracy": true.astype(np.float32).mean().item()}


class AlmostAccuracyBatched:
    def __init__(self, thresh=0.25):
        self.thresh = thresh
        self.batch_acc = []

    def __call__(self, eval_pred, compute_result):
        predictions, labels = eval_pred
        if isinstance(predictions, tuple):
            predictions = predictions[0]
        if isinstance(labels, tuple):
            labels = labels[0]
        batch_size = len(predictions)
        true = torch.abs(predictions - labels) <= self.thresh
        acc = true.type(torch.FloatTensor).mean().item()
        self.batch_acc.extend([acc] * batch_size)
        if compute_result:
            result = {"accuracy": np.mean(self.batch_acc).item()}
            self.batch_acc = []
            return result


class RegressionModelConfig(PreTrainedConfig):
    def __init__(self, a=0, b=0, double_output=False, random_torch=True, **kwargs):
        super().__init__(**kwargs)
        self.a = a
        self.b = b
        self.double_output = double_output
        self.random_torch = random_torch
        self.hidden_size = 1


if is_torch_available():

    class SampleIterableDataset(IterableDataset):
        def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
            self.dataset = RegressionDataset(a=a, b=b, length=length, seed=seed, label_names=label_names)

        def __iter__(self):
            for i in range(len(self.dataset)):
                yield self.dataset[i]

    class FiniteIterableDataset(SampleIterableDataset):
        def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
            super().__init__(a, b, length, seed, label_names)
            self.current_sample = 0

        def __iter__(self):
            while self.current_sample < len(self.dataset):
                yield self.dataset[self.current_sample]
                self.current_sample += 1

    class MultiLoader:
        def __init__(self, loaders):
            self.loaders = loaders

        def __len__(self):
            return sum(len(loader) for loader in self.loaders)

        def __iter__(self):
            for loader in self.loaders:
                yield from loader

    class CustomDataloaderTrainer(Trainer):
        def get_train_dataloader(self):
            dataloaders = [super().get_train_dataloader(), super().get_train_dataloader()]
            return MultiLoader(dataloaders)

        def get_eval_dataloader(self, eval_dataset):
            dataloaders = [super().get_eval_dataloader(eval_dataset), super().get_eval_dataloader(eval_dataset)]
            return MultiLoader(dataloaders)

    class RegressionModel(nn.Module):
        def __init__(self, a=0, b=0, double_output=False):
            super().__init__()
            self.a = nn.Parameter(torch.tensor(a).float())
            self.b = nn.Parameter(torch.tensor(b).float())
            self.double_output = double_output
            self.config = None

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            if labels is None:
                return (y, y) if self.double_output else (y,)
            loss = nn.functional.mse_loss(y, labels)
            return (loss, y, y) if self.double_output else (loss, y)

    class RegressionDictModel(nn.Module):
        def __init__(self, a=0, b=0):
            super().__init__()
            self.a = nn.Parameter(torch.tensor(a).float())
            self.b = nn.Parameter(torch.tensor(b).float())
            self.config = None

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            result = {"output": y}
            if labels is not None:
                result["loss"] = nn.functional.mse_loss(y, labels)
            return result

    class RegressionPreTrainedModel(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"

        def __init__(self, config):
            super().__init__(config)
            self.a = nn.Parameter(torch.tensor(config.a).float())
            self.b = nn.Parameter(torch.tensor(config.b).float())
            self.double_output = config.double_output
            self.post_init()

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            if labels is None:
                return (y, y) if self.double_output else (y,)
            loss = nn.functional.mse_loss(y, labels)
            return (loss, y, y) if self.double_output else (loss, y)

    class RegressionPreTrainedModelWithGradientCheckpointing(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"
        supports_gradient_checkpointing = True

        def __init__(self, config):
            super().__init__(config)
            self.layers = nn.ModuleList([nn.Linear(config.hidden_size, config.hidden_size) for _ in range(4)])
            self.head = nn.Linear(config.hidden_size, 1)
            self.gradient_checkpointing = False
            self.double_output = config.double_output
            self.post_init()

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x.unsqueeze(0)

            for layer in self.layers:
                if self.training and self.gradient_checkpointing:
                    outputs = self._gradient_checkpointing_func(layer.__call__, y)
                else:
                    outputs = layer(y)

                y = outputs * 3

            logits = self.head(y)

            if labels is None:
                return (logits, logits) if self.double_output else (logits,)

            loss = nn.functional.mse_loss(logits, labels)

            return (loss, y, y) if self.double_output else (loss, y)

    class RegressionRandomPreTrainedModel(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"

        def __init__(self, config):
            super().__init__(config)
            self.a = nn.Parameter(torch.tensor(config.a).float())
            self.b = nn.Parameter(torch.tensor(config.b).float())
            self.random_torch = config.random_torch
            self.post_init()

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            if self.random_torch:
                torch_rand = torch.randn(1).squeeze()
            np_rand = np.random.rand()
            rand_rand = random.random()

            if self.random_torch:
                y += 0.05 * torch_rand
            y += 0.05 * torch.tensor(np_rand + rand_rand)

            if labels is None:
                return (y,)
            loss = nn.functional.mse_loss(y, labels)
            return (loss, y)

    class BasicTextGenerationModel(nn.Module):
        def __init__(self, vocab_size, hidden_size):
            super().__init__()
            self.embedding = nn.Embedding(vocab_size, hidden_size)
            self.lstm = nn.LSTM(hidden_size, hidden_size, batch_first=True)
            self.fc = nn.Linear(hidden_size, vocab_size)

        def forward(self, input_ids, labels=None, **kwargs):
            embedded = self.embedding(input_ids)
            lstm_out, _ = self.lstm(embedded)
            logits = self.fc(lstm_out)
            if labels is None:
                return logits

            loss = nn.functional.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1))
            return loss, logits

    def create_dummy_dataset_for_text_generation(vocab_size, seq_length, num_samples):
        import numpy as np

        # Create random input sequences
        input_ids = np.random.randint(0, vocab_size, (num_samples, seq_length))

        # Create a datasets.Dataset
        dataset = datasets.Dataset.from_dict({"input_ids": input_ids, "labels": input_ids})

        return dataset

    class TstLayer(nn.Module):
        def __init__(self, hidden_size):
            super().__init__()
            self.linear1 = nn.Linear(hidden_size, hidden_size)
            self.ln1 = nn.LayerNorm(hidden_size)
            self.linear2 = nn.Linear(hidden_size, hidden_size)
            self.ln2 = nn.LayerNorm(hidden_size)
            self.bias = nn.Parameter(torch.zeros(hidden_size))

        def forward(self, x):
            h = self.ln1(nn.functional.relu(self.linear1(x)))
            h = nn.functional.relu(self.linear2(x))
            return self.ln2(x + h + self.bias)

    def get_regression_trainer(
        a=0,
        b=0,
        double_output=False,
        train_len=64,
        eval_len=64,
        pretrained=True,
        keep_report_to=False,
        output_dir=None,
        **kwargs,
    ):
        label_names = kwargs.get("label_names")
        gradient_checkpointing = kwargs.get("gradient_checkpointing", False)
        train_dataset = RegressionDataset(length=train_len, label_names=label_names)
        eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)

        model_init = kwargs.pop("model_init", None)
        if model_init is not None:
            model = None
        else:
            if pretrained:
                config = RegressionModelConfig(a=a, b=b, double_output=double_output)
                # We infer the correct model class if one uses gradient_checkpointing or not
                target_cls = (
                    RegressionPreTrainedModel
                    if not gradient_checkpointing
                    else RegressionPreTrainedModelWithGradientCheckpointing
                )
                model = target_cls(config)
            else:
                model = RegressionModel(a=a, b=b, double_output=double_output)

        compute_metrics = kwargs.pop("compute_metrics", None)
        data_collator = kwargs.pop("data_collator", None)
        optimizers = kwargs.pop("optimizers", (None, None))
        preprocess_logits_for_metrics = kwargs.pop("preprocess_logits_for_metrics", None)
        assert output_dir is not None, "output_dir should be specified for testing"
        args = RegressionTrainingArguments(output_dir, a=a, b=b, keep_report_to=keep_report_to, **kwargs)
        trainer = Trainer(
            model,
            args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            compute_metrics=compute_metrics,
            optimizers=optimizers,
            model_init=model_init,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )
        # TODO: loss function defined in RegressionModel doesn't accept num_item_per_batch, to fix later
        trainer.model_accepts_loss_kwargs = False
        return trainer

    def get_language_model_trainer(**kwargs):
        dataset = datasets.load_dataset("fka/awesome-chatgpt-prompts")
        model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
        tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
        tokenizer.pad_token = tokenizer.eos_token

        def _tokenize_function(examples):
            model_inputs = tokenizer(examples["prompt"], padding="max_length", truncation=True)
            model_inputs["labels"] = np.array(model_inputs["input_ids"]).astype(np.int64)
            return model_inputs

        tokenized_datasets = dataset.map(_tokenize_function, batched=True)
        training_args = TrainingArguments(**kwargs)

        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=tokenized_datasets["train"],
        )

        return trainer


class TrainerIntegrationCommon:
    def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True, use_scaler=False):
        weights_file = SAFE_WEIGHTS_NAME
        file_list = [weights_file, "training_args.bin", "optimizer.pt", "scheduler.pt", "trainer_state.json"]
        if is_pretrained:
            file_list.append("config.json")
        if use_scaler:
            file_list.append("scaler.pt")
        for step in range(freq, total, freq):
            checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
            self.assertTrue(os.path.isdir(checkpoint))
            for filename in file_list:
                self.assertTrue(os.path.isfile(os.path.join(checkpoint, filename)))

    def check_best_model_has_been_loaded(
        self,
        output_dir,
        freq,
        total,
        trainer,
        metric,
        greater_is_better=False,
        is_pretrained=True,
    ):
        checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}")
        log_history = TrainerState.load_from_json(os.path.join(checkpoint, "trainer_state.json")).log_history

        values = [d[metric] for d in log_history]
        best_value = max(values) if greater_is_better else min(values)
        best_checkpoint = (values.index(best_value) + 1) * freq
        checkpoint = os.path.join(output_dir, f"checkpoint-{best_checkpoint}")
        if is_pretrained:
            best_model = RegressionPreTrainedModel.from_pretrained(checkpoint)
            best_model.to(trainer.args.device)
        else:
            best_model = RegressionModel()
            state_dict = safetensors.torch.load_file(os.path.join(checkpoint, SAFE_WEIGHTS_NAME))
            best_model.load_state_dict(state_dict)
            best_model.to(trainer.args.device)
        torch.testing.assert_close(best_model.a, trainer.model.a)
        torch.testing.assert_close(best_model.b, trainer.model.b)

        metrics = trainer.evaluate()
        self.assertEqual(metrics[metric], best_value)

    def remove_nan_logs(self, log):
        for key in list(log.keys()):
            if log[key] != log[key]:  # Check if the value is NaN
                del log[key]

    def check_trainer_state_are_the_same(self, trainer_state, trainer_state1):
        # We'll pop things so operate on copies.
        state = trainer_state.copy()
        state1 = trainer_state1.copy()
        # Log history main contain different logs for the time metrics (after resuming a training).
        log_history = state.pop("log_history", None)
        log_history1 = state1.pop("log_history", None)
        self.assertEqual(state, state1)
        skip_log_keys = ["train_runtime", "train_samples_per_second", "train_steps_per_second", "train_loss"]
        for log, log1 in zip(log_history, log_history1):
            for key in skip_log_keys:
                _ = log.pop(key, None)
                _ = log1.pop(key, None)

            self.remove_nan_logs(log)
            self.remove_nan_logs(log1)

            self.assertEqual(log, log1)

    def convert_to_sharded_checkpoint(self, folder):
        # Converts a checkpoint of a regression model to a sharded checkpoint.
        loader = safetensors.torch.load_file
        weights_file = os.path.join(folder, SAFE_WEIGHTS_NAME)

        extension = "safetensors"
        saver = safetensors.torch.save_file
        index_file = os.path.join(folder, SAFE_WEIGHTS_INDEX_NAME)
        shard_name = SAFE_WEIGHTS_NAME

        state_dict = loader(weights_file)

        os.remove(weights_file)
        keys = list(state_dict.keys())

        shard_files = [
            shard_name.replace(f".{extension}", f"-{idx + 1:05d}-of-{len(keys):05d}.{extension}")
            for idx in range(len(keys))
        ]
        index = {"metadata": {}, "weight_map": {key: shard_files[i] for i, key in enumerate(keys)}}

        with open(index_file, "w", encoding="utf-8") as f:
            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
            f.write(content)

        for param_name, shard_file in zip(keys, shard_files):
            saver({param_name: state_dict[param_name]}, os.path.join(folder, shard_file))


@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
    """
    Only tests that want to tap into the auto-pre-run 2 trainings:
    - self.default_trained_model
    - self.alternate_trained_model
    directly, or via check_trained_model
    """

    def setUp(self):
        super().setUp()
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, output_dir=tmp_dir)
            trainer.train()
            self.default_trained_model = (trainer.model.a, trainer.model.b)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, seed=314, output_dir=tmp_dir)
            trainer.train()
            self.alternate_trained_model = (trainer.model.a, trainer.model.b)

    def check_trained_model(self, model, alternate_seed=False, **kwargs):
        # Checks a training seeded with learning_rate = 0.1
        (a, b) = self.alternate_trained_model if alternate_seed else self.default_trained_model
        torch.testing.assert_close(model.a, a, **kwargs)
        torch.testing.assert_close(model.b, b, **kwargs)

    def test_reproducible_training(self):
        # Checks that training worked, model trained and seed made a reproducible training.
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, output_dir=tmp_dir)
            trainer.train()
            self.check_trained_model(trainer.model)

        # Checks that a different seed gets different (reproducible) results.
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, seed=314, output_dir=tmp_dir)
            trainer.train()
            self.check_trained_model(trainer.model, alternate_seed=True)

    def test_trainer_with_datasets(self):
        np.random.seed(42)
        x = np.random.normal(size=(64,)).astype(np.float32)
        y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,)).astype(np.float32)
        train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})

        # Base training. Should have the same results as test_reproducible_training
        model = RegressionModel()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, learning_rate=0.1, report_to="none")
            trainer = Trainer(model, args, train_dataset=train_dataset)
            trainer.train()
            self.check_trained_model(trainer.model)

            # Can return tensors.
            train_dataset.set_format(type="torch", dtype=torch.float32)
            model = RegressionModel()
            trainer = Trainer(model, args, train_dataset=train_dataset)
            trainer.train()
            self.check_trained_model(trainer.model)

            # Adding one column not used by the model should have no impact
            z = np.random.normal(size=(64,)).astype(np.float32)
            train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y, "extra": z})
            model = RegressionModel()
            trainer = Trainer(model, args, train_dataset=train_dataset)
            trainer.train()
            self.check_trained_model(trainer.model)

    def test_model_init(self):
        train_dataset = RegressionDataset()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, learning_rate=0.1, report_to="none")
            trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
            trainer.train()
            self.check_trained_model(trainer.model)

            # Re-training should restart from scratch, thus lead the same results.
            trainer.train()
            self.check_trained_model(trainer.model)

            # Re-training should restart from scratch, thus lead the same results and new seed should be used.
            trainer.args.seed = 314
            trainer.train()
            self.check_trained_model(trainer.model, alternate_seed=True)

    @slow
    def test_gradient_accumulation_loss_alignment_with_model_loss(self):
        set_seed(42)

        model_name = "nickypro/tinyllama-15M"
        dataset_name = "wikitext"
        dataset_config = "wikitext-2-raw-v1"
        dataset = datasets.load_dataset(dataset_name, dataset_config, split="train[:40]")
        tokenizer = AutoTokenizer.from_pretrained(model_name)

        tokenizer.pad_token = tokenizer.eos_token

        def tokenize_function(examples):
            return tokenizer(examples["text"], max_length=16, padding="max_length", truncation=True)

        tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=dataset.column_names)

        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

        args_kwargs = {
            "report_to": "none",
            "logging_steps": 1,
            "max_steps": 5,
            "learning_rate": 3e-4,
            "disable_tqdm": True,
        }

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                **args_kwargs,
            )
            # train with base loss
            set_seed(42)
            model = AutoModelForCausalLM.from_pretrained(model_name, dtype=torch.float32)
            base_loss_callback = StoreLossCallback()
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[base_loss_callback],
                data_collator=data_collator,
            )
            assert trainer.model_accepts_loss_kwargs
            trainer.train()

            args = TrainingArguments(
                tmp_dir,
                **args_kwargs,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
            )

            # train with gradient accumulation
            set_seed(42)
            model = AutoModelForCausalLM.from_pretrained(model_name)
            grad_accum_loss_callback = StoreLossCallback()
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[grad_accum_loss_callback],
                data_collator=data_collator,
            )
            assert trainer.model_accepts_loss_kwargs
            trainer.train()

            # train with broken loss
            set_seed(42)
            model = AutoModelForCausalLM.from_pretrained(model_name)
            broken_loss_callback = StoreLossCallback()
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[broken_loss_callback],
                data_collator=data_collator,
            )
            # disable model_accepts_loss_kwargs so that "num_items_in_batch" is not passed to the model
            trainer.model_accepts_loss_kwargs = False
            trainer.train()

        # Calculate the difference between the base loss and the grad_accum loss
        diff_truth = [
            abs(base - grad) for base, grad in zip(base_loss_callback.losses, grad_accum_loss_callback.losses)
        ]
        diff_broken = [abs(base - grad) for base, grad in zip(base_loss_callback.losses, broken_loss_callback.losses)]

        # all diff truth should be quite close
        self.assertLess(max(diff_truth), 0.01, f"Difference {max(diff_truth)} is not within 0.01")
        # max diff broken should be very off ("very off" is arbitrary, but as long as it's bigger than 0.1, it's fine)
        self.assertGreater(max(diff_broken), 0.7, f"Difference {max(diff_broken)} is not greater than 0.7")

        loss_base = sum(base_loss_callback.losses)
        loss_broken = sum(broken_loss_callback.losses)

        # mean/sum loss should not vary too much.
        relative_diff = abs(loss_base - loss_broken) / max(loss_base, loss_broken)
        self.assertLess(relative_diff, 0.2, f"Relative difference {relative_diff} is not within 0.2")

    def test_gradient_accumulation_loss_alignment_with_loss_func(self):
        set_seed(42)

        model_name = "roneneldan/TinyStories-33M"
        dataset_name = "wikitext"
        dataset_config = "wikitext-2-raw-v1"
        dataset = datasets.load_dataset(dataset_name, dataset_config, split="train[:40]")
        tokenizer = AutoTokenizer.from_pretrained(model_name)

        tokenizer.pad_token = tokenizer.eos_token

        def tokenize_function(examples):
            return tokenizer(examples["text"], max_length=16, padding="max_length", truncation=True)

        tokenized_dataset = dataset.map(tokenize_function, batched=True)

        tokenizer.pad_token = tokenizer.eos_token
        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

        model = AutoModelForCausalLM.from_pretrained(model_name)

        def compute_loss(logits, labels, vocab_size, num_items_in_batch, disable_num_items_in_batch=False):
            return ForCausalLMLoss(
                logits["logits"], labels, vocab_size, num_items_in_batch, disable_num_items_in_batch
            )

        loss_fn = partial(compute_loss, vocab_size=model.config.vocab_size, disable_num_items_in_batch=False)

        base_loss_callback = StoreLossCallback()

        args_kwargs = {
            "report_to": "none",
            "logging_steps": 1,
            "max_steps": 5,
            "learning_rate": 3e-4,
            "disable_tqdm": True,
        }

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                **args_kwargs,
            )
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[base_loss_callback],
                compute_loss_func=loss_fn,
                data_collator=data_collator,
            )
            trainer.train()

        grad_accum_loss_callback = StoreLossCallback()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                **args_kwargs,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
            )
            set_seed(42)
            model = AutoModelForCausalLM.from_pretrained(model_name)
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[grad_accum_loss_callback],
                compute_loss_func=loss_fn,
                data_collator=data_collator,
            )
            trainer.train()

            set_seed(42)
            model = AutoModelForCausalLM.from_pretrained(model_name)
            broken_loss_callback = StoreLossCallback()
            loss_fn = partial(compute_loss, vocab_size=model.config.vocab_size, disable_num_items_in_batch=True)
            trainer = Trainer(
                model,
                args,
                train_dataset=tokenized_dataset,
                callbacks=[broken_loss_callback],
                compute_loss_func=loss_fn,
                data_collator=data_collator,
            )
            trainer.train()

            # Calculate the difference between the base loss and the grad_accum loss
            diff_truth = [
                abs(base - grad) for base, grad in zip(base_loss_callback.losses, grad_accum_loss_callback.losses)
            ]
            diff_broken = [
                abs(base - grad) for base, grad in zip(base_loss_callback.losses, broken_loss_callback.losses)
            ]

            # all diff truth should be quite close
            self.assertLess(max(diff_truth), 0.01, f"Difference {max(diff_truth)} is not within 0.01")

            # max diff broken should be very off
            self.assertGreater(max(diff_broken), 3, f"Difference {max(diff_broken)} is not greater than 3")

    def test_gradient_accumulation(self):
        # Training with half the batch size but accumulation steps as 2 should give the same training losses.
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                gradient_accumulation_steps=2, per_device_train_batch_size=4, learning_rate=0.1, output_dir=tmp_dir
            )
            trainer.train()
            self.check_trained_model(trainer.model)

    def test_gradient_checkpointing(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                per_device_train_batch_size=1,
                learning_rate=0.1,
                gradient_checkpointing=True,
                gradient_checkpointing_kwargs={"use_reentrant": False},
                output_dir=tmp_dir,
            )
            previous_params = {k: v.detach().clone() for k, v in trainer.model.named_parameters()}

            trainer.train()

            # Check if model weights have been updated
            for k, v in trainer.model.named_parameters():
                self.assertFalse(
                    torch.allclose(previous_params[k], v, rtol=1e-4, atol=1e-4),
                    f"Model weights for {k} have not been updated",
                )

    def test_training_loss(self):
        n_gpus = max(1, backend_device_count(torch_device))

        # With even logs
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(logging_steps=64 / (8 * n_gpus), output_dir=tmp_dir)
            trainer.train()
            log_history = trainer.state.log_history

            losses = [log["loss"] for log in log_history if "loss" in log]
            train_loss = log_history[-1]["train_loss"]
            self.assertAlmostEqual(sum(losses) / len(losses), train_loss, places=4)

        # With uneven logs
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(logging_steps=5, output_dir=tmp_dir)
            trainer.train()
            log_history = trainer.state.log_history

            # Training loss should be the same as before
            new_train_loss = log_history[-1]["train_loss"]
            self.assertAlmostEqual(train_loss, new_train_loss, places=4)

    def test_custom_optimizer(self):
        train_dataset = RegressionDataset()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, report_to="none")
            model = RegressionModel()
            optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
            lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: 1.0)
            trainer = Trainer(model, args, train_dataset=train_dataset, optimizers=(optimizer, lr_scheduler))
            trainer.train()

            (a, b) = self.default_trained_model
            self.assertFalse(torch.allclose(trainer.model.a, a))
            self.assertFalse(torch.allclose(trainer.model.b, b))
            self.assertEqual(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 1.0)

    def test_lr_scheduler_kwargs(self):
        # test scheduler kwargs passed via TrainingArguments
        train_dataset = RegressionDataset()
        model = RegressionModel()
        num_steps, num_warmup_steps = 10, 2
        extra_kwargs = {"power": 5.0, "lr_end": 1e-5}  # Non-default arguments
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                lr_scheduler_type="polynomial",
                lr_scheduler_kwargs=extra_kwargs,
                learning_rate=0.2,
                warmup_steps=num_warmup_steps,
                report_to="none",
            )
            trainer = Trainer(model, args, train_dataset=train_dataset)
            trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

            # Checking that the scheduler was created
            self.assertIsNotNone(trainer.lr_scheduler)

            # Checking that the correct args were passed
            sched1 = trainer.lr_scheduler
            sched2 = get_polynomial_decay_schedule_with_warmup(
                trainer.optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_steps, **extra_kwargs
            )
            self.assertEqual(sched1.lr_lambdas[0].args, sched2.lr_lambdas[0].args)
            self.assertEqual(sched1.lr_lambdas[0].keywords, sched2.lr_lambdas[0].keywords)

    def test_cosine_with_min_lr_scheduler(self):
        train_dataset = RegressionDataset()
        model = RegressionModel()
        num_steps, num_warmup_steps = 10, 2
        extra_kwargs = {"min_lr": 1e-5}  # Non-default arguments
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                lr_scheduler_type="cosine_with_min_lr",
                lr_scheduler_kwargs=extra_kwargs,
                learning_rate=0.2,
                warmup_steps=num_warmup_steps,
                report_to="none",
            )
            trainer = Trainer(model, args, train_dataset=train_dataset)
            trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

            # Checking that the scheduler was created
            self.assertIsNotNone(trainer.lr_scheduler)

            # Check the last learning rate
            for _ in range(num_steps):
                trainer.lr_scheduler.step()
            self.assertEqual(trainer.lr_scheduler.get_last_lr()[0], 1e-5)

    def test_cosine_with_min_lr_schedule_with_warmup_lr_rate(self):
        train_dataset = RegressionDataset()
        model = RegressionModel()
        num_steps, num_warmup_steps = 10, 2
        extra_kwargs = {"min_lr": 1e-5}  # Non-default arguments
        args = TrainingArguments(
            "./regression",
            lr_scheduler_type="cosine_warmup_with_min_lr",
            lr_scheduler_kwargs=extra_kwargs,
            learning_rate=0.2,
            warmup_steps=num_warmup_steps,
            report_to="none",
        )
        trainer = Trainer(model, args, train_dataset=train_dataset)
        trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

        # Checking that the scheduler was created
        self.assertIsNotNone(trainer.lr_scheduler)

        # Check the last learning rate
        step_lrs = []
        for _ in range(num_steps):
            step_lrs.append(trainer.optimizer.param_groups[0]["lr"])
            trainer.lr_scheduler.step()
        self.assertEqual(step_lrs[0], 0.1)
        self.assertEqual(step_lrs[1], 0.2)
        self.assertEqual(step_lrs[-1], 1e-05)

    def test_reduce_lr_on_plateau_args(self):
        # test passed arguments for a custom ReduceLROnPlateau scheduler
        train_dataset = RegressionDataset(length=64)
        eval_dataset = RegressionDataset(length=64)
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                eval_strategy="epoch",
                metric_for_best_model="eval_loss",
                report_to="none",
            )
            model = RegressionModel()
            optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
            lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, cooldown=2)
            trainer = Trainer(
                model,
                args,
                train_dataset=train_dataset,
                eval_dataset=eval_dataset,
                optimizers=(optimizer, lr_scheduler),
            )
            trainer.train()

            self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
            self.assertEqual(trainer.lr_scheduler.factor, 0.2)
            self.assertEqual(trainer.lr_scheduler.patience, 5)
            self.assertEqual(trainer.lr_scheduler.cooldown, 2)

    def test_reduce_lr_on_plateau(self):
        # test the ReduceLROnPlateau scheduler

        class TrainerWithLRLogs(Trainer):
            def log(self, logs):
                # the LR is computed after metrics and does not exist for the first epoch
                if hasattr(self.lr_scheduler, "_last_lr"):
                    logs["learning_rate"] = self.lr_scheduler._last_lr[0]
                super().log(logs)

        train_dataset = RegressionDataset(length=64)
        eval_dataset = RegressionDataset(length=64)

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(
                tmp_dir,
                lr_scheduler_type="reduce_lr_on_plateau",
                eval_strategy="epoch",
                metric_for_best_model="eval_loss",
                num_train_epochs=10,
                learning_rate=0.2,
                report_to="none",
            )
            model = RegressionModel()
            trainer = TrainerWithLRLogs(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
            trainer.train()

            self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
            patience = trainer.lr_scheduler.patience

            logs = trainer.state.log_history[1:]
            best_loss = logs[0]["eval_loss"]
            bad_epochs = 0
            for i, log in enumerate(logs[:-1]):  # Compare learning rate to next epoch's
                loss = log["eval_loss"]
                just_decreased = False
                if loss > best_loss:
                    bad_epochs += 1
                    if bad_epochs > patience:
                        self.assertLess(logs[i + 1]["learning_rate"], log["learning_rate"])
                        just_decreased = True
                        bad_epochs = 0
                else:
                    best_loss = loss
                    bad_epochs = 0
                if not just_decreased:
                    self.assertEqual(logs[i + 1]["learning_rate"], log["learning_rate"])

    def test_adafactor_lr_none(self):
        # test the special case where lr=None, since Trainer can't not have lr_scheduler

        from transformers.optimization import Adafactor, AdafactorSchedule

        train_dataset = RegressionDataset()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, report_to="none")
            model = RegressionModel()
            optimizer = Adafactor(
                model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None
            )
            lr_scheduler = AdafactorSchedule(optimizer)
            trainer = Trainer(model, args, train_dataset=train_dataset, optimizers=(optimizer, lr_scheduler))
            trainer.train()

            (a, b) = self.default_trained_model
            self.assertFalse(torch.allclose(trainer.model.a, a))
            self.assertFalse(torch.allclose(trainer.model.b, b))
            self.assertGreater(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 0)

    @require_torch_fp16
    @require_torch_accelerator
    def test_mixed_fp16(self):
        # very basic test
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, fp16=True, logging_steps=1, output_dir=tmp_dir)
            trainer.train()
            self.check_trained_model(trainer.model, atol=ATOL, rtol=RTOL)
            log_0 = trainer.state.log_history[:-1][0]
            # check that the grads were properly clipped due to the grad scaler. Otherwise, we get huge values
            self.assertEqual(log_0["grad_norm"] < 100, True)

    @require_torch_bf16
    @require_torch_accelerator
    def test_mixed_bf16(self):
        # very basic test
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, bf16=True, output_dir=tmp_dir)
            trainer.train()
            self.check_trained_model(trainer.model, atol=ATOL, rtol=RTOL)

    @require_torch_gpu
    @require_torch_tf32
    def test_tf32(self):
        # very basic test
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, tf32=True, output_dir=tmp_dir)
            trainer.train()
            self.check_trained_model(trainer.model)

    def test_include_num_input_tokens_seen(self):
        model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
        tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
        tokenizer.pad_token = "[PAD]"
        model.config.pad_token_id = tokenizer.pad_token_id

        sentences = ["This is a short sentence.", "This is a much longer sentence that will require padding."]
        labels = torch.tensor([0, 1])

        # 1. Test with attention_mask
        tokenized_dataset_with_mask = tokenizer(sentences, truncation=True, padding="longest", return_tensors="pt")
        tokenized_dataset_with_mask["labels"] = labels
        dataset_with_mask = datasets.Dataset.from_dict(tokenized_dataset_with_mask)

        # 2. Test without attention_mask
        tokenized_dataset_no_mask = {k: v for k, v in tokenized_dataset_with_mask.items() if k != "attention_mask"}
        dataset_no_mask = datasets.Dataset.from_dict(tokenized_dataset_no_mask)

        # 3. Test with no padding information
        tokenizer_no_pad = AutoTokenizer.from_pretrained("bert-base-cased")
        tokenizer_no_pad.pad_token = None

        data_collator = default_data_collator

        with tempfile.TemporaryDirectory() as tmp_dir:
            # Test case 1: "non_padding" with attention_mask
            args = TrainingArguments(
                output_dir=tmp_dir,
                include_num_input_tokens_seen="non_padding",
                per_device_train_batch_size=2,
                max_steps=1,
                report_to="none",
            )
            trainer = Trainer(
                model=model,
                args=args,
                train_dataset=dataset_with_mask,
                data_collator=data_collator,
                processing_class=tokenizer,
            )
            trainer.train()
            attention_mask = tokenized_dataset_with_mask["attention_mask"]
            non_padded_tokens_with_mask = attention_mask.sum().item()
            self.assertEqual(trainer.state.num_input_tokens_seen, non_padded_tokens_with_mask)

            # Test case 2: "non_padding" without attention_mask (fallback to pad_token_id)
            trainer = Trainer(
                model=model,
                args=args,
                train_dataset=dataset_no_mask,
                data_collator=data_collator,
                processing_class=tokenizer,
            )
            trainer.train()
            input_ids = tokenized_dataset_with_mask["input_ids"]  # use original to compute expected
            non_padded_tokens_no_mask = (input_ids != tokenizer.pad_token_id).sum().item()
            self.assertEqual(trainer.state.num_input_tokens_seen, non_padded_tokens_no_mask)

            # Test case 3: "non_padding" with no padding info (fallback to numel)
            with self.assertLogs("transformers.trainer", level="WARNING") as cm:
                trainer = Trainer(
                    model=model,
                    args=args,
                    train_dataset=dataset_no_mask,  # still has input_ids
                    data_collator=data_collator,
                    processing_class=tokenizer_no_pad,  # tokenizer without pad token
                )
                trainer.train()
                self.assertTrue(
                    any("Could not determine method to count non-padding tokens" in log for log in cm.output)
                )
            total_tokens = input_ids.numel()
            self.assertEqual(trainer.state.num_input_tokens_seen, total_tokens)

            # Test case 4: "all"
            args.include_num_input_tokens_seen = "all"
            trainer = Trainer(
                model=model,
                args=args,
                train_dataset=dataset_with_mask,
                data_collator=data_collator,
                processing_class=tokenizer,
            )
            trainer.train()
            self.assertEqual(trainer.state.num_input_tokens_seen, total_tokens)

            # Test case 5: True (backward compatibility)
            args.include_num_input_tokens_seen = True
            trainer = Trainer(
                model=model,
                args=args,
                train_dataset=dataset_with_mask,
                data_collator=data_collator,
                processing_class=tokenizer,
            )
            trainer.train()
            self.assertEqual(trainer.state.num_input_tokens_seen, total_tokens)


@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
    def setUp(self):
        super().setUp()
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_trainer_works_with_dict(self):
        # Edge case because Apex with mode O2 will change our models to return dicts. This test checks it doesn't break
        # anything.
        train_dataset = RegressionDataset()
        eval_dataset = RegressionDataset()
        model = RegressionDictModel()
        args = TrainingArguments(self.get_auto_remove_tmp_dir(), report_to="none")
        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
        trainer.train()
        _ = trainer.evaluate()
        _ = trainer.predict(eval_dataset)

    def test_init_with_offloaded_model(self):
        # Test that Trainer can be initialized with a model that has been offloaded to CPU
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)

        # Simulate a model with some parts offloaded to CPU
        device_map = {"a": "cpu"}
        if torch.cuda.is_available():
            device_map["b"] = "cuda:0"
        else:
            device_map["b"] = "cpu"
        model.hf_device_map = device_map

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(output_dir=tmp_dir, report_to="none")
            # This should not raise an error.
            _ = Trainer(model, args=args, train_dataset=RegressionDataset())

    def test_training_arguments_are_left_untouched(self):
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir)
        trainer.train()
        args = TrainingArguments(tmp_dir, report_to=[])
        dict1, dict2 = args.to_dict(), trainer.args.to_dict()
        for key in dict1:
            self.assertEqual(dict1[key], dict2[key])

    def test_number_of_steps_in_training(self):
        # Regular training has n_epochs * len(train_dl) steps
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(learning_rate=0.1, output_dir=tmp_dir)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, self.n_epochs * 64 / self.batch_size)

        # Check passing num_train_epochs works (and a float version too):
        trainer = get_regression_trainer(learning_rate=0.1, num_train_epochs=1.5, output_dir=tmp_dir)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, int(1.5 * 64 / self.batch_size))

        # If we pass a max_steps, num_train_epochs is ignored
        trainer = get_regression_trainer(learning_rate=0.1, max_steps=10, output_dir=tmp_dir)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, 10)

    @pytest.mark.torch_compile_test
    def test_torch_compile_loss_func_compatibility(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)

        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            per_device_train_batch_size=2,
            torch_compile=True,
            max_steps=1,  # compile happens on the first step
            report_to="none",
        )
        trainer = Trainer(model=tiny_llama, args=args, train_dataset=train_dataset)  # noqa
        trainer.train()

    @require_peft
    @require_bitsandbytes
    @pytest.mark.torch_compile_test
    def test_bnb_compile(self):
        from peft import LoraConfig, get_peft_model

        # Simply tests if initializing a Trainer with a PEFT + compiled model works out of the box
        # QLoRA + torch compile is not really supported yet, but we should at least support the model
        # loading and let torch throw the
        tiny_model = AutoModelForCausalLM.from_pretrained(
            "hf-internal-testing/tiny-random-LlamaForCausalLM",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        peft_config = LoraConfig(
            r=8,
            lora_alpha=32,
            target_modules=["q_proj", "k_proj", "v_proj"],
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )
        tiny_model = get_peft_model(tiny_model, peft_config)

        tiny_model = torch.compile(tiny_model)

        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
        )
        with self.assertRaises(ValueError):
            _ = Trainer(tiny_model, args, train_dataset=train_dataset)  # noqa

    @require_peft
    def test_multiple_peft_adapters(self):
        from peft import LoraConfig, get_peft_model

        # Tests if resuming from checkpoint works if the model has multiple adapters

        MODEL_ID = "hf-internal-testing/tiny-random-LlamaForCausalLM"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        tiny_model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

        peft_config = LoraConfig(
            r=4,
            lora_alpha=16,
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )
        tiny_model = get_peft_model(tiny_model, peft_config, "adapter1")
        tiny_model.add_adapter("adapter2", peft_config)

        train_dataset = get_dataset(PATH_SAMPLE_TEXT, tokenizer, 100)

        tokenizer.pad_token = tokenizer.eos_token

        tmp_dir = self.get_auto_remove_tmp_dir()
        args = TrainingArguments(
            tmp_dir,
            per_device_train_batch_size=1,
            learning_rate=1e-9,
            save_steps=5,
            logging_steps=5,
            max_steps=10,
            use_cpu=True,
        )
        trainer = Trainer(tiny_model, args, processing_class=tokenizer, train_dataset=train_dataset)

        trainer.train()
        parameters = dict(tiny_model.named_parameters())
        state = dataclasses.asdict(trainer.state)

        # Reinitialize trainer
        trainer = Trainer(tiny_model, args, processing_class=tokenizer, train_dataset=train_dataset)

        checkpoint = os.path.join(tmp_dir, "checkpoint-5")

        trainer.train(resume_from_checkpoint=checkpoint)
        parameters1 = dict(tiny_model.named_parameters())
        state1 = dataclasses.asdict(trainer.state)
        self.assertEqual(parameters, parameters1)
        self.check_trainer_state_are_the_same(state, state1)

    @require_bitsandbytes
    def test_rmsprop_bnb(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            optim="rmsprop_bnb",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

    @require_bitsandbytes
    def test_ademamix_bnb(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            optim="ademamix",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

    @require_bitsandbytes
    def test_ademamix_bnb_8bit(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            optim="ademamix_8bit",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

    @require_bitsandbytes
    def test_rmsprop_bnb_8bit(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            optim="rmsprop_bnb_8bit",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

    @require_bitsandbytes
    def test_rmsprop_bnb_32bit(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)
        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            optim="rmsprop_bnb_32bit",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

    def test_neftune(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            neftune_noise_alpha=0.4,
            report_to="none",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        trainer.model = trainer._activate_neftune(trainer.model)

        dummy_input = torch.LongTensor([[1, 0, 1]]).to(torch_device)

        emb1 = trainer.model.get_input_embeddings()(dummy_input)
        emb2 = trainer.model.get_input_embeddings()(dummy_input)

        self.assertFalse(torch.allclose(emb1, emb2), "Neftune noise is not applied!")

        # redefine the model
        tiny_gpt2 = GPT2LMHeadModel(config)
        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            neftune_noise_alpha=0.4,
            report_to="none",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)

        # Check that it trains without errors
        trainer.train()

        # Make sure forward pass works fine
        _ = trainer.model(dummy_input)
        self.assertTrue(len(trainer.model.get_input_embeddings()._forward_hooks) == 0)

        trainer.model.eval()

        # Check that we get identical embeddings just in case
        emb1 = trainer.model.get_input_embeddings()(dummy_input)
        emb2 = trainer.model.get_input_embeddings()(dummy_input)
        torch.testing.assert_close(emb1, emb2)

    def test_logging_inf_nan_filter(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e9,
            logging_steps=5,
            logging_nan_inf_filter=False,
            report_to="none",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
        trainer.train()
        log_history_no_filter = trainer.state.log_history

        # Trainer with inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e9,
            logging_steps=5,
            logging_nan_inf_filter=True,
            report_to="none",
        )
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
        trainer.train()
        log_history_filter = trainer.state.log_history

        def is_any_loss_nan_or_inf(log_history):
            losses = [l["loss"] for l in log_history[:-1]]
            return any(math.isnan(x) for x in losses) or any(math.isinf(x) for x in losses)

        self.assertTrue(is_any_loss_nan_or_inf(log_history_no_filter))
        self.assertFalse(is_any_loss_nan_or_inf(log_history_filter))

    def test_train_and_eval_dataloaders(self):
        if torch_device == "cuda":
            n_gpu = max(1, backend_device_count(torch_device))
        else:
            # DP is deprecated by PyTorch, accelerators like XPU doesn't support DP
            n_gpu = 1

        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16, output_dir=tmp_dir)
        self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16 * n_gpu)
        trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16, output_dir=tmp_dir)
        self.assertEqual(trainer.get_eval_dataloader().total_batch_size, 16 * n_gpu)

        # Check drop_last works
        trainer = get_regression_trainer(
            train_len=66,
            eval_len=74,
            learning_rate=0.1,
            per_device_train_batch_size=16,
            per_device_eval_batch_size=32,
            output_dir=tmp_dir,
        )
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu) + 1)
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu) + 1)

        trainer = get_regression_trainer(
            train_len=66,
            eval_len=74,
            learning_rate=0.1,
            per_device_train_batch_size=16,
            per_device_eval_batch_size=32,
            dataloader_drop_last=True,
            output_dir=tmp_dir,
        )
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu))
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu))

        # Check passing a new dataset for evaluation works
        new_eval_dataset = RegressionDataset(length=128)
        self.assertEqual(len(trainer.get_eval_dataloader(new_eval_dataset)), 128 // (32 * n_gpu))

    # tests that we do not require dataloader to have a .dataset attribute
    def test_dataloader_without_dataset(self):
        train_dataset = RegressionDataset(length=128)
        trainer = CustomDataloaderTrainer(
            model=RegressionModel(),
            train_dataset=train_dataset,
            eval_dataset=train_dataset,
            args=TrainingArguments(output_dir=self.get_auto_remove_tmp_dir(), report_to="none"),
        )

        trainer.train()
        trainer.evaluate()

    def test_get_eval_dataloader_without_persistent_workers(self):
        train_dataset = RegressionDataset()
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        args = TrainingArguments(self.get_auto_remove_tmp_dir(), report_to="none", dataloader_persistent_workers=False)

        # Single evaluation dataset
        eval_dataset = RegressionDataset()
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
        # Mocking the prepare method to avoid the dataloader changing with each call to get_eval_dataloader
        trainer.accelerator.prepare = lambda x: x

        default_dataloader = trainer.get_eval_dataloader()
        dataloader_with_dataset = trainer.get_eval_dataloader(eval_dataset)

        self.assertEqual(default_dataloader.dataset, eval_dataset)
        self.assertEqual(dataloader_with_dataset.dataset, eval_dataset)
        self.assertNotEqual(default_dataloader, dataloader_with_dataset)

        # Multiple evaluation datasets
        first_dataset = RegressionDataset()
        second_dataset = RegressionDataset()
        trainer = Trainer(
            tiny_gpt2,
            args,
            train_dataset=train_dataset,
            eval_dataset={"first": first_dataset, "second": second_dataset},
        )
        # Mocking the prepare method to avoid the dataloader changing with each call to get_eval_dataloader
        trainer.accelerator.prepare = lambda x: x

        first_dataloader = trainer.get_eval_dataloader("first")
        first_dataloader_repeated = trainer.get_eval_dataloader("first")
        second_dataloader = trainer.get_eval_dataloader("second")
        second_dataloader_repeated = trainer.get_eval_dataloader("second")

        self.assertEqual(first_dataset, first_dataloader.dataset)
        self.assertEqual(first_dataloader.dataset, first_dataloader_repeated.dataset)
        self.assertEqual(second_dataset, second_dataloader.dataset)
        self.assertEqual(second_dataloader.dataset, second_dataloader_repeated.dataset)
        self.assertNotEqual(first_dataloader, first_dataloader_repeated)
        self.assertNotEqual(second_dataloader, second_dataloader_repeated)

    def test_get_eval_dataloader_with_persistent_workers(self):
        train_dataset = RegressionDataset()
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            report_to="none",
            dataloader_persistent_workers=True,
            dataloader_num_workers=2,
        )

        # Single evaluation dataset
        eval_dataset = RegressionDataset()
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
        # Mocking the prepare method to avoid the dataloader changing with each call to get_eval_dataloader
        trainer.accelerator.prepare = lambda x: x

        default_dataloader = trainer.get_eval_dataloader()
        dataloader_with_dataset = trainer.get_eval_dataloader(eval_dataset)

        self.assertEqual(default_dataloader.dataset, eval_dataset)
        self.assertEqual(dataloader_with_dataset.dataset, eval_dataset)
        self.assertEqual(default_dataloader, dataloader_with_dataset)

        # Multiple evaluation datasets
        first_dataset = RegressionDataset()
        second_dataset = RegressionDataset()
        trainer = Trainer(
            tiny_gpt2,
            args,
            train_dataset=train_dataset,
            eval_dataset={"first": first_dataset, "second": second_dataset},
        )
        # Mocking the prepare method to avoid the dataloader changing with each call to get_eval_dataloader
        trainer.accelerator.prepare = lambda x: x

        first_dataloader = trainer.get_eval_dataloader("first")
        first_dataloader_repeated = trainer.get_eval_dataloader("first")
        second_dataloader = trainer.get_eval_dataloader("second")
        second_dataloader_repeated = trainer.get_eval_dataloader("second")

        self.assertEqual(first_dataset, first_dataloader.dataset)
        self.assertEqual(first_dataloader.dataset, first_dataloader_repeated.dataset)
        self.assertEqual(second_dataset, second_dataloader.dataset)
        self.assertEqual(second_dataloader.dataset, second_dataloader_repeated.dataset)
        self.assertEqual(first_dataloader, first_dataloader_repeated)
        self.assertEqual(second_dataloader, second_dataloader_repeated)

    @require_liger_kernel
    def test_use_liger_kernel_patching(self):
        # Ensure any monkey patching is cleaned up for subsequent tests
        with patch("transformers.models.llama.modeling_llama"):
            from liger_kernel.transformers import liger_rotary_pos_emb

            from transformers.models.llama import modeling_llama

            config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
            tiny_llama = LlamaForCausalLM(config)

            # Spot check that modeling code and model instance variables are not yet patched
            self.assertNotEqual(modeling_llama.apply_rotary_pos_emb, liger_rotary_pos_emb)
            self.assertFalse("LigerRMSNorm" in tiny_llama.model.norm.__repr__())

            args = TrainingArguments(
                self.get_auto_remove_tmp_dir(),
                use_liger_kernel=True,
            )
            Trainer(tiny_llama, args)

            # Spot check that modeling code and model instance variables are patched
            self.assertEqual(modeling_llama.apply_rotary_pos_emb, liger_rotary_pos_emb)
            self.assertTrue("LigerRMSNorm" in tiny_llama.model.norm.__repr__())

    @require_liger_kernel
    def test_use_liger_kernel_custom_config_patching(self):
        # Ensure any monkey patching is cleaned up for subsequent tests
        with patch("transformers.models.llama.modeling_llama"):
            from liger_kernel.transformers import LigerRMSNorm

            config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
            tiny_llama = LlamaForCausalLM(config)

            args = TrainingArguments(
                self.get_auto_remove_tmp_dir(),
                use_liger_kernel=True,
                liger_kernel_config={"rms_norm": False},  # Don't apply Liger's RMSNorm
            )
            Trainer(tiny_llama, args)

            # Check that the RMSNorm kernel is not applied as specified in the config
            self.assertFalse(isinstance(tiny_llama.model.norm, LigerRMSNorm))

    @require_liger_kernel
    @require_torch_accelerator
    @require_torch_non_multi_accelerator  # Don't work with DP
    def test_use_liger_kernel_trainer(self):
        # Ensure any monkey patching is cleaned up for subsequent tests
        with patch("transformers.models.llama.modeling_llama"):
            # Check that trainer still works with liger kernel applied
            config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
            tiny_llama = LlamaForCausalLM(config)

            x = torch.randint(0, 100, (128,))
            train_dataset = RepeatDataset(x)

            args = TrainingArguments(
                self.get_auto_remove_tmp_dir(),
                learning_rate=1e-2,
                logging_steps=5,
                max_steps=20,
                use_liger_kernel=True,
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

    @require_liger_kernel
    @require_torch_accelerator
    @require_torch_non_multi_accelerator  # don't work with DP
    def test_use_liger_kernel_custom_config_trainer(self):
        # Ensure any monkey patching is cleaned up for subsequent tests
        with patch("transformers.models.llama.modeling_llama"):
            # Check that trainer still works with liger kernel applied when using a custom config
            config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
            tiny_llama = LlamaForCausalLM(config)

            x = torch.randint(0, 100, (128,))
            train_dataset = RepeatDataset(x)

            args = TrainingArguments(
                self.get_auto_remove_tmp_dir(),
                learning_rate=1e-2,
                logging_steps=5,
                max_steps=20,
                use_liger_kernel=True,
                liger_kernel_config={"rms_norm": False, "cross_entropy": True, "fused_linear_cross_entropy": False},
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

    @require_lomo
    @require_torch_accelerator
    def test_lomo(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)

        previous_params = {n: p.clone() for n, p in tiny_llama.named_parameters()}

        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(), learning_rate=1e-2, logging_steps=5, optim="lomo", max_steps=20
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

        for name, param in tiny_llama.named_parameters():
            self.assertFalse(torch.allclose(param, previous_params[name].to(param.device), rtol=1e-12, atol=1e-12))

    @require_lomo
    @require_torch_accelerator
    def test_adalomo(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="adalomo",
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_grokadamw
    @require_torch_accelerator
    def test_grokadamw(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=2e-5,
            logging_steps=5,
            optim="grokadamw",
            max_steps=20,
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_schedulefree
    @require_torch_accelerator
    def test_schedulefree_adam(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        with tempfile.TemporaryDirectory() as tmpdir:
            # Trainer without inf/nan filter
            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                optim="schedule_free_adamw",
                lr_scheduler_type="constant",
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

    @require_schedulefree
    @require_torch_accelerator
    def test_schedulefree_radam(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        with tempfile.TemporaryDirectory() as tmpdir:
            # Trainer without inf/nan filter
            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                lr_scheduler_type="constant",
                optim="schedule_free_radam",
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    def test_galore_matched_modules(self):
        regex_patterns = [r".*.attn.*", r".*.mlp.*"]

        module_names = [
            "model.transformer.h.0.ln_1",
            "model.transformer.h.0.attn.q_proj",
            "model.lm_head",
            "model.transformer.h.0.mlp.up_proj",
        ]
        expected_values = [False, True, False, True]

        for expected_value, module_name in zip(expected_values, module_names):
            is_module_matched, is_regex = check_target_module_exists(regex_patterns, module_name, return_is_regex=True)
            self.assertTrue(is_module_matched == expected_value)
            if is_module_matched:
                self.assertTrue(is_regex)

        exact_patterns = ["q_proj", "up_proj"]

        module_names = [
            "model.transformer.h.0.ln_1",
            "model.transformer.h.0.attn.q_proj",
            "model.lm_head",
            "model.transformer.h.0.mlp.up_proj",
        ]
        expected_values = [False, True, False, True]

        for expected_value, module_name in zip(expected_values, module_names):
            is_module_matched, is_regex = check_target_module_exists(exact_patterns, module_name, return_is_regex=True)
            self.assertTrue(is_module_matched == expected_value)
            if is_module_matched:
                self.assertFalse(is_regex)

        simple_regex = r".*.attn.*"

        module_names = [
            "model.transformer.h.0.ln_1",
            "model.transformer.h.0.attn.q_proj",
            "model.lm_head",
            "model.transformer.h.0.mlp.up_proj",
        ]
        expected_values = [False, True, False, False]

        for expected_value, module_name in zip(expected_values, module_names):
            is_module_matched, is_regex = check_target_module_exists(simple_regex, module_name, return_is_regex=True)
            self.assertTrue(is_module_matched == expected_value)
            if is_module_matched:
                self.assertTrue(is_regex)

        simple_regex = "model.transformer.h.0.attn.q_proj"

        module_names = [
            "model.transformer.h.0.ln_1",
            "model.transformer.h.0.attn.q_proj",
            "model.lm_head",
            "model.transformer.h.0.mlp.up_proj",
        ]
        expected_values = [False, True, False, False]

        for expected_value, module_name in zip(expected_values, module_names):
            is_module_matched, is_regex = check_target_module_exists(simple_regex, module_name, return_is_regex=True)
            self.assertTrue(is_module_matched == expected_value)
            if is_module_matched:
                self.assertFalse(is_regex)

        target_modules = ["attn", "mlp"]

        module_names = [
            "model.transformer.h.0.ln_1",
            "model.transformer.h.0.attn.q_proj",
            "model.lm_head",
            "model.transformer.h.0.mlp.up_proj",
        ]
        expected_values = [False, True, False, True]

        for expected_value, module_name in zip(expected_values, module_names):
            is_module_matched, is_regex = check_target_module_exists(target_modules, module_name, return_is_regex=True)
            self.assertTrue(is_module_matched == expected_value)
            if is_module_matched:
                self.assertFalse(is_regex)

    @require_galore_torch
    @require_torch_accelerator
    def test_galore(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="galore_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_extra_args(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="galore_adamw",
            optim_args="rank=64, update_proj_gap=100, scale=0.10",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_layerwise(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="galore_adamw_layerwise",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_layerwise_with_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="galore_adamw_layerwise",
            lr_scheduler_type="cosine",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_adamw_8bit(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="galore_adamw_8bit",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_adafactor(self):
        # These are the intervals of the peak memory usage of training such a tiny model
        # if the peak memory goes outside that range, then we know there might be a bug somewhere
        upper_bound_pm = 700
        lower_bound_pm = 650

        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        with tempfile.TemporaryDirectory() as tmpdir, TorchTracemalloc() as tracemalloc:
            # Trainer without inf/nan filter
            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                optim="galore_adafactor",
                optim_target_modules=[r".*attn.*", r".*mlp.*"],
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

        galore_peak_memory = tracemalloc.peaked + bytes2megabytes(tracemalloc.begin)

        self.assertTrue(galore_peak_memory < upper_bound_pm)
        self.assertTrue(lower_bound_pm < galore_peak_memory)

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_adafactor_attention_only(self):
        # These are the intervals of the peak memory usage of training such a tiny model
        # if the peak memory goes outside that range, then we know there might be a bug somewhere
        upper_bound_pm = 700
        lower_bound_pm = 650

        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        with tempfile.TemporaryDirectory() as tmpdir, TorchTracemalloc() as tracemalloc:
            # Trainer without inf/nan filter
            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                optim="galore_adafactor",
                optim_target_modules=["q_proj", "k_proj", "v_proj"],
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

        galore_peak_memory = tracemalloc.peaked + bytes2megabytes(tracemalloc.begin)
        self.assertTrue(galore_peak_memory < upper_bound_pm)
        self.assertTrue(lower_bound_pm < galore_peak_memory)

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_adafactor_all_linear(self):
        # These are the intervals of the peak memory usage of training such a tiny model
        # if the peak memory goes outside that range, then we know there might be a bug somewhere
        upper_bound_pm = 700
        lower_bound_pm = 650

        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        with tempfile.TemporaryDirectory() as tmpdir, TorchTracemalloc() as tracemalloc:
            # Trainer without inf/nan filter
            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                optim="galore_adafactor",
                optim_target_modules="all-linear",
            )
            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

            # Check this works
            _ = trainer.train()

        galore_peak_memory = tracemalloc.peaked + bytes2megabytes(tracemalloc.begin)
        self.assertTrue(galore_peak_memory < upper_bound_pm)
        self.assertTrue(lower_bound_pm < galore_peak_memory)

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_lr_display_without_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 1e-9
        num_steps = 10

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=learning_rate,
            logging_steps=5,
            optim="galore_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
        trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

        # reflects displayed lr in trainer
        self.assertEqual(trainer.get_learning_rates(), [learning_rate, learning_rate])

    @require_galore_torch
    @require_torch_accelerator
    def test_galore_lr_display_with_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 2e-4
        num_train_epochs = 2
        num_warmup_steps = 5

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            num_train_epochs=num_train_epochs,
            learning_rate=learning_rate,
            warmup_steps=num_warmup_steps,
            lr_scheduler_type="cosine",
            logging_steps=1,
            optim="galore_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # creating log history of trainer, results don't matter
        trainer.train()
        logs = trainer.state.log_history[1:-1]

        # reach given learning rate peak and end with 0 lr
        self.assertTrue(logs[num_warmup_steps - 1]["learning_rate"] == learning_rate)
        # self.assertTrue(logs[-1]["learning_rate"] == 0)
        self.assertTrue(np.allclose(logs[-1]["learning_rate"], 0, atol=5e-6))

        # increasing and decreasing pattern of lrs
        increasing_lrs = [
            logs[i]["learning_rate"] < logs[i + 1]["learning_rate"]
            for i in range(len(logs))
            if i < num_warmup_steps - 1
        ]
        decreasing_lrs = [
            logs[i]["learning_rate"] > logs[i + 1]["learning_rate"]
            for i in range(len(logs) - 1)
            if i >= num_warmup_steps - 1
        ]

        self.assertTrue(all(increasing_lrs))
        self.assertTrue(all(decreasing_lrs))

        # warm up steps << total steps
        self.assertTrue(len(decreasing_lrs) > len(increasing_lrs))

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="apollo_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo_extra_args(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="apollo_adamw",
            optim_args="proj=random,scale_type=tensor,rank=1,update_proj_gap=100,scale=128.0",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo_layerwise(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="apollo_adamw_layerwise",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo_layerwise_with_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="apollo_adamw_layerwise",
            lr_scheduler_type="cosine",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo_lr_display_without_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 1e-9
        num_steps = 10

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=learning_rate,
            logging_steps=5,
            optim="apollo_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
        trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

        # reflects displayed lr in trainer
        self.assertEqual(trainer.get_learning_rates(), [learning_rate, learning_rate])

    @require_apollo_torch
    @require_torch_accelerator
    def test_apollo_lr_display_with_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 2e-4
        num_train_epochs = 10
        num_warmup_steps = 5

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            num_train_epochs=num_train_epochs,
            learning_rate=learning_rate,
            warmup_steps=num_warmup_steps,
            lr_scheduler_type="cosine",
            logging_steps=1,
            optim="apollo_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # creating log history of trainer, results don't matter
        trainer.train()
        logs = trainer.state.log_history[1:][:-1]

        # reach given learning rate peak and end with 0 lr
        self.assertTrue(logs[num_warmup_steps - 1]["learning_rate"] == learning_rate)
        self.assertTrue(np.allclose(logs[-1]["learning_rate"], 0, atol=5e-6))

        # increasing and decreasing pattern of lrs
        increasing_lrs = [
            logs[i]["learning_rate"] < logs[i + 1]["learning_rate"]
            for i in range(len(logs))
            if i < num_warmup_steps - 1
        ]
        decreasing_lrs = [
            logs[i]["learning_rate"] > logs[i + 1]["learning_rate"]
            for i in range(len(logs) - 1)
            if i >= num_warmup_steps - 1
        ]

        self.assertTrue(all(increasing_lrs))
        self.assertTrue(all(decreasing_lrs))

        # warm up steps << total steps
        self.assertTrue(len(decreasing_lrs) > len(increasing_lrs))

    @require_torch_optimi
    @require_torch_accelerator
    def test_stable_adamw(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="stable_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
        _ = trainer.train()

    @require_torch_optimi
    @require_torch_accelerator
    def test_stable_adamw_extra_args(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=1e-9,
            logging_steps=5,
            optim="stable_adamw",
            optim_args="decouple_lr=True,max_lr=1e-3,kahan_sum=True",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # Check this works
        _ = trainer.train()

    @require_torch_optimi
    @require_torch_accelerator
    def test_stable_adamw_lr_display_without_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 1e-9
        num_steps = 10

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=learning_rate,
            logging_steps=5,
            optim="stable_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
        trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

        # reflects displayed lr in trainer
        self.assertEqual(trainer.get_learning_rates(), [learning_rate, learning_rate])

    @require_torch_optimi
    @require_torch_accelerator
    def test_stable_adamw_trainer_adamw_args(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 1e-9
        num_steps = 10

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            learning_rate=learning_rate,
            logging_steps=5,
            weight_decay=0.001,
            adam_beta1=0.89,
            adam_beta2=0.98,
            adam_epsilon=1e-8,
            optim="stable_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
        trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)

        # check StableAdamW optimizer is created with the correct parameters
        self.assertEqual(trainer.optimizer.defaults["beta1"], args.adam_beta1)
        self.assertEqual(trainer.optimizer.defaults["beta2"], args.adam_beta2)
        self.assertEqual(trainer.optimizer.defaults["eps"], args.adam_epsilon)
        self.assertEqual(trainer.optimizer.defaults["weight_decay"], args.weight_decay)

    @require_torch_optimi
    @require_torch_accelerator
    def test_stable_adamw_lr_display_with_scheduler(self):
        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
        tiny_llama = LlamaForCausalLM(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        learning_rate = 2e-4
        num_train_epochs = 10
        num_warmup_steps = 5

        # Trainer without inf/nan filter
        args = TrainingArguments(
            self.get_auto_remove_tmp_dir(),
            num_train_epochs=num_train_epochs,
            learning_rate=learning_rate,
            warmup_steps=num_warmup_steps,
            lr_scheduler_type="cosine",
            logging_steps=1,
            optim="stable_adamw",
            optim_target_modules=[r".*attn.*", r".*mlp.*"],
        )
        trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)

        # creating log history of trainer, results don't matter
        trainer.train()
        logs = trainer.state.log_history[1:][:-1]

        # reach given learning rate peak and end with 0 lr
        self.assertTrue(logs[num_warmup_steps - 1]["learning_rate"] == learning_rate)
        self.assertTrue(np.allclose(logs[-1]["learning_rate"], 0, atol=5e-6))

        # increasing and decreasing pattern of lrs
        increasing_lrs = [
            logs[i]["learning_rate"] < logs[i + 1]["learning_rate"]
            for i in range(len(logs))
            if i < num_warmup_steps - 1
        ]
        decreasing_lrs = [
            logs[i]["learning_rate"] > logs[i + 1]["learning_rate"]
            for i in range(len(logs) - 1)
            if i >= num_warmup_steps - 1
        ]

        self.assertTrue(all(increasing_lrs))
        self.assertTrue(all(decreasing_lrs))

        # warm up steps << total steps
        self.assertTrue(len(decreasing_lrs) > len(increasing_lrs))

    def test_evaluate(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy(), output_dir=tmp_dir)
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

            # With a number of elements not a round multiple of the batch size
            trainer = get_regression_trainer(
                a=1.5, b=2.5, eval_len=66, compute_metrics=AlmostAccuracy(), output_dir=tmp_dir
            )
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

            # With logits preprocess
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                compute_metrics=AlmostAccuracy(),
                preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
                output_dir=tmp_dir,
            )
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred + 1, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

    def test_evaluate_with_batch_eval_metrics(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                a=1.5, b=2.5, compute_metrics=AlmostAccuracyBatched(), batch_eval_metrics=True, output_dir=tmp_dir
            )
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

            # With a number of elements not a round multiple of the batch size
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                eval_len=66,
                compute_metrics=AlmostAccuracyBatched(),
                batch_eval_metrics=True,
                output_dir=tmp_dir,
            )
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

            # With logits preprocess
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                compute_metrics=AlmostAccuracyBatched(),
                batch_eval_metrics=True,
                preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
                output_dir=tmp_dir,
            )
            results = trainer.evaluate()

            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred + 1, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

    def test_predict(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(a=1.5, b=2.5, output_dir=tmp_dir)
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = trainer.eval_dataset.x
            self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

            # With a number of elements not a round multiple of the batch size
            trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, output_dir=tmp_dir)
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = trainer.eval_dataset.x
            self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

            # With more than one output of the model
            trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True, output_dir=tmp_dir)
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = trainer.eval_dataset.x
            self.assertEqual(len(preds), 2)
            self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
            self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))

            # With more than one output/label of the model
            trainer = get_regression_trainer(
                a=1.5, b=2.5, double_output=True, label_names=["labels", "labels_2"], output_dir=tmp_dir
            )
            outputs = trainer.predict(trainer.eval_dataset)
            preds = outputs.predictions
            labels = outputs.label_ids
            x = trainer.eval_dataset.x
            self.assertEqual(len(preds), 2)
            self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
            self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
            self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
            self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))

    def test_train_and_predict_loss_parity(self):
        """
        Tests that the loss computed during a training_step is the same as the one computed during prediction_step.
        for the same inputs
        """
        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
        # Create a dummy batch of inputs
        inputs = {}
        inputs["input_ids"] = []
        for row_ind in range(4):
            seq_len = torch.randint(32, 64, (1,)).item()
            x = torch.randint(1, 100, (seq_len,))
            inputs["input_ids"].append(x)
        inputs["input_ids"] = torch.nn.utils.rnn.pad_sequence(inputs["input_ids"], batch_first=True, padding_value=0)
        inputs["labels"] = inputs["input_ids"].clone()
        inputs["labels"][inputs["input_ids"] == 0] = -100
        num_items_in_batch = inputs["labels"].ne(-100).sum().item()

        def custom_loss_func(outputs, labels, num_items_in_batch=None):
            logits = outputs["logits"]
            loss_fct = torch.nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
            if num_items_in_batch is not None:
                return loss / num_items_in_batch  # multiply by number of items to get the sum
            return loss

        trainer = Trainer(model, train_dataset=None, compute_loss_func=custom_loss_func)

        # creating log history of trainer, results don't matter
        train_loss = trainer.training_step(model, inputs, num_items_in_batch)
        predict_loss = trainer.prediction_step(model, inputs, prediction_loss_only=True)[0]

        torch.testing.assert_close(train_loss, predict_loss, atol=1e-6, rtol=0)

    def test_predict_with_batch_eval_metrics(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                a=1.5, b=2.5, compute_metrics=AlmostAccuracyBatched(), batch_eval_metrics=True, output_dir=tmp_dir
            )
            results = trainer.predict(trainer.eval_dataset)
            preds = results.predictions
            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            gt = 1.5 * x + 2.5
            self.assertTrue(np.allclose(preds, gt))
            expected_acc = AlmostAccuracy()((preds, y))["accuracy"]
            self.assertAlmostEqual(results.metrics["test_accuracy"], expected_acc)

            # With a number of elements not a round multiple of the batch size
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                eval_len=66,
                compute_metrics=AlmostAccuracyBatched(),
                batch_eval_metrics=True,
                output_dir=tmp_dir,
            )
            results = trainer.predict(trainer.eval_dataset)
            preds = results.predictions
            x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
            self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
            expected_acc = AlmostAccuracy()((preds, y))["accuracy"]
            self.assertAlmostEqual(results.metrics["test_accuracy"], expected_acc)

            # With more than one output of the model
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                double_output=True,
                compute_metrics=AlmostAccuracyBatched(),
                batch_eval_metrics=True,
                output_dir=tmp_dir,
            )
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = trainer.eval_dataset.x
            self.assertEqual(len(preds), 2)
            self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
            self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))

            # With more than one output/label of the model
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                double_output=True,
                label_names=["labels", "labels_2"],
                compute_metrics=AlmostAccuracyBatched(),
                batch_eval_metrics=True,
                output_dir=tmp_dir,
            )
            outputs = trainer.predict(trainer.eval_dataset)
            preds = outputs.predictions
            labels = outputs.label_ids
            x = trainer.eval_dataset.x
            self.assertEqual(len(preds), 2)
            self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
            self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
            self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
            self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))

    def test_dynamic_shapes(self):
        eval_dataset = DynamicShapesDataset(batch_size=self.batch_size)
        model = RegressionModel(a=2, b=1)
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, report_to="none")
            trainer = Trainer(model, args, eval_dataset=eval_dataset)

            # Check evaluation can run to completion
            _ = trainer.evaluate()

            # Check predictions
            preds = trainer.predict(eval_dataset)
            for expected, seen in zip(eval_dataset.ys, preds.label_ids):
                self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
                self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

            for expected, seen in zip(eval_dataset.xs, preds.predictions):
                self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
                self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

        # Same tests with eval accumulation
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, eval_accumulation_steps=2, report_to="none")
            trainer = Trainer(model, args, eval_dataset=eval_dataset)

            # Check evaluation can run to completion
            _ = trainer.evaluate()

            # Check predictions
            preds = trainer.predict(eval_dataset)
            for expected, seen in zip(eval_dataset.ys, preds.label_ids):
                self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
                self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

            for expected, seen in zip(eval_dataset.xs, preds.predictions):
                self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
                self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

    def test_log_level(self):
        # testing only --log_level (--log_level_replica requires multiple gpus and DDP and is tested elsewhere)
        logger = logging.get_logger()
        log_info_string = "Running training"

        # test with the default log_level - should be the same as before and thus we test depending on is_info
        is_info = logging.get_verbosity() <= 20

        with tempfile.TemporaryDirectory() as tmp_dir:
            with CaptureLogger(logger) as cl:
                trainer = get_regression_trainer(output_dir=tmp_dir)
                trainer.train()
            if is_info:
                self.assertIn(log_info_string, cl.out)
            else:
                self.assertNotIn(log_info_string, cl.out)

            with LoggingLevel(logging.INFO):
                # test with low log_level - lower than info
                with CaptureLogger(logger) as cl:
                    trainer = get_regression_trainer(log_level="debug", output_dir=tmp_dir)
                    trainer.train()
                self.assertIn(log_info_string, cl.out)

            with LoggingLevel(logging.INFO):
                # test with high log_level - should be quiet
                with CaptureLogger(logger) as cl:
                    trainer = get_regression_trainer(log_level="error", output_dir=tmp_dir)
                    trainer.train()
                self.assertNotIn(log_info_string, cl.out)

    def test_save_checkpoints(self):
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir, save_steps=5)
        trainer.train()
        self.check_saved_checkpoints(tmp_dir, 5, int(self.n_epochs * 64 / self.batch_size))

        # With a regular model that is not a PreTrainedModel
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir, save_steps=5, pretrained=False)
        trainer.train()
        self.check_saved_checkpoints(tmp_dir, 5, int(self.n_epochs * 64 / self.batch_size), False)

    def test_save_collator_tokenizer_by_default(self):
        class FakeCollator:
            def __init__(self):
                self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
                self.tokenizer.add_tokens(["<NEW_TOKEN1>", "<NEW_TOKEN2>"])

            def __call__(self, features: list[Any], return_tensors="pt") -> dict[str, Any]:
                return default_data_collator(features, return_tensors)

        data_collator = FakeCollator()
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir, save_steps=5, data_collator=data_collator)
        trainer.train()
        loaded_tokenizer = AutoTokenizer.from_pretrained(os.path.join(tmp_dir, os.listdir(tmp_dir)[0]))
        assert len(loaded_tokenizer) == len(trainer.data_collator.tokenizer), "Failed to load updated tokenizer"

    def test_load_best_model_with_save(self):
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(
            output_dir=tmp_dir,
            save_steps=5,
            eval_strategy="steps",
            eval_steps=5,
            max_steps=9,
        )
        trainer.train()
        # Check that we have the last known step:
        assert os.path.exists(os.path.join(tmp_dir, f"checkpoint-{trainer.state.max_steps}")), (
            f"Could not find checkpoint-{trainer.state.max_steps}"
        )
        # And then check the last step
        assert os.path.exists(os.path.join(tmp_dir, "checkpoint-9")), "Could not find checkpoint-9"

        # Now test that using a limit works
        # Should result in:
        # - save at step 5 (but is deleted)
        # - save at step 10 (loaded in at the end when `load_best_model=True`)
        # - save at step 11
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(
            output_dir=tmp_dir,
            save_steps=5,
            eval_strategy="steps",
            eval_steps=5,
            load_best_model_at_end=True,
            save_total_limit=2,
            max_steps=11,
        )
        trainer.train()
        # Check that we have the last known step:
        assert os.path.exists(os.path.join(tmp_dir, "checkpoint-11")), "Could not find checkpoint-11"
        # And then check the last multiple
        assert os.path.exists(os.path.join(tmp_dir, "checkpoint-10")), "Could not find checkpoint-10"
        # Finally check that we don't have an old one
        assert not os.path.exists(os.path.join(tmp_dir, "checkpoint-5")), "Found checkpoint-5, limit not respected"

        # Finally check that the right model was loaded in, checkpoint-10
        # this goes by the last `eval` step check to do so, so it won't be
        # the last model *saved*
        model_state = trainer.model.state_dict()
        final_model_weights = safetensors.torch.load_file(os.path.join(tmp_dir, "checkpoint-10", "model.safetensors"))
        for k, v in model_state.items():
            assert torch.allclose(v, final_model_weights[k]), f"{k} is not the same"

    @require_torch_multi_accelerator
    def test_run_seq2seq_double_train_wrap_once(self):
        # test that we don't wrap the model more than once
        # since wrapping primarily happens on multi-gpu setup we want multiple gpus to test for
        # example DataParallel(DataParallel(model))

        trainer = get_regression_trainer(output_dir=self.get_auto_remove_tmp_dir())
        trainer.train()
        model_wrapped_before = trainer.model_wrapped
        trainer.train()
        model_wrapped_after = trainer.model_wrapped
        self.assertIs(model_wrapped_before, model_wrapped_after, "should be not wrapped twice")

    @require_torch_non_multi_accelerator
    def test_can_resume_training(self):
        # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
        # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
        # won't be the same since the training dataloader is shuffled).

        tmp_dir = self.get_auto_remove_tmp_dir()
        kwargs = {
            "output_dir": tmp_dir,
            "train_len": 128,
            "save_steps": 5,
            "learning_rate": 0.1,
            "logging_steps": 5,
        }
        trainer = get_regression_trainer(**kwargs)
        trainer.train()
        (a, b) = trainer.model.a.item(), trainer.model.b.item()
        state = dataclasses.asdict(trainer.state)

        checkpoint = os.path.join(tmp_dir, "checkpoint-5")

        # Reinitialize trainer
        trainer = get_regression_trainer(**kwargs)

        trainer.train(resume_from_checkpoint=checkpoint)
        (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
        state1 = dataclasses.asdict(trainer.state)
        self.assertEqual(a, a1)
        self.assertEqual(b, b1)
        self.check_trainer_state_are_the_same(state, state1)

        # Now check with a later checkpoint that it also works when we span over one epoch
        checkpoint = os.path.join(tmp_dir, "checkpoint-15")

        # Reinitialize trainer and load model
        trainer = get_regression_trainer(**kwargs)

        trainer.train(resume_from_checkpoint=checkpoint)
        (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
        state1 = dataclasses.asdict(trainer.state)
        self.assertEqual(a, a1)
        self.assertEqual(b, b1)
        self.check_trainer_state_are_the_same(state, state1)

        # With a regular model that is not a PreTrainedModel
        tmp_dir = self.get_auto_remove_tmp_dir()
        kwargs = {
            "output_dir": tmp_dir,
            "train_len": 128,
            "save_steps": 5,
            "learning_rate": 0.1,
            "pretrained": False,
        }

        trainer = get_regression_trainer(**kwargs)
        trainer.train()
        (a, b) = trainer.model.a.item(), trainer.model.b.item()
        state = dataclasses.asdict(trainer.state)

        checkpoint = os.path.join(tmp_dir, "checkpoint-5")

        # Reinitialize trainer and load model
        trainer = get_regression_trainer(**kwargs)

        trainer.train(resume_from_checkpoint=checkpoint)
        (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
        state1 = dataclasses.asdict(trainer.state)
        self.assertEqual(a, a1)
        self.assertEqual(b, b1)
        self.check_trainer_state_are_the_same(state, state1)

        # Now check with a later checkpoint that it also works when we span over one epoch
        checkpoint = os.path.join(tmp_dir, "checkpoint-15")

        # Reinitialize trainer and load model
        trainer = get_regression_trainer(**kwargs)

        trainer.train(resume_from_checkpoint=checkpoint)
        (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
        state1 = dataclasses.asdict(trainer.state)
        self.assertEqual(a, a1)
        self.assertEqual(b, b1)
        self.check_trainer_state_are_the_same(state, state1)

        # Now check failures

        # 1. fail to find a bogus checkpoint
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir)
        with self.assertRaises(Exception) as context:
            trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
        self.assertTrue("Can't find a valid checkpoint at" in str(context.exception))

        # 2. fail to find any checkpoint - due a fresh output_dir
        tmp_dir = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=tmp_dir)
        with self.assertRaises(Exception) as context:
            trainer.train(resume_from_checkpoint=True)
        self.assertTrue("No valid checkpoint found in output directory" in str(context.exception))

    # require_torch_non_multi_accelerator is necessary because this worker blocks runs when using multiple GPUs, making
    # the test slower.
    @require_torch_non_multi_accelerator
    @run_test_using_subprocess
    @run_first
    @slow
    def test_can_resume_training_lm(self):
        # Check if it works for a simple language modeling example
        training_steps = 10
        resume_from_step = 8
        with tempfile.TemporaryDirectory() as tmpdir:
            kwargs = {
                "output_dir": tmpdir,
                "fp16": True,
                "max_steps": training_steps,
                "per_device_train_batch_size": 1,
                "learning_rate": 1e-5,
                "lr_scheduler_type": "cosine",
                "save_strategy": "steps",
                "save_steps": 1,
                "logging_strategy": "steps",
                "logging_steps": 1,
                "report_to": "none",
            }

            trainer = get_language_model_trainer(**kwargs)
            trainer.train(resume_from_checkpoint=False)
            # Get the parameter length of the model
            model_params = torch.cat([p.cpu().flatten() for p in trainer.model.parameters()])
            model_param_len = len(model_params)
            # Sample uniform indexes and save the values of the parameters (considering an unrolled vector with
            # all of them)
            indices = torch.randint(0, model_param_len, (1000,))
            # Save the values of the parameters for later comparison
            model_params_sample = model_params[indices].detach().clone()
            state1 = dataclasses.asdict(trainer.state)
            # Delete the reference
            del model_params, trainer
            # Checks if all checkpoints are there, +1 is necessary because range is 1-indexed
            self.check_saved_checkpoints(tmpdir, freq=1, total=training_steps + 1, is_pretrained=True, use_scaler=True)

            # Checkpoint at intermediate step
            checkpoint = os.path.join(tmpdir, f"checkpoint-{resume_from_step + 1}")
            trainer = get_language_model_trainer(**kwargs)
            trainer.train(resume_from_checkpoint=checkpoint)
            model_params = torch.cat([p.cpu().flatten() for p in trainer.model.parameters()])

            # Check that the parameters are the same
            self.assertTrue(torch.allclose(model_params[indices], model_params_sample))
            state2 = dataclasses.asdict(trainer.state)
            self.check_trainer_state_are_the_same(state1, state2)
            del model_params, trainer

    @unittest.skip(
        reason="@muellerzr: Fix once Trainer can take an accelerate configuration. Need to set `seedable_sampler=True`."
    )
    def test_resume_training_with_randomness(self):
        # For more than 1 GPUs, since the randomness is introduced in the model and with DataParallel (which is used
        # in this test for more than 2 GPUs), the calls to the torch RNG will happen in a random order (sometimes
        # GPU 0 will call first and sometimes GPU 1).
        random_torch = not torch.cuda.is_available() or backend_device_count(torch_device) <= 1

        if torch.cuda.is_available():
            torch.backends.cudnn.deterministic = True
        train_dataset = RegressionDataset(length=128)
        eval_dataset = RegressionDataset()

        with self.subTest("Test every step"):
            config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
            model = RegressionRandomPreTrainedModel(config)

            tmp_dir = self.get_auto_remove_tmp_dir()
            args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()

            model = RegressionRandomPreTrainedModel(config)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
            trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, "checkpoint-15"))
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()

            self.assertAlmostEqual(a, a1, delta=1e-5)
            self.assertAlmostEqual(b, b1, delta=1e-5)

        with self.subTest("Test every epoch"):
            config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
            model = RegressionRandomPreTrainedModel(config)

            tmp_dir = self.get_auto_remove_tmp_dir()
            args = RegressionTrainingArguments(tmp_dir, save_strategy="epoch", learning_rate=0.1)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()

            model = RegressionRandomPreTrainedModel(config)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

            checkpoints = [d for d in os.listdir(tmp_dir) if d.startswith("checkpoint-")]
            # There should be one checkpoint per epoch.
            self.assertEqual(len(checkpoints), 3)
            checkpoint_dir = min(checkpoints, key=lambda x: int(x.replace("checkpoint-", "")))

            trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, checkpoint_dir))
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()

            self.assertAlmostEqual(a, a1, delta=1e-5)
            self.assertAlmostEqual(b, b1, delta=1e-5)

    @slow
    @require_non_hpu
    @require_accelerate
    @require_torch_non_multi_accelerator
    def test_auto_batch_size_finder(self):
        if torch.cuda.is_available():
            torch.backends.cudnn.deterministic = True

        SRC_DIR = os.path.abspath(
            os.path.join(os.path.dirname(__file__), "..", "..", "examples", "pytorch", "text-classification")
        )
        sys.path.append(SRC_DIR)
        import run_glue

        with tempfile.TemporaryDirectory() as tmpdir:
            testargs = f"""
                run_glue.py
                --model_name_or_path distilbert/distilbert-base-uncased
                --task_name mrpc
                --do_train
                --do_eval
                --max_seq_len 128
                --per_device_train_batch_size 4096
                --learning_rate 2e-5
                --num_train_epochs 1
                --output_dir {tmpdir}
                --report_to none
                --auto_find_batch_size 0
                """.split()
            with self.assertRaises(RuntimeError):
                with patch.object(sys, "argv", testargs):
                    run_glue.main()

        testargs[-1] = "1"
        with patch.object(sys, "argv", testargs):
            run_glue.main()

    @require_deepspeed
    def test_auto_batch_size_with_deepspeed(self):
        train_dataset = RegressionDataset(length=128)

        config = RegressionModelConfig(a=0, b=2)
        model = RegressionRandomPreTrainedModel(config)

        tmp_dir = self.get_auto_remove_tmp_dir()

        for stage in [1, 2]:
            deepspeed = {
                "zero_optimization": {
                    "stage": stage,
                },
                "train_batch_size": "auto",
                "train_micro_batch_size_per_gpu": "auto",
            }

        args = RegressionTrainingArguments(
            tmp_dir,
            do_train=True,
            max_steps=2,
            save_strategy="no",
            per_device_train_batch_size=16,
            auto_find_batch_size=True,
            deepspeed=deepspeed,
        )
        trainer = Trainer(model, args, train_dataset=train_dataset, callbacks=[MockCudaOOMCallback()])
        trainer.train()
        self.assertEqual(trainer._train_batch_size, 14)

    def test_auto_batch_size_with_resume_from_checkpoint(self):
        train_dataset = RegressionDataset(length=128)

        config = RegressionModelConfig(a=0, b=2)
        model = RegressionRandomPreTrainedModel(config)

        tmp_dir = self.get_auto_remove_tmp_dir()

        args = RegressionTrainingArguments(
            tmp_dir,
            do_train=True,
            max_steps=2,
            save_steps=1,
            per_device_train_batch_size=16,
            auto_find_batch_size=True,
        )
        trainer = Trainer(model, args, train_dataset=train_dataset, callbacks=[MockCudaOOMCallback()])
        trainer.train()
        previous_batch_size = trainer._train_batch_size
        # Depends on the number of gpus so it is easier to just check that the batch_size decreased as expected
        self.assertEqual(trainer._train_batch_size < 16, True)

        # We can then make a new Trainer
        trainer = Trainer(model, args, train_dataset=train_dataset)
        # Check we are at 16 to start
        self.assertEqual(trainer._train_batch_size, 16 * max(trainer.args.n_gpu, 1))
        trainer.train(resume_from_checkpoint=True)
        # We should be back to 14 again, picking up based upon the last ran Trainer
        self.assertEqual(trainer._train_batch_size, previous_batch_size)

    # regression for this issue: https://github.com/huggingface/transformers/issues/12970
    def test_training_with_resume_from_checkpoint_false(self):
        train_dataset = RegressionDataset(length=128)
        eval_dataset = RegressionDataset()

        config = RegressionModelConfig(a=0, b=2)
        model = RegressionRandomPreTrainedModel(config)

        tmp_dir = self.get_auto_remove_tmp_dir()
        args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

        trainer.train(resume_from_checkpoint=False)

    @require_torch_up_to_2_accelerators
    def test_resume_training_with_shard_checkpoint(self):
        # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
        # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
        # won't be the same since the training dataloader is shuffled).

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")
            self.convert_to_sharded_checkpoint(checkpoint)

            # Reinitialize trainer
            trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

    @require_torch_up_to_2_accelerators
    def test_resume_training_with_checkpoint(self):
        # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
        # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
        # won't be the same since the training dataloader is shuffled).

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")
            self.convert_to_sharded_checkpoint(checkpoint)

            # Reinitialize trainer
            trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

    @require_torch_up_to_2_accelerators
    def test_resume_training_with_gradient_accumulation(self):
        # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
        # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
        # won't be the same since the training dataloader is shuffled).

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")

            # Reinitialize trainer
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

    @require_torch_up_to_2_accelerators
    def test_resume_training_with_frozen_params(self):
        # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
        # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
        # won't be the same since the training dataloader is shuffled).

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.model.a.requires_grad_(False)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")

            # Reinitialize trainer
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.model.a.requires_grad_(False)

            trainer.train(resume_from_checkpoint=checkpoint)

            self.assertFalse(trainer.model.a.requires_grad)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

    def test_load_best_model_at_end(self):
        total = int(self.n_epochs * 64 / self.batch_size)
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                eval_strategy="steps",
                save_steps=5,
                load_best_model_at_end=True,
            )
            self.assertFalse(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss")

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                eval_strategy="steps",
                save_steps=5,
                load_best_model_at_end=True,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_accuracy", greater_is_better=True)

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_strategy="epoch",
                save_strategy="epoch",
                load_best_model_at_end=True,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 64 // self.batch_size, total)
            self.check_best_model_has_been_loaded(
                tmpdir, 64 // self.batch_size, total, trainer, "eval_accuracy", greater_is_better=True
            )

        # Test this works with a non PreTrainedModel
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                eval_strategy="steps",
                save_steps=5,
                load_best_model_at_end=True,
                pretrained=False,
            )
            self.assertFalse(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss", is_pretrained=False)

    def test_load_best_model_from_safetensors(self):
        total = int(self.n_epochs * 64 / self.batch_size)
        for pretrained in [False, True]:
            with tempfile.TemporaryDirectory() as tmpdir:
                trainer = get_regression_trainer(
                    a=1.5,
                    b=2.5,
                    output_dir=tmpdir,
                    learning_rate=0.1,
                    eval_steps=5,
                    eval_strategy="steps",
                    save_steps=5,
                    load_best_model_at_end=True,
                    pretrained=pretrained,
                )
                self.assertFalse(trainer.args.greater_is_better)
                trainer.train()
                self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=pretrained)
                self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss", is_pretrained=pretrained)

    @slow
    def test_trainer_eval_mrpc(self):
        MODEL_ID = "google-bert/bert-base-cased-finetuned-mrpc"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
        data_args = GlueDataTrainingArguments(
            task_name="mrpc", data_dir=f"{get_tests_dir()}/fixtures/tests_samples/MRPC", overwrite_cache=True
        )
        eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")

        with tempfile.TemporaryDirectory() as tmp_dir:
            training_args = TrainingArguments(output_dir=tmp_dir, use_cpu=True, report_to="none")
            trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
            result = trainer.evaluate()
            self.assertLess(result["eval_loss"], 0.2)

    @slow
    def test_trainer_eval_multiple(self):
        MODEL_ID = "openai-community/gpt2"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

        dataset = get_dataset(PATH_SAMPLE_TEXT, tokenizer, 100)
        with tempfile.TemporaryDirectory() as tmp_dir:
            training_args = TrainingArguments(
                output_dir=tmp_dir,
                use_cpu=True,
                per_device_eval_batch_size=1,
                report_to="none",
            )
            trainer = Trainer(
                model=model,
                args=training_args,
                eval_dataset={
                    "data1": dataset,
                    "data2": dataset,
                },
            )
            result = trainer.evaluate()
            self.assertIn("eval_data1_loss", result)
            self.assertIn("eval_data2_loss", result)

    @slow
    def test_trainer_eval_lm(self):
        MODEL_ID = "distilbert/distilroberta-base"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        dataset = get_dataset(PATH_SAMPLE_TEXT, tokenizer, 100)
        self.assertEqual(len(dataset), 31)

    def test_training_iterable_dataset(self):
        config = RegressionModelConfig()
        model = RegressionPreTrainedModel(config)
        # Adding one column not used by the model should have no impact
        train_dataset = SampleIterableDataset(label_names=["labels", "extra"])

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = RegressionTrainingArguments(output_dir=tmp_dir, max_steps=4)
            trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
            trainer.train()
            self.assertEqual(trainer.state.global_step, 4)

            loader = trainer.get_train_dataloader()
            self.assertIsInstance(loader, torch.utils.data.DataLoader)
            self.assertIsInstance(loader.sampler, torch.utils.data.dataloader._InfiniteConstantSampler)

    def test_evaluation_iterable_dataset(self):
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
        # Adding one column not used by the model should have no impact
        eval_dataset = SampleIterableDataset(label_names=["labels", "extra"])

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = RegressionTrainingArguments(output_dir=tmp_dir)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
            results = trainer.evaluate()
            x, y = trainer.eval_dataset.dataset.x, trainer.eval_dataset.dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

            # With a number of elements not a round multiple of the batch size
            eval_dataset = SampleIterableDataset(length=66)
            results = trainer.evaluate(eval_dataset)

            x, y = eval_dataset.dataset.x, eval_dataset.dataset.ys[0]
            pred = 1.5 * x + 2.5
            expected_loss = ((pred - y) ** 2).mean()
            self.assertAlmostEqual(results["eval_loss"], expected_loss)
            expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

    def test_predict_iterable_dataset(self):
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
        eval_dataset = SampleIterableDataset()

        with tempfile.TemporaryDirectory() as tmp_dir:
            args = RegressionTrainingArguments(output_dir=tmp_dir)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = eval_dataset.dataset.x
            self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

            # With a number of elements not a round multiple of the batch size
            # Adding one column not used by the model should have no impact
            test_dataset = SampleIterableDataset(length=66, label_names=["labels", "extra"])
            preds = trainer.predict(test_dataset).predictions
            x = test_dataset.dataset.x
            self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

    def test_num_train_epochs_in_training(self):
        # len(train_dl) < gradient_accumulation_steps shouldn't give ``ZeroDivisionError`` when ``max_steps`` is given.
        # It should give 1 update step for each epoch.
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                max_steps=3,
                train_len=64,
                per_device_train_batch_size=16,
                gradient_accumulation_steps=5,
                output_dir=tmp_dir,
            )
            train_output = trainer.train()
            self.assertEqual(train_output.global_step, 3)

            # Even ``max_steps`` is not specified, we still expect 1 update step for each epoch if
            # len(train_dl) < gradient_accumulation_steps.
            trainer = get_regression_trainer(
                train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5, output_dir=tmp_dir
            )
            train_output = trainer.train()
            self.assertEqual(train_output.global_step, int(self.n_epochs))

    @require_torch_multi_accelerator
    def test_num_batches_in_training_with_gradient_accumulation(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            for num_train_epochs in [1, 2]:
                for train_len in [123, 120]:
                    trainer = get_regression_trainer(
                        train_len=train_len,
                        per_device_train_batch_size=4,
                        gradient_accumulation_steps=5,
                        num_train_epochs=num_train_epochs,
                        output_dir=tmp_dir,
                    )

                    total_batch_samples = []

                    def wrap_get_batch_samples(fn):
                        def wrapped_fn(epoch_iterator, num_batches, device):
                            self.assertGreater(num_batches, 0)
                            batch_samples, num_items_in_batch = fn(epoch_iterator, num_batches, device)
                            self.assertEqual(len(batch_samples), num_batches)
                            total_batch_samples.append(num_batches)
                            return batch_samples, num_items_in_batch

                        return wrapped_fn

                    trainer.get_batch_samples = wrap_get_batch_samples(trainer.get_batch_samples)

                    trainer.train()

                    self.assertEqual(len(trainer.get_train_dataloader()) * num_train_epochs, sum(total_batch_samples))

    def test_early_stopping_callback(self):
        # early stopping stops training before num_training_epochs
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                num_train_epochs=20,
                gradient_accumulation_steps=1,
                per_device_train_batch_size=16,
                load_best_model_at_end=True,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
            train_output = trainer.train()
            self.assertLess(train_output.global_step, 20 * 64 / 16)

        # Invalid inputs to trainer with early stopping callback result in assertion error
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                num_train_epochs=20,
                gradient_accumulation_steps=1,
                per_device_train_batch_size=16,
                eval_strategy=IntervalStrategy.EPOCH,
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1))
            self.assertEqual(trainer.state.global_step, 0)
            try:
                trainer.train()
            except AssertionError:
                self.assertEqual(trainer.state.global_step, 0)

        # even if load_best_model_at_end is False, `best_model_checkpoint` should be set
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                num_train_epochs=20,
                gradient_accumulation_steps=1,
                per_device_train_batch_size=16,
                load_best_model_at_end=False,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
            train_output = trainer.train()
            self.assertIsNotNone(trainer.state.best_model_checkpoint)

    def test_flos_extraction(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(learning_rate=0.1, output_dir=tmp_dir)

            def assert_flos_extraction(trainer, wrapped_model_to_check):
                self.assertEqual(trainer.model, trainer.accelerator.unwrap_model(wrapped_model_to_check))
                self.assertGreaterEqual(
                    getattr(trainer.accelerator.unwrap_model(wrapped_model_to_check).config, "total_flos", 0), 0
                )

            # with plain model
            assert_flos_extraction(trainer, trainer.model)

            # with enforced DataParallel
            assert_flos_extraction(trainer, nn.DataParallel(trainer.model))

            trainer.train()
            self.assertTrue(isinstance(trainer.state.total_flos, float))

    def check_checkpoint_deletion(self, trainer, output_dir, expected):
        # Make fake checkpoints
        for n in [5, 10, 15, 20, 25]:
            os.makedirs(os.path.join(output_dir, f"{PREFIX_CHECKPOINT_DIR}-{n}"), exist_ok=True)
        trainer._rotate_checkpoints(output_dir=output_dir)
        glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{PREFIX_CHECKPOINT_DIR}-*")]
        values = [int(re.match(f".*{PREFIX_CHECKPOINT_DIR}-([0-9]+)", d).groups()[0]) for d in glob_checkpoints]
        self.assertSetEqual(set(values), set(expected))

    def test_checkpoint_rotation(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            # Without best model at end
            trainer = get_regression_trainer(output_dir=tmp_dir, save_total_limit=2)
            self.check_checkpoint_deletion(trainer, tmp_dir, [20, 25])

            # With best model at end
            trainer = get_regression_trainer(
                output_dir=tmp_dir, eval_strategy="steps", load_best_model_at_end=True, save_total_limit=2
            )
            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
            self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])

            # Edge case: we don't always honor save_total_limit=1 if load_best_model_at_end=True to be able to resume
            # from checkpoint
            trainer = get_regression_trainer(
                output_dir=tmp_dir, eval_strategy="steps", load_best_model_at_end=True, save_total_limit=1
            )
            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-25")
            self.check_checkpoint_deletion(trainer, tmp_dir, [25])

            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
            self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])

    def test_compare_trainer_and_checkpoint_args_logging(self):
        logger = logging.get_logger()

        with tempfile.TemporaryDirectory() as tmpdir, CaptureLogger(logger) as cl:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                eval_steps=5,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.train()

            checkpoint = os.path.join(tmpdir, "checkpoint-5")
            checkpoint_trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=256,
                eval_steps=10,
                gradient_accumulation_steps=4,
                per_device_train_batch_size=8,
                save_steps=10,
                learning_rate=0.1,
            )
            checkpoint_trainer.train(resume_from_checkpoint=checkpoint)

        self.assertIn("save_steps: 10 (from args) != 5 (from trainer_state.json)", cl.out)

        self.assertIn(
            "per_device_train_batch_size: 8 (from args) != 4 (from trainer_state.json)",
            cl.out,
        )
        self.assertIn(
            "eval_steps: 10 (from args) != 5 (from trainer_state.json)",
            cl.out,
        )

    def check_mem_metrics(self, trainer, check_func):
        metrics = trainer.train().metrics
        check_func("init_mem_cpu_alloc_delta", metrics)
        check_func("train_mem_cpu_alloc_delta", metrics)
        if backend_device_count(torch_device) > 0:
            check_func("init_mem_gpu_alloc_delta", metrics)
            check_func("train_mem_gpu_alloc_delta", metrics)

        metrics = trainer.evaluate()
        check_func("eval_mem_cpu_alloc_delta", metrics)
        if backend_device_count(torch_device) > 0:
            check_func("eval_mem_gpu_alloc_delta", metrics)

        metrics = trainer.predict(RegressionDataset()).metrics
        check_func("test_mem_cpu_alloc_delta", metrics)
        if backend_device_count(torch_device) > 0:
            check_func("test_mem_gpu_alloc_delta", metrics)

    def test_mem_metrics(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            # with mem metrics enabled
            trainer = get_regression_trainer(skip_memory_metrics=False, output_dir=tmp_dir)
            self.check_mem_metrics(trainer, self.assertIn)

            # with mem metrics disabled
            trainer = get_regression_trainer(skip_memory_metrics=True, output_dir=tmp_dir)
            self.check_mem_metrics(trainer, self.assertNotIn)

    @require_torch_fp16
    @require_torch_accelerator
    def test_fp16_full_eval(self):
        # this is a sensitive test so let's keep debugging printouts in place for quick diagnosis.
        # it's using pretty large safety margins, but small enough to detect broken functionality.
        debug = 0
        n_gpus = backend_device_count(torch_device)

        with tempfile.TemporaryDirectory() as tmp_dir:
            bs = 8
            eval_len = 16 * n_gpus
            # make the params somewhat big so that there will be enough RAM consumed to be able to
            # measure things. We should get about 64KB for a+b in fp32
            a = torch.ones(1000, bs) + 0.001
            b = torch.ones(1000, bs) - 0.001

            # 1. with fp16_full_eval disabled
            trainer = get_regression_trainer(
                a=a, b=b, eval_len=eval_len, skip_memory_metrics=False, output_dir=tmp_dir
            )
            metrics = trainer.evaluate()
            del trainer
            gc.collect()

            fp32_init = metrics["init_mem_gpu_alloc_delta"]
            fp32_eval = metrics["eval_mem_gpu_alloc_delta"]

            if debug:
                print(f"fp32_init {fp32_init}")
                print(f"fp32_eval {fp32_eval}")

            # here we expect the model to be preloaded in trainer.__init__ and consume around 64K gpu ram.
            # perfect world: fp32_init == 64<<10
            self.assertGreater(fp32_init, 59_000)
            # after eval should be no extra memory allocated - with a small margin (other than the peak
            # memory consumption for the forward calculation that gets recovered)
            # perfect world: fp32_eval == close to zero
            self.assertLess(fp32_eval, 5_000)

            # 2. with fp16_full_eval enabled
            trainer = get_regression_trainer(
                a=a, b=b, eval_len=eval_len, fp16_full_eval=True, skip_memory_metrics=False, output_dir=tmp_dir
            )
            metrics = trainer.evaluate()
            fp16_init = metrics["init_mem_gpu_alloc_delta"]
            fp16_eval = metrics["eval_mem_gpu_alloc_delta"]

            if debug:
                print(f"fp16_init {fp16_init}")
                print(f"fp16_eval {fp16_eval}")

            # here we expect the model to not be preloaded in trainer.__init__, so with a small margin it should be close to 0
            # perfect world: fp16_init == close to zero
            self.assertLess(fp16_init, 5_000)
            # here we put the model on device in eval and only `half()` of it, i.e. about 32K,(again we ignore the peak margin which gets returned back)
            # perfect world: fp32_init == 32<<10
            self.assertGreater(fp16_eval, 27_000)

            # 3. relative comparison fp32 vs full fp16
            # should be about half of fp16_init
            # perfect world: fp32_init/2 == fp16_eval
            self.assertAlmostEqual(fp16_eval, fp32_init / 2, delta=5_000)

    @require_torch_accelerator
    @pytest.mark.torch_compile_test
    def test_torch_compile_train(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(output_dir=tmp_dir)
            metrics = trainer.train()
            original_train_loss = metrics.training_loss

            trainer = get_regression_trainer(torch_compile=True, output_dir=tmp_dir)
            metrics = trainer.train()
            self.assertAlmostEqual(metrics.training_loss, original_train_loss)

    @require_torch_accelerator
    @pytest.mark.torch_compile_test
    def test_torch_compile_eval(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(output_dir=tmp_dir)
            metrics = trainer.evaluate()
            original_eval_loss = metrics["eval_loss"]

            trainer = get_regression_trainer(torch_compile=True, output_dir=tmp_dir)
            metrics = trainer.evaluate()

            self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss, delta=1e-6)

    @require_torch_accelerator
    @require_torch_bf16
    def test_bf16_full_eval(self):
        # note: most of the logic is the same as test_fp16_full_eval

        # this is a sensitive test so let's keep debugging printouts in place for quick diagnosis.
        # it's using pretty large safety margins, but small enough to detect broken functionality.
        debug = 0
        n_gpus = backend_device_count(torch_device)

        bs = 8
        eval_len = 16 * n_gpus
        # make the params somewhat big so that there will be enough RAM consumed to be able to
        # measure things. We should get about 64KB for a+b in fp32
        a = torch.ones(1000, bs) + 0.001
        b = torch.ones(1000, bs) - 0.001

        with tempfile.TemporaryDirectory() as tmp_dir:
            # 1. with bf16_full_eval disabled
            trainer = get_regression_trainer(
                a=a, b=b, eval_len=eval_len, skip_memory_metrics=False, output_dir=tmp_dir
            )
            metrics = trainer.evaluate()
            del trainer
            gc.collect()

            fp32_init = metrics["init_mem_gpu_alloc_delta"]
            fp32_eval = metrics["eval_mem_gpu_alloc_delta"]

            if debug:
                print(f"fp32_init {fp32_init}")
                print(f"fp32_eval {fp32_eval}")

            # here we expect the model to be preloaded in trainer.__init__ and consume around 64K gpu ram.
            # perfect world: fp32_init == 64<<10
            self.assertGreater(fp32_init, 59_000)
            # after eval should be no extra memory allocated - with a small margin (other than the peak
            # memory consumption for the forward calculation that gets recovered)
            # perfect world: fp32_eval == close to zero
            self.assertLess(fp32_eval, 5_000)

            # 2. with bf16_full_eval enabled
            trainer = get_regression_trainer(
                a=a, b=b, eval_len=eval_len, bf16_full_eval=True, skip_memory_metrics=False, output_dir=tmp_dir
            )
            metrics = trainer.evaluate()
            bf16_init = metrics["init_mem_gpu_alloc_delta"]
            bf16_eval = metrics["eval_mem_gpu_alloc_delta"]

            if debug:
                print(f"bf16_init {bf16_init}")
                print(f"bf16_eval {bf16_eval}")

            # here we expect the model to not be preloaded in trainer.__init__, so with a small margin it should be close to 0
            # perfect world: bf16_init == close to zero
            self.assertLess(bf16_init, 5_000)
            # here we put the model on device in eval and only `half()` of it, i.e. about 32K,(again we ignore the peak margin which gets returned back)
            # perfect world: fp32_init == 32<<10
            self.assertGreater(bf16_eval, 27_000)

            # 3. relative comparison fp32 vs full bf16
            # should be about half of bf16_init
            # perfect world: fp32_init/2 == bf16_eval
            self.assertAlmostEqual(bf16_eval, fp32_init / 2, delta=5_000)

    def test_no_wd_param_group(self):
        model = nn.Sequential(TstLayer(128), nn.ModuleList([TstLayer(128), TstLayer(128)]))
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = Trainer(model=model, args=TrainingArguments(output_dir=tmp_dir, report_to="none"))
            trainer.create_optimizer_and_scheduler(10)
            wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight']  # fmt: skip
            wd_params = [p for n, p in model.named_parameters() if n in wd_names]
            no_wd_params = [p for n, p in model.named_parameters() if n not in wd_names]
            self.assertListEqual(trainer.optimizer.param_groups[0]["params"], wd_params)
            self.assertListEqual(trainer.optimizer.param_groups[1]["params"], no_wd_params)

    @slow
    @run_first
    @require_non_hpu
    @require_torch_multi_accelerator
    def test_end_to_end_example(self):
        # Tests that `translation.py` will run without issues
        script_path = os.path.abspath(
            os.path.join(
                os.path.dirname(__file__), "..", "..", "examples", "pytorch", "translation", "run_translation.py"
            )
        )

        with tempfile.TemporaryDirectory() as tmpdir:
            command = [
                "accelerate",
                "launch",
                script_path,
                "--model_name_or_path",
                "google-t5/t5-small",
                "--per_device_train_batch_size",
                "1",
                "--output_dir",
                tmpdir,
                "--do_train",
                "--max_train_samples",
                "64",
                "--num_train_epochs",
                "1",
                "--dataset_name",
                "wmt16",
                "--dataset_config",
                "ro-en",
                "--source_lang",
                "en",
                "--target_lang",
                "ro",
                "--do_predict",
                "--max_predict_samples",
                "64",
                "--predict_with_generate",
                "--ddp_timeout",
                "60",
                "--report_to",
                "none",
            ]
            execute_subprocess_async(command)
            # successful return here == success - any errors would have caused an error or a timeout in the sub-call

    def test_accelerator_config_empty(self):
        # Checks that a config can be made with the defaults if not passed
        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            model = RegressionPreTrainedModel(config)
            eval_dataset = SampleIterableDataset()

            # Leaves one option as something *not* basic
            args = RegressionTrainingArguments(output_dir=tmp_dir)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.split_batches, False)
            self.assertEqual(trainer.accelerator.dispatch_batches, None)
            self.assertEqual(trainer.accelerator.even_batches, True)
            self.assertEqual(trainer.accelerator.use_seedable_sampler, True)
            # gradient accumulation kwargs configures gradient_state
            self.assertNotIn("sync_each_batch", trainer.accelerator.gradient_state.plugin_kwargs)

    def test_accelerator_config_from_dict(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively
        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            model = RegressionPreTrainedModel(config)
            eval_dataset = SampleIterableDataset()

            accelerator_config: dict[str, Any] = {
                "split_batches": True,
                "dispatch_batches": True,
                "even_batches": False,
                "use_seedable_sampler": True,
            }
            accelerator_config["gradient_accumulation_kwargs"] = {"sync_each_batch": True}

            # Leaves all options as something *not* basic
            args = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config=accelerator_config)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.split_batches, True)
            self.assertEqual(trainer.accelerator.dispatch_batches, True)
            self.assertEqual(trainer.accelerator.even_batches, False)
            self.assertEqual(trainer.accelerator.use_seedable_sampler, True)

    def test_accelerator_config_from_yaml(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively
        with tempfile.TemporaryDirectory() as tmp_dir:
            path_file = Path(tmp_dir) / "accelerator_config.json"
            with open(path_file, "w") as f:
                accelerator_config = {
                    "split_batches": True,
                    "dispatch_batches": True,
                    "even_batches": False,
                    "use_seedable_sampler": False,
                }
                json.dump(accelerator_config, f)
            config = RegressionModelConfig(a=1.5, b=2.5)
            model = RegressionPreTrainedModel(config)
            eval_dataset = SampleIterableDataset()

            # Leaves all options as something *not* basic
            args = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config=path_file)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.split_batches, True)
            self.assertEqual(trainer.accelerator.dispatch_batches, True)
            self.assertEqual(trainer.accelerator.even_batches, False)
            self.assertEqual(trainer.accelerator.use_seedable_sampler, False)

    def test_accelerator_config_from_dataclass(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively

        accelerator_config = AcceleratorConfig(
            split_batches=True,
            dispatch_batches=True,
            even_batches=False,
            use_seedable_sampler=False,
        )
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
        eval_dataset = SampleIterableDataset()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config=accelerator_config)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.split_batches, True)
            self.assertEqual(trainer.accelerator.dispatch_batches, True)
            self.assertEqual(trainer.accelerator.even_batches, False)
            self.assertEqual(trainer.accelerator.use_seedable_sampler, False)

    def test_accelerate_config_from_dataclass_grad_accum(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively

        grad_acc_kwargs = {
            "num_steps": 10,
            "adjust_scheduler": False,
            "sync_with_dataloader": False,
            "sync_each_batch": True,
        }
        accelerator_config = AcceleratorConfig(
            split_batches=True,
            dispatch_batches=True,
            even_batches=False,
            use_seedable_sampler=False,
            gradient_accumulation_kwargs=grad_acc_kwargs,
        )
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
        eval_dataset = SampleIterableDataset()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config=accelerator_config)
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.args.gradient_accumulation_steps, 10)

    def test_accelerator_config_from_partial(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively
        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            model = RegressionPreTrainedModel(config)
            eval_dataset = SampleIterableDataset()

            # Leaves one option as something *not* basic
            args = RegressionTrainingArguments(
                output_dir=tmp_dir,
                accelerator_config={
                    "split_batches": True,
                },
            )
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.split_batches, True)
            self.assertEqual(trainer.accelerator.dispatch_batches, None)
            self.assertEqual(trainer.accelerator.even_batches, True)
            self.assertEqual(trainer.accelerator.use_seedable_sampler, True)

    def test_accelerator_custom_state(self):
        AcceleratorState._reset_state(reset_partial_state=True)
        with tempfile.TemporaryDirectory() as tmp_dir:
            with self.assertRaises(ValueError) as cm:
                _ = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config={"use_configured_state": True})
                self.assertIn("Please define this beforehand", str(cm.warnings[0].message))
            _ = Accelerator()
            _ = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config={"use_configured_state": True})
        AcceleratorState._reset_state(reset_partial_state=True)

    def test_accelerator_config_from_dict_grad_accum_num_steps(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            model = RegressionPreTrainedModel(config)
            eval_dataset = SampleIterableDataset()

            # case - TrainingArguments.gradient_accumulation_steps == 1
            #      - gradient_accumulation_kwargs['num_steps] == 1
            # results in grad accum set to 1
            args = RegressionTrainingArguments(
                output_dir=tmp_dir,
                gradient_accumulation_steps=1,
                accelerator_config={
                    "gradient_accumulation_kwargs": {
                        "num_steps": 1,
                    }
                },
            )
            trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertEqual(trainer.accelerator.gradient_state.plugin_kwargs["num_steps"], 1)

            # case - TrainingArguments.gradient_accumulation_steps > 1
            #      - gradient_accumulation_kwargs['num_steps] specified
            # results in exception raised
            args = RegressionTrainingArguments(
                output_dir=tmp_dir,
                gradient_accumulation_steps=2,
                accelerator_config={
                    "gradient_accumulation_kwargs": {
                        "num_steps": 10,
                    }
                },
            )
            with self.assertRaises(Exception) as context:
                trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
            self.assertTrue("The `AcceleratorConfig`'s `num_steps` is set but" in str(context.exception))

    def test_accelerator_config_not_instantiated(self):
        # Checks that accelerator kwargs can be passed through
        # and the accelerator is initialized respectively
        with tempfile.TemporaryDirectory() as tmp_dir:
            with self.assertRaises(NotImplementedError) as context:
                _ = RegressionTrainingArguments(
                    output_dir=tmp_dir,
                    accelerator_config=AcceleratorConfig,
                )
            self.assertTrue("Tried passing in a callable to `accelerator_config`" in str(context.exception))

        # Now test with a custom subclass
        @dataclasses.dataclass
        class CustomAcceleratorConfig(AcceleratorConfig):
            pass

        @dataclasses.dataclass
        class CustomTrainingArguments(TrainingArguments):
            accelerator_config: dict = dataclasses.field(
                default=CustomAcceleratorConfig,
            )

        with tempfile.TemporaryDirectory() as tmp_dir:
            with self.assertRaises(NotImplementedError) as context:
                _ = CustomTrainingArguments(
                    output_dir=tmp_dir,
                )
            self.assertTrue("Tried passing in a callable to `accelerator_config`" in str(context.exception))

    def test_dtype_to_json(self):
        @dataclasses.dataclass
        class TorchDtypeTrainingArguments(TrainingArguments):
            dtype: torch.dtype = dataclasses.field(
                default=torch.float32,
            )

        for dtype in [
            "float32",
            "float64",
            "complex64",
            "complex128",
            "float16",
            "bfloat16",
            "uint8",
            "int8",
            "int16",
            "int32",
            "int64",
            "bool",
        ]:
            torch_dtype = getattr(torch, dtype)
            with tempfile.TemporaryDirectory() as tmp_dir:
                args = TorchDtypeTrainingArguments(output_dir=tmp_dir, dtype=torch_dtype)

                args_dict = args.to_dict()
                self.assertIn("dtype", args_dict)
                self.assertEqual(args_dict["dtype"], dtype)

    def test_eval_use_gather_object(self):
        train_dataset = RegressionDataset()
        eval_dataset = RegressionDataset()
        model = RegressionDictModel()
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(tmp_dir, report_to="none", eval_use_gather_object=True)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
            trainer.train()
            _ = trainer.evaluate()
            _ = trainer.predict(eval_dataset)

    def test_trainer_saves_tokenizer(self):
        MODEL_ID = "google-bert/bert-base-uncased"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)

        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            trainer = Trainer(
                model=RegressionPreTrainedModel(config),
                args=TrainingArguments(output_dir=tmp_dir),
                processing_class=tokenizer,
            )
            trainer.save_model()

            reloaded_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)

        # For tokenizers, there isn't a direct to_dict method and the properties stored in the configs e.g.
        # saved tokens change overtime, so we check that two tokenizers are equal by comparing their encoded outputs
        test_sentence = "This is a test sentence"
        self.assertListEqual(
            tokenizer(test_sentence, padding="max_length").input_ids,
            reloaded_tokenizer(test_sentence, padding="max_length").input_ids,
        )

    @require_vision
    def test_trainer_saves_image_processor(self):
        MODEL_ID = "openai/clip-vit-base-patch32"
        image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)

        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            trainer = Trainer(
                model=RegressionPreTrainedModel(config),
                args=TrainingArguments(output_dir=tmp_dir, report_to="none"),
                processing_class=image_processor,
            )
            trainer.save_model()
            reloaded_image_processor = AutoImageProcessor.from_pretrained(tmp_dir)

        self.assertDictEqual(image_processor.to_dict(), reloaded_image_processor.to_dict())

    def test_trainer_saves_feature_extractor(self):
        MODEL_ID = "facebook/wav2vec2-base-960h"
        feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)

        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            trainer = Trainer(
                model=RegressionPreTrainedModel(config),
                args=TrainingArguments(output_dir=tmp_dir, report_to="none"),
                processing_class=feature_extractor,
            )
            trainer.save_model()

            reloaded_feature_extractor = AutoFeatureExtractor.from_pretrained(tmp_dir)

        self.assertDictEqual(feature_extractor.to_dict(), reloaded_feature_extractor.to_dict())

    @require_vision
    def test_trainer_saves_processor(self):
        MODEL_ID = "openai/clip-vit-base-patch32"
        image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
        processor = AutoProcessor.from_pretrained(MODEL_ID)

        with tempfile.TemporaryDirectory() as tmp_dir:
            config = RegressionModelConfig(a=1.5, b=2.5)
            trainer = Trainer(
                model=RegressionPreTrainedModel(config),
                args=TrainingArguments(output_dir=tmp_dir, report_to="none"),
                processing_class=processor,
            )
            trainer.save_model()

            reloaded_processor = AutoProcessor.from_pretrained(tmp_dir)
            reloaded_image_processor = AutoImageProcessor.from_pretrained(tmp_dir)
            reloaded_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)

        self.assertDictEqual(reloaded_processor.to_dict(), processor.to_dict())

        image_processor_dict = image_processor.to_dict()
        reloaded_image_processor_dict = reloaded_image_processor.to_dict()
        self.assertDictEqual(image_processor_dict, reloaded_image_processor_dict)

        # For tokenizers, there isn't a direct to_dict method and the properties stored in the configs e.g.
        # saved tokens change overtime, so we check that two tokenizers are equal by comparing their encoded outputs
        test_sentence = "This is a test sentence"
        self.assertListEqual(
            tokenizer(test_sentence, padding="max_length").input_ids,
            reloaded_tokenizer(test_sentence, padding="max_length").input_ids,
        )

    def test_save_best_checkpoint(self):
        freq = int(64 / self.batch_size)
        total = int(self.n_epochs * 64 / self.batch_size)

        # Case 1: args.metric_for_best_model == "accuracy".
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_strategy="epoch",
                save_strategy="best",
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.metric_for_best_model == "accuracy")

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=[
                    {"eval_loss": 0.03, "eval_accuracy": 0.60, "epoch": 1.0},
                    {"eval_loss": 0.02, "eval_accuracy": 0.65, "epoch": 2.0},
                    {"eval_loss": 0.01, "eval_accuracy": 0.64, "epoch": 3.0},
                ],
            ):
                trainer.train()

                self.assertEqual(len(os.listdir(tmpdir)), 2)
                self.check_saved_checkpoints(
                    output_dir=tmpdir,
                    freq=freq,
                    total=total,
                )

        # Case 2: args.metric_for_best_model == "loss".
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_strategy="epoch",
                save_strategy="best",
                metric_for_best_model="loss",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.metric_for_best_model == "loss")

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=[
                    {"eval_loss": 0.03, "eval_accuracy": 0.60, "epoch": 1.0},
                    {"eval_loss": 0.02, "eval_accuracy": 0.65, "epoch": 2.0},
                    {"eval_loss": 0.03, "eval_accuracy": 0.66, "epoch": 3.0},
                ],
            ):
                trainer.train()

                self.assertEqual(len(os.listdir(tmpdir)), 2)
                self.check_saved_checkpoints(
                    output_dir=tmpdir,
                    freq=freq,
                    total=total,
                )

    def test_metric_for_best_model_behavior(self):
        # Case 1: Metric name not provided when `save_strategy == "best"`.
        # Should raise ValueError.
        with tempfile.TemporaryDirectory() as tmpdir:
            with self.assertRaises(ValueError) as context:
                trainer = get_regression_trainer(
                    a=1.5,
                    b=2.5,
                    output_dir=tmpdir,
                    learning_rate=0.1,
                    eval_strategy="epoch",
                    save_strategy="best",
                    compute_metrics=AlmostAccuracy(),
                )
            self.assertIn("`args.metric_for_best_model` must be provided", str(context.exception))

        # Case 2: Metric name not provided when `load_best_model_at_end == True`.
        # `metric_for_best_model` should be set to `"loss"` by default.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_strategy="steps",
                save_strategy="steps",
                load_best_model_at_end=True,
            )
            self.assertTrue(trainer.args.metric_for_best_model == "loss")

    def test_best_model_checkpoint_behavior(self):
        # Case 1. Never evaluated, save_total_limit > 1 and save_steps == 1.
        # Both best_metric and best_model_checkpoint should be None.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="steps",
                save_strategy="steps",
                save_steps=1,
                metric_for_best_model="accuracy",
                greater_is_better=True,
            )
            trainer.train()

            assert trainer.state.best_metric is None
            assert trainer.state.best_model_checkpoint is None
            assert len(os.listdir(tmpdir)) == trainer.state.global_step

        # Case 2. Never evaluated and save_total_limit == 1.
        # Both best_metric and best_model_checkpoint should be None.
        # Only the last checkpoint should remain.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="steps",
                save_strategy="steps",
                save_steps=1,
                metric_for_best_model="accuracy",
                greater_is_better=True,
                save_total_limit=1,
            )
            trainer.train()

            num_steps = trainer.state.global_step

            assert trainer.state.best_metric is None
            assert trainer.state.best_model_checkpoint is None
            assert len(os.listdir(tmpdir)) == 1

            ckpt = os.path.join(tmpdir, f"{PREFIX_CHECKPOINT_DIR}-{num_steps}")
            assert os.path.isdir(ckpt)
            assert os.listdir(tmpdir)[0] == f"{PREFIX_CHECKPOINT_DIR}-{num_steps}"

        # Case 3. eval_strategy == save_strategy.
        # best_model_checkpoint should be at epoch 1.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="epoch",
                save_strategy="epoch",
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
                greater_is_better=True,
                load_best_model_at_end=False,
            )

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=evaluate_side_effect_factory(
                    [
                        {"eval_accuracy": 0.59},
                        {"eval_accuracy": 0.57},
                        {"eval_accuracy": 0.55},
                    ]
                ),
            ):
                trainer.train()

            steps_per_epoch = get_steps_per_epoch(trainer)

            assert trainer.state.best_metric == 0.59
            assert trainer.state.best_global_step == steps_per_epoch

            best_ckpt = os.path.join(tmpdir, f"{PREFIX_CHECKPOINT_DIR}-{trainer.state.best_global_step}")
            assert trainer.state.best_model_checkpoint == best_ckpt

            assert len(os.listdir(tmpdir)) == trainer.state.num_train_epochs

        # Case 4. eval_strategy != save_strategy.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="epoch",
                save_strategy="steps",
                save_steps=1,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
                greater_is_better=True,
                load_best_model_at_end=False,
            )

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=evaluate_side_effect_factory(
                    [
                        {"eval_accuracy": 0.59},
                        {"eval_accuracy": 0.57},
                        {"eval_accuracy": 0.55},
                    ]
                ),
            ):
                trainer.train()

            steps_per_epoch = get_steps_per_epoch(trainer)

            assert trainer.state.best_metric == 0.59
            assert trainer.state.best_global_step == steps_per_epoch

            best_ckpt = os.path.join(tmpdir, f"{PREFIX_CHECKPOINT_DIR}-{trainer.state.best_global_step}")
            assert trainer.state.best_model_checkpoint == best_ckpt

            assert len(os.listdir(tmpdir)) == trainer.state.global_step

        # Case 5. Multiple checkpoints, save_total_limit == 1.
        # Best metric is found at step 1 and that checkpoint should be saved.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="steps",
                eval_steps=1,
                save_strategy="steps",
                save_steps=1,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
                greater_is_better=True,
                save_total_limit=1,
            )

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=evaluate_side_effect_factory(
                    [
                        {"eval_accuracy": 0.90},
                        {"eval_accuracy": 0.80},
                        {"eval_accuracy": 0.70},
                    ]
                ),
            ):
                trainer.train()

            assert trainer.state.best_metric == 0.90
            assert trainer.state.best_global_step == 1

            best_ckpt = os.path.join(tmpdir, f"{PREFIX_CHECKPOINT_DIR}-{trainer.state.best_global_step}")
            assert trainer.state.best_model_checkpoint == best_ckpt

            assert len(os.listdir(tmpdir)) == 1

        # Case 6. Saving happens more often and eval/save mismatch.
        # `best_model_checkpoint` should be None due to a step mismatch.
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                eval_strategy="steps",
                eval_steps=3,
                save_strategy="steps",
                save_steps=2,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
                greater_is_better=True,
            )

            with patch.object(
                trainer,
                "_evaluate",
                side_effect=evaluate_side_effect_factory(
                    [
                        {"eval_accuracy": 0.90},
                        {"eval_accuracy": 0.80},
                        {"eval_accuracy": 0.70},
                    ]
                ),
            ):
                trainer.train()

            assert trainer.state.best_metric == 0.90
            assert trainer.state.best_global_step == 3

            assert trainer.state.best_model_checkpoint is None

            assert len(os.listdir(tmpdir)) == trainer.state.global_step // 2

    def test_special_token_alignment(self):
        """
        Tests that special token changes in the tokenizer result in model configs updates when using the trainer, to
        ensure special tokens are aligned across configs
        """

        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")

        # add new special tokens to tokenizer, so we can test that trainer aligns the model configs with the tokenizer
        tokenizer.eos_token = "<|im_end|>"
        tokenizer.pad_token = "<|im_end|>"
        tokenizer.bos_token = "<|im_start|>"
        tokenizer.add_special_tokens({"additional_special_tokens": ["<|im_end|>", "<|im_start|>"]})

        # the model needs to have its embedding layer resized accordingly
        model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)

        # create a random dataset from the **new** vocab size
        x = torch.randint(0, len(tokenizer), (64,))
        dataset = RepeatDataset(x, length=2)

        with tempfile.TemporaryDirectory() as tmpdir:
            training_args = TrainingArguments(
                output_dir=tmpdir, report_to="none", max_steps=1, per_device_train_batch_size=1
            )
            trainer = Trainer(
                model=model,
                args=training_args,
                processing_class=tokenizer,
                train_dataset=dataset,
            )

            # We haven't started training -> not yet aligned
            self.assertNotEqual(trainer.model.config.eos_token_id, tokenizer.eos_token_id)
            self.assertNotEqual(trainer.model.config.pad_token_id, tokenizer.pad_token_id)
            self.assertNotEqual(trainer.model.config.bos_token_id, tokenizer.bos_token_id)

            trainer.train()

            # Must be aligned as soon as we start training
            self.assertEqual(trainer.model.config.eos_token_id, tokenizer.eos_token_id)
            self.assertEqual(trainer.model.config.pad_token_id, tokenizer.pad_token_id)
            self.assertEqual(trainer.model.config.bos_token_id, tokenizer.bos_token_id)

    def test_trainer_works_without_model_config(self):
        """
        Tests that models without a `config` parameter can still be trained.
        This is useful for preserving compatibility with third parties that train different models using the
        transformers Trainer.

        If this test fails, it doesn't imply that there's issues with transformers, but perhaps with third
        parties.
        """

        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
        model = BasicTextGenerationModel(vocab_size=tokenizer.vocab_size, hidden_size=32)

        train_dataset = get_dataset(PATH_SAMPLE_TEXT, tokenizer, 100)

        with tempfile.TemporaryDirectory() as tmpdir:
            training_args = TrainingArguments(
                output_dir=tmpdir, report_to="none", max_steps=5, per_device_train_batch_size=1, use_cpu=True
            )
            trainer = Trainer(
                model=model,
                args=training_args,
                processing_class=tokenizer,
                train_dataset=train_dataset,
            )
            trainer.train()

    def test_resume_from_interrupted_training(self):
        """
        Tests resuming training from a checkpoint after a simulated interruption.
        """

        # --- Helper classes and functions defined locally for this test ---
        class DummyModel(nn.Module):
            def __init__(self, input_dim=10, num_labels=2):
                super().__init__()
                self.linear = nn.Linear(input_dim, num_labels)

            def forward(self, input_ids=None, attention_mask=None, labels=None):
                logits = self.linear(input_ids.float())
                loss = None
                if labels is not None:
                    loss_fn = nn.CrossEntropyLoss()
                    loss = loss_fn(logits, labels)
                return {"loss": loss, "logits": logits}

        class DummyDictDataset(torch.utils.data.Dataset):
            def __init__(self, input_ids, attention_mask, labels):
                self.input_ids = input_ids
                self.attention_mask = attention_mask
                self.labels = labels

            def __len__(self):
                return len(self.input_ids)

            def __getitem__(self, idx):
                return {
                    "input_ids": self.input_ids[idx],
                    "attention_mask": self.attention_mask[idx],
                    "labels": self.labels[idx],
                }

        def create_dummy_dataset():
            """Creates a dummy dataset for this specific test."""
            num_samples = 13
            input_dim = 10
            dummy_input_ids = torch.rand(num_samples, input_dim)
            dummy_attention_mask = torch.ones(num_samples, input_dim)
            dummy_labels = torch.randint(0, 2, (num_samples,))
            return DummyDictDataset(dummy_input_ids, dummy_attention_mask, dummy_labels)

        # 1. Set up a dummy model and dataset
        model = DummyModel(input_dim=10, num_labels=2)
        dummy_dataset = create_dummy_dataset()

        # 2. First training phase (simulating an interruption)
        output_dir_initial = self.get_auto_remove_tmp_dir()
        training_args_initial = TrainingArguments(
            output_dir=output_dir_initial,
            num_train_epochs=1,
            per_device_train_batch_size=2,
            gradient_accumulation_steps=3,
            save_strategy="steps",
            save_steps=1,  # Save at every step
            report_to=[],  # Disable wandb/tensorboard and other loggers
            max_steps=2,  # Stop after step 2 to simulate interruption
        )

        trainer_initial = Trainer(
            model=model,
            args=training_args_initial,
            train_dataset=dummy_dataset,
        )
        trainer_initial.train()

        # 3. Verify that a checkpoint was created before the "interruption"
        checkpoint_path = os.path.join(output_dir_initial, "checkpoint-2")
        self.assertTrue(os.path.exists(checkpoint_path), f"Checkpoint not found at {checkpoint_path}")

        # 4. Second training phase (resuming from the checkpoint)
        output_dir_resumed = self.get_auto_remove_tmp_dir()
        # Note: total steps for one epoch is ceil(13 / (2*3)) = 3.
        # We stopped at step 2, so the resumed training should run for 1 more step.
        training_args_resumed = TrainingArguments(
            output_dir=output_dir_resumed,
            num_train_epochs=1,
            per_device_train_batch_size=2,
            gradient_accumulation_steps=3,
            save_strategy="steps",
            save_steps=1,
            report_to=[],
        )

        trainer_resumed = Trainer(
            model=model,
            args=training_args_resumed,
            train_dataset=dummy_dataset,
        )
        # Resume from the interrupted checkpoint and finish the remaining training
        trainer_resumed.train(resume_from_checkpoint=checkpoint_path)

        # 5. Assertions: Check if the training completed and the final model was saved
        # The training should have completed step 3.
        # Total steps per epoch = ceil(13 samples / (2 batch_size * 3 grad_accum)) = 3
        self.assertEqual(trainer_resumed.state.global_step, 3)

        # Check that a checkpoint for the final step exists.
        final_checkpoint_path = os.path.join(output_dir_resumed, "checkpoint-3")
        self.assertTrue(os.path.exists(final_checkpoint_path))

        # Check if the model weights file exists in the final checkpoint directory.
        # Trainer saves non-PreTrainedModel models as `model.safetensors` by default if safetensors is available.
        final_model_path = os.path.join(final_checkpoint_path, SAFE_WEIGHTS_NAME)
        self.assertTrue(os.path.exists(final_model_path), "Final model checkpoint was not saved!")

    @require_torch_non_multi_accelerator
    def test_resume_batch_order(self):
        """
        Test that verifies dataloader order is reproducible when resuming from partial checkpoints.
        Tests resuming from checkpoint 7 (within epoch 1).
        """

        # --- Helper classes and functions defined locally for this test ---
        class DummyDataset(torch.utils.data.Dataset):
            def __init__(self, size: int = 32):
                self.size = size
                self.data = torch.randn((size, 10))
                self.data[:, 0] = torch.arange(0, size)  # Encode the data order
                self.labels = torch.randint(0, 10, (size,))

            def __len__(self) -> int:
                return self.size

            def __getitem__(self, idx: int):
                return {"input_ids": self.data[idx], "labels": self.labels[idx]}

        class DummyModel(nn.Module):
            def __init__(self, size: int):
                super().__init__()
                self.fc = nn.Linear(10, 10, bias=False)
                # data_order logs the order of data points seen by the model
                self.register_buffer("data_order", torch.empty(0, dtype=torch.long))

            def load_state_dict(self, state_dict, strict=True):
                # Handle data_order buffer size mismatch during checkpoint loading
                if "data_order" in state_dict:
                    saved_data_order = state_dict["data_order"]
                    if hasattr(self, "data_order") and self.data_order.shape != saved_data_order.shape:
                        # Resize the buffer to match the saved state
                        self.data_order = saved_data_order.clone()

                return super().load_state_dict(state_dict, strict=strict)

            def forward(self, input_ids: torch.Tensor, labels: torch.Tensor = None):
                logits = self.fc(input_ids)
                loss = None
                if labels is not None:
                    loss_fn = nn.CrossEntropyLoss()
                    loss = loss_fn(logits, labels)

                # Log the data order for verification
                data_indices = input_ids[:, 0].int()
                self.data_order = torch.cat([self.data_order, data_indices.detach().clone()])

                return {"loss": loss, "logits": logits}

        # Scenario 1: Run baseline training to completion
        # 1.1 Run training to completion
        set_seed(42)
        train_dataset = DummyDataset(size=10)
        model_baseline = DummyModel(size=10)

        exp_dir_baseline = self.get_auto_remove_tmp_dir()
        args_baseline = TrainingArguments(
            output_dir=str(exp_dir_baseline),
            seed=42,
            learning_rate=0.1,
            per_device_train_batch_size=2,
            gradient_accumulation_steps=1,
            save_strategy="steps",
            save_steps=1,
            num_train_epochs=3,
            optim="sgd",
            disable_tqdm=True,
            dataloader_num_workers=0,  # Ensures that main process loads the data
            report_to=[],  # Disable wandb/tensorboard and other loggers
        )

        trainer_baseline = Trainer(
            model=model_baseline,
            args=args_baseline,
            train_dataset=train_dataset,
        )

        trainer_baseline.train()

        # 1.2 Get the data order from the last saved checkpoint for the full run
        last_checkpoint_path = get_last_checkpoint(exp_dir_baseline)
        last_ckpt_num = int(os.path.basename(last_checkpoint_path).split("-")[1])  # Must be 15

        baseline_state_dict = safetensors.torch.load_file(
            os.path.join(exp_dir_baseline, f"checkpoint-{last_ckpt_num}", "model.safetensors")
        )
        baseline_data_order = baseline_state_dict["data_order"]

        # Scenario 2: Resume training from checkpoint in the middle of the second epoch
        # 2.1 Resume training from the second batch of epoch 1 (target_ckpt_num = 7)
        # 1 epoch consists of 10 points, so 5 steps with batch size 2
        target_ckpt_num = 7
        checkpoint_path = os.path.join(exp_dir_baseline, f"checkpoint-{target_ckpt_num - 1}")

        set_seed(42)
        model_resume = DummyModel(size=10)

        exp_dir_resume = self.get_auto_remove_tmp_dir()
        args_resume = TrainingArguments(
            output_dir=str(exp_dir_resume),
            seed=42,
            learning_rate=0.1,
            per_device_train_batch_size=2,
            gradient_accumulation_steps=1,
            save_strategy="steps",
            save_steps=1,
            num_train_epochs=3,
            optim="sgd",
            disable_tqdm=True,
            dataloader_num_workers=0,  # Ensures that main process loads the data
            report_to=[],  # Disable wandb/tensorboard and other loggers
        )

        trainer_resume = Trainer(
            model=model_resume,
            args=args_resume,
            train_dataset=train_dataset,
        )

        trainer_resume.train(resume_from_checkpoint=checkpoint_path)

        # 2.2 Get the data order from the last saved checkpoint for the resumed run
        resumed_state_dict = safetensors.torch.load_file(
            os.path.join(exp_dir_resume, f"checkpoint-{last_ckpt_num}", "model.safetensors")
        )
        resumed_data_order = resumed_state_dict["data_order"]

        # 3. Compare results: the data order should be identical
        self.assertTrue(
            torch.equal(baseline_data_order, resumed_data_order),
            f"Data order mismatch after checkpoint deletion and resume.\n"
            f"Baseline: {baseline_data_order}\n"
            f"Resumed: {resumed_data_order}",
        )


@require_torch
@is_staging_test
class TrainerIntegrationWithHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._token = TOKEN

    def test_push_to_hub(self):
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            output_dir_name = tmp_repo.repo_name
            with tempfile.TemporaryDirectory() as tmp_dir:
                trainer = get_regression_trainer(
                    output_dir=os.path.join(tmp_dir, output_dir_name),
                    push_to_hub=True,
                    hub_token=self._token,
                )
                url = trainer.push_to_hub()

            # Extract repo_name from the url
            re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
            self.assertTrue(re_search is not None)
            repo_name = re_search.groups()[0]

            self.assertEqual(repo_name, f"{USER}/{output_dir_name}")

            model = RegressionPreTrainedModel.from_pretrained(repo_name)
            self.assertEqual(model.a.item(), trainer.model.a.item())
            self.assertEqual(model.b.item(), trainer.model.b.item())

    def test_push_to_hub_in_organization(self):
        with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                trainer = get_regression_trainer(output_dir=tmp_dir)
                trainer.save_model()
                output_dir_name = tmp_repo.repo_name
                trainer = get_regression_trainer(
                    output_dir=os.path.join(tmp_dir, output_dir_name),
                    push_to_hub=True,
                    hub_model_id=f"valid_org/{output_dir_name}",
                    hub_token=self._token,
                )
                url = trainer.push_to_hub()

            # Extract repo_name from the url
            re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
            self.assertTrue(re_search is not None)
            repo_name = re_search.groups()[0]
            self.assertEqual(repo_name, f"valid_org/{output_dir_name}")

            model = RegressionPreTrainedModel.from_pretrained(f"valid_org/{output_dir_name}")
            self.assertEqual(model.a.item(), trainer.model.a.item())
            self.assertEqual(model.b.item(), trainer.model.b.item())

    def get_commit_history(self, repo):
        commit_logs = subprocess.run(
            ["git", "log"],
            capture_output=True,
            check=True,
            encoding="utf-8",
            cwd=repo,
        ).stdout
        commits = commit_logs.split("\n\n")[1::2]
        return [commit.strip() for commit in commits]

    # TODO: @ydshieh or @SunMarc
    @unittest.skip("unknown failure reason, possibly staging hub issue")
    def test_push_to_hub_with_saves_each_epoch(self):
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                with self.assertLogs(level="WARNING") as logs:
                    output_dir_name = tmp_repo.repo_name
                    trainer = get_regression_trainer(
                        output_dir=os.path.join(tmp_dir, output_dir_name),
                        push_to_hub=True,
                        hub_token=self._token,
                        # To avoid any flakiness if the training goes faster than the uploads.
                        hub_always_push=True,
                        save_strategy="epoch",
                    )
                    trainer.train()

            commits = list_repo_commits(f"{USER}/{output_dir_name}", token=self._token)
            commits = [c.title for c in commits]
            self.assertIn("initial commit", commits)
            self.assertIn("Training in progress, epoch 1", commits)
            self.assertIn("Training in progress, epoch 2", commits)
            # Epochs 3 and 4 are not guaranteed to be present (empty commits)
            self.assertTrue(any("Skipping to prevent empty commit." in record.message for record in logs.records))

    def test_push_to_hub_with_saves_each_n_steps(self):
        num_gpus = max(1, backend_device_count(torch_device))
        if num_gpus > 2:
            self.skipTest(reason="More than 2 GPUs available")

        with TemporaryHubRepo(token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                with self.assertLogs(level="WARNING") as logs:
                    output_dir_name = tmp_repo.repo_name
                    trainer = get_regression_trainer(
                        output_dir=os.path.join(tmp_dir, output_dir_name),
                        push_to_hub=True,
                        hub_token=self._token,
                        # To avoid any flakiness if the training goes faster than the uploads.
                        hub_always_push=True,
                        save_strategy="steps",
                        save_steps=5,
                    )
                    trainer.train()

            commits = list_repo_commits(f"{USER}/{output_dir_name}", token=self._token)
            commits = [c.title for c in commits]
            self.assertIn("initial commit", commits)

            # Some commits are skipped if nothing has changed
            # We expect 1 commit per 5 epochs + 1 commit at the end
            nb_empty_commits = len(
                [record for record in logs.records if "Skipping to prevent empty commit." in record.message]
            )
            nb_epoch_commits = len([commit for commit in commits if "Training in progress, step" in commit])

            # max_steps depend on the number of available GPUs
            max_steps = math.ceil(trainer.args.num_train_epochs * len(trainer.get_train_dataloader()))
            nb_expected_commits = len(range(5, max_steps, 5))

            # '>=' since final commit might be an empty commit as well (not deterministic)
            self.assertGreaterEqual(nb_empty_commits + nb_epoch_commits, nb_expected_commits)

    @require_tensorboard
    def test_push_to_hub_with_tensorboard_logs(self):
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                output_dir_name = tmp_repo.repo_name
                trainer = get_regression_trainer(
                    output_dir=os.path.join(tmp_dir, output_dir_name),
                    hub_token=self._token,
                    save_strategy="epoch",
                    report_to=["tensorboard"],
                    keep_report_to=True,
                )
                trainer.train()
                # Push the runs via `push_to_hub()`
                trainer.push_to_hub()

            files = list_repo_files(f"{USER}/{output_dir_name}", token=self._token)
            found_log = False
            for f in files:
                if len(f.split("runs")) > 1 and "events.out.tfevents" in f:
                    found_log = True

            assert found_log is True, "No tensorboard log found in repo"

    def test_push_to_hub_tags(self):
        # Checks if `trainer.push_to_hub()` works correctly by adding the desired
        # tag without having to pass `tags` in `push_to_hub`
        # see:
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                output_dir_name = tmp_repo.repo_name
                trainer = get_regression_trainer(
                    output_dir=os.path.join(tmp_dir, output_dir_name),
                    push_to_hub=True,
                    hub_token=self._token,
                )

                trainer.model.add_model_tags(["test-trainer-tags"])

                url = trainer.push_to_hub()

            # Extract repo_name from the url
            re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
            self.assertTrue(re_search is not None)
            repo_name = re_search.groups()[0]

            self.assertEqual(repo_name, f"{USER}/{output_dir_name}")

            model_card = ModelCard.load(repo_name)
            self.assertTrue("test-trainer-tags" in model_card.data.tags)

    def test_push_to_hub_with_revision(self):
        # Checks if `trainer.push_to_hub()` works correctly by adding revision
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            with tempfile.TemporaryDirectory() as tmp_dir:
                output_dir_name = tmp_repo.repo_name
                trainer = get_regression_trainer(
                    output_dir=os.path.join(tmp_dir, output_dir_name),
                    push_to_hub=True,
                    hub_token=self._token,
                )
                branch = "v1.0"
                create_branch(repo_id=trainer.hub_model_id, branch=branch, token=self._token, exist_ok=True)
                push_commit = trainer.push_to_hub(revision=branch)

            commits = list_repo_commits(repo_id=trainer.hub_model_id, revision=branch, token=self._token)
            self.assertEqual(commits[0].commit_id, push_commit.oid)


@require_torch
@require_optuna
class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_hyperparameter_search(self):
        class MyTrialShortNamer(TrialShortNamer):
            DEFAULTS = {"a": 0, "b": 0}

        def hp_space(trial):
            return {}

        def model_init(trial):
            if trial is not None:
                a = trial.suggest_int("a", -4, 4)
                b = trial.suggest_int("b", -4, 4)
            else:
                a = 0
                b = 0
            config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(config).to(torch_device)

        def hp_name(trial):
            return MyTrialShortNamer.shortname(trial.params)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=4,
                disable_tqdm=True,
                load_best_model_at_end=True,
                run_name="test",
                model_init=model_init,
            )
            trainer.hyperparameter_search(direction="minimize", hp_space=hp_space, hp_name=hp_name, n_trials=4)


@require_torch
@require_optuna
class TrainerHyperParameterMultiObjectOptunaIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_hyperparameter_search(self):
        class MyTrialShortNamer(TrialShortNamer):
            DEFAULTS = {"a": 0, "b": 0}

        def hp_space(trial):
            return {}

        def model_init(trial):
            if trial is not None:
                a = trial.suggest_int("a", -4, 4)
                b = trial.suggest_int("b", -4, 4)
            else:
                a = 0
                b = 0
            config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(config).to(torch_device)

        def hp_name(trial):
            return MyTrialShortNamer.shortname(trial.params)

        def compute_objective(metrics: dict[str, float]) -> list[float]:
            return metrics["eval_loss"], metrics["eval_accuracy"]

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=10,
                disable_tqdm=True,
                load_best_model_at_end=True,
                run_name="test",
                model_init=model_init,
                compute_metrics=AlmostAccuracy(),
            )
            trainer.hyperparameter_search(
                direction=["minimize", "maximize"],
                hp_space=hp_space,
                hp_name=hp_name,
                n_trials=4,
                compute_objective=compute_objective,
            )


@require_torch
@require_optuna
class TrainerHyperParameterOptunaIntegrationTestWithFullEval(unittest.TestCase):
    def test_hyperparameter_search(self):
        def hp_space(trial):
            return {}

        def model_init(trial):
            if trial is not None:
                a = trial.suggest_int("a", -4, 4)
                b = trial.suggest_int("b", -4, 4)
            else:
                a = 0
                b = 0
            config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(config).to(torch_device)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                disable_tqdm=True,
                model_init=model_init,
                fp16_full_eval=True,
            )
            trainer.hyperparameter_search(
                direction="minimize",
                hp_space=hp_space,
                n_trials=2,
            )


@require_torch
@require_ray
@unittest.skip("don't work because of a serialization issue")
class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def ray_hyperparameter_search(self):
        class MyTrialShortNamer(TrialShortNamer):
            DEFAULTS = {"a": 0, "b": 0}

        def hp_space(trial):
            from ray import tune

            return {
                "a": tune.randint(-4, 4),
                "b": tune.randint(-4, 4),
            }

        def model_init(config):
            if config is None:
                a = 0
                b = 0
            else:
                a = config["a"]
                b = config["b"]
            model_config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(model_config).to(torch_device)

        def hp_name(params):
            return MyTrialShortNamer.shortname(params)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=4,
                disable_tqdm=True,
                load_best_model_at_end=True,
                run_name="test",
                model_init=model_init,
            )
            trainer.hyperparameter_search(
                direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="ray", n_trials=4
            )

    def test_hyperparameter_search(self):
        self.ray_hyperparameter_search()

    def test_hyperparameter_search_ray_client(self):
        import ray
        from ray.util.client.ray_client_helpers import ray_start_client_server

        with ray_start_client_server():
            assert ray.util.client.ray.is_connected()
            self.ray_hyperparameter_search()


optim_test_params = []
if is_torch_available():
    default_adam_kwargs = {
        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
        "eps": TrainingArguments.adam_epsilon,
        "lr": TrainingArguments.learning_rate,
    }

    default_lion_kwargs = {
        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
        "lr": TrainingArguments.learning_rate,
    }

    default_ademamix_kwargs = {
        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2, 0.9999),
        "alpha": 5.0,
        "eps": TrainingArguments.adam_epsilon,
        "lr": TrainingArguments.learning_rate,
    }

    default_anyprecision_kwargs = {
        "use_kahan_summation": False,
        "momentum_dtype": torch.float32,
        "variance_dtype": torch.float32,
        "compensation_buffer_dtype": torch.bfloat16,
    }

    optim_test_params = [
        (
            OptimizerNames.ADAMW_TORCH,
            torch.optim.AdamW,
            default_adam_kwargs,
        ),
        (
            OptimizerNames.ADAFACTOR,
            transformers.optimization.Adafactor,
            {
                "scale_parameter": False,
                "relative_step": False,
                "lr": TrainingArguments.learning_rate,
            },
        ),
    ]

    if is_apex_available():
        import apex

        optim_test_params.append(
            (
                OptimizerNames.ADAMW_APEX_FUSED,
                apex.optimizers.FusedAdam,
                default_adam_kwargs,
            )
        )

    if is_bitsandbytes_available():
        import bitsandbytes as bnb

        optim_test_params.append(
            (
                OptimizerNames.ADAMW_BNB,
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.ADAMW_8BIT,
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.PAGED_ADAMW,
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.PAGED_ADAMW_8BIT,
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.LION,
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.LION_8BIT,
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.PAGED_LION_8BIT,
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

        optim_test_params.append(
            (
                OptimizerNames.ADEMAMIX,
                bnb.optim.AdEMAMix,
                default_ademamix_kwargs,
            )
        )
        optim_test_params.append(
            (
                OptimizerNames.ADEMAMIX_8BIT,
                bnb.optim.AdEMAMix,
                default_ademamix_kwargs,
            )
        )
        optim_test_params.append(
            (
                OptimizerNames.PAGED_ADEMAMIX_8BIT,
                bnb.optim.AdEMAMix,
                default_ademamix_kwargs,
            )
        )
        optim_test_params.append(
            (
                OptimizerNames.PAGED_ADEMAMIX,
                bnb.optim.AdEMAMix,
                default_ademamix_kwargs,
            )
        )

    if is_torchdistx_available():
        import torchdistx

        optim_test_params.append(
            (
                OptimizerNames.ADAMW_ANYPRECISION,
                torchdistx.optimizers.AnyPrecisionAdamW,
                dict(default_adam_kwargs, **default_anyprecision_kwargs),
            )
        )
    if is_torchao_available():
        from torchao.optim import AdamW4bit, AdamW8bit

        optim_test_params.append(
            (
                OptimizerNames.ADAMW_TORCH_4BIT,
                AdamW4bit,
                default_adam_kwargs,
            )
        )
        optim_test_params.append(
            (
                OptimizerNames.ADAMW_TORCH_8BIT,
                AdamW8bit,
                default_adam_kwargs,
            )
        )


@require_torch
class TrainerOptimizerChoiceTest(unittest.TestCase):
    def check_optim_and_kwargs(self, training_args: TrainingArguments, expected_cls, expected_kwargs):
        actual_cls, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
        self.assertEqual(expected_cls, actual_cls)
        self.assertIsNotNone(optim_kwargs)

        for p, v in expected_kwargs.items():
            self.assertTrue(p in optim_kwargs)
            actual_v = optim_kwargs[p]
            self.assertTrue(actual_v == v, f"Failed check for {p}. Expected {v}, but got {actual_v}.")

    @parameterized.expand(optim_test_params, skip_on_empty=True)
    def test_optim_supported(self, optim: str, expected_cls, expected_kwargs):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(output_dir=tmp_dir, optim=optim)

            # exercises all the valid --optim options
            self.check_optim_and_kwargs(trainer.args, expected_cls, expected_kwargs)
            trainer.train()

    def test_fused_adam(self):
        # Pretend that apex is installed and mock apex.optimizers.FusedAdam exists.
        # Trainer.get_optimizer_cls_and_kwargs does not use FusedAdam. It only has to return the
        # class given, so mocking apex.optimizers.FusedAdam should be fine for testing and allow
        # the test to run without requiring an apex installation.
        mock = Mock()
        modules = {
            "apex": mock,
            "apex.optimizers": mock.optimizers,
            "apex.optimizers.FusedAdam": mock.optimizers.FusedAdam,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir=tmp_dir),
                    mock.optimizers.FusedAdam,
                    default_adam_kwargs,
                )

    def test_fused_adam_no_apex(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir=tmp_dir)

            # Pretend that apex does not exist, even if installed. By setting apex to None, importing
            # apex will fail even if apex is installed.
            with patch.dict("sys.modules", {"apex.optimizers": None}):
                with self.assertRaises(ValueError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    @require_bitsandbytes
    def test_bnb_adam8bit(self):
        # Pretend that Bits and Bytes is installed and mock bnb.optim.Adam8bit exists.
        # Trainer.get_optimizer_cls_and_kwargs does not use Adam8bit. It only has to return the
        # class given, so mocking bnb.optim.Adam8bit should be fine for testing and allow
        # the test to run without requiring a bnb installation.
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir=tmp_dir),
                    mock.optim.AdamW,
                    default_adam_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_adam8bit_alias(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir=tmp_dir),
                    mock.optim.AdamW,
                    default_adam_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_adam(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir=tmp_dir),
                    mock.optim.AdamW,
                    default_adam_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_adam8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir=tmp_dir),
                    mock.optim.AdamW,
                    default_adam_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_ademamix(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADEMAMIX, output_dir=tmp_dir),
                    mock.optim.AdEMAMix,
                    default_ademamix_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_ademamix8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADEMAMIX_8BIT, output_dir=tmp_dir),
                    mock.optim.AdEMAMix,
                    default_ademamix_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_ademamix(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX, output_dir=tmp_dir),
                    mock.optim.AdEMAMix,
                    default_ademamix_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_ademamix8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX_8BIT, output_dir=tmp_dir),
                    mock.optim.AdEMAMix,
                    default_ademamix_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_lion(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.LION, output_dir=tmp_dir),
                    mock.optim.Lion,
                    default_lion_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_lion8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir=tmp_dir),
                    mock.optim.Lion,
                    default_lion_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_lion8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir=tmp_dir),
                    mock.optim.Lion,
                    default_lion_kwargs,
                )

    @require_bitsandbytes
    def test_bnb_paged_lion(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir=tmp_dir),
                    mock.optim.Lion,
                    default_lion_kwargs,
                )

    def test_bnb_adam8bit_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_adam_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_adam8bit_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_ademamix_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.ADEMAMIX, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_ademamix8bit_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.ADEMAMIX_8BIT, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_ademamix_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_ademamix8bit_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX_8BIT, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_lion_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_lion8bit_no_bnb(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir=tmp_dir)

            # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
            # bnb will fail even if `bitsandbytes` is installed.
            with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
                with self.assertRaises(ImportError):
                    Trainer.get_optimizer_cls_and_kwargs(args)

    def test_anyprecision_adamw(self):
        # Pretend that torchdistx is installed and mock torchdistx.optimizers.AnyPrecisionAdamW exists.
        # Trainer.get_optimizer_cls_and_kwargs does not use AnyPrecisioinAdamW. It only has to return the
        # class given, so mocking torchdistx.optimizers.AnyPrecisionAdamW should be fine for testing and allow
        # the test to run without requiring a bnb installation.
        mock = Mock()
        modules = {
            "torchdistx": mock,
            "torchdistx.optimizers": mock.optimizers,
            "torchdistx.optimizers.AnyPrecisionAdamW.": mock.optimizers.AnyPrecisionAdamW,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
            with patch.dict("sys.modules", modules):
                self.check_optim_and_kwargs(
                    TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir=tmp_dir),
                    mock.optimizers.AnyPrecisionAdamW,
                    dict(default_adam_kwargs, **default_anyprecision_kwargs),
                )

    def test_no_torchdistx_anyprecision_adamw(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            args = TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir=tmp_dir)

            # Pretend that torchdistx does not exist, even if installed. By setting torchdistx to None, importing
            # torchdistx.optimizers will fail even if torchdistx is installed.
            with patch.dict("sys.modules", {"torchdistx.optimizers": None}):
                with self.assertRaises(ValueError):
                    Trainer.get_optimizer_cls_and_kwargs(args)


@require_torch
@require_wandb
class TrainerHyperParameterWandbIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments("..")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_hyperparameter_search(self):
        def hp_space(trial):
            return {
                "method": "random",
                "metric": {},
                "parameters": {
                    "a": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
                    "b": {"distribution": "int_uniform", "min": 1, "max": 6},
                },
            }

        def model_init(config):
            if config is None:
                a = 0
                b = 0
            else:
                a = config["a"]
                b = config["b"]
            model_config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(model_config).to(torch_device)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                eval_strategy=IntervalStrategy.EPOCH,
                save_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=4,
                disable_tqdm=True,
                load_best_model_at_end=True,
                run_name="test",
                model_init=model_init,
            )
            sweep_kwargs = {
                "direction": "minimize",
                "hp_space": hp_space,
                "backend": "wandb",
                "n_trials": 4,
            }
            best_run = trainer.hyperparameter_search(**sweep_kwargs)

            self.assertIsNotNone(best_run.run_id)
            self.assertIsNotNone(best_run.run_summary)
            hp_keys = set(best_run.hyperparameters.keys())
            self.assertSetEqual(hp_keys, {"a", "b", "assignments", "metric"})

            # pretend restarting the process purged the environ
            import os

            del os.environ["WANDB_ENTITY"]
            del os.environ["WANDB_PROJECT"]
            sweep_kwargs["sweep_id"] = best_run.run_summary
            updated_best_run = trainer.hyperparameter_search(**sweep_kwargs)

            self.assertIsNotNone(updated_best_run.run_id)
            self.assertEqual(updated_best_run.run_summary, best_run.run_summary)
            updated_hp_keys = set(updated_best_run.hyperparameters.keys())
            self.assertSetEqual(updated_hp_keys, {"a", "b", "assignments", "metric"})


class HyperParameterSearchBackendsTest(unittest.TestCase):
    def test_hyperparameter_search_backends(self):
        self.assertEqual(
            list(ALL_HYPERPARAMETER_SEARCH_BACKENDS.keys()),
            list(HPSearchBackend),
        )


@require_torch
class OptimizerAndModelInspectionTest(unittest.TestCase):
    def test_get_num_trainable_parameters(self):
        model = nn.Sequential(nn.Linear(128, 64), nn.Linear(64, 32))
        # in_features * out_features + bias
        layer_1 = 128 * 64 + 64
        layer_2 = 64 * 32 + 32
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = Trainer(model=model, args=TrainingArguments(output_dir=tmp_dir, report_to="none"))
            self.assertEqual(trainer.get_num_trainable_parameters(), layer_1 + layer_2)
            # Freeze the last layer
            for param in model[-1].parameters():
                param.requires_grad = False
            self.assertEqual(trainer.get_num_trainable_parameters(), layer_1)

    def test_get_learning_rates(self):
        model = nn.Sequential(nn.Linear(128, 64))
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = Trainer(model=model, args=TrainingArguments(output_dir=tmp_dir, report_to="none"))
            with self.assertRaises(ValueError):
                trainer.get_learning_rates()
            trainer.create_optimizer()
            self.assertEqual(trainer.get_learning_rates(), [5e-05, 5e-05])

    def test_get_optimizer_group(self):
        model = nn.Sequential(nn.Linear(128, 64))
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = Trainer(model=model, args=TrainingArguments(output_dir=tmp_dir, report_to="none"))
            # ValueError is raised if optimizer is None
            with self.assertRaises(ValueError):
                trainer.get_optimizer_group()
            trainer.create_optimizer()
            # Get groups
            num_groups = len(trainer.get_optimizer_group())
            self.assertEqual(num_groups, 2)
            # Get group of parameter
            param = next(model.parameters())
            group = trainer.get_optimizer_group(param)
            self.assertIn(param, group["params"])

    @require_bitsandbytes
    def test_bnb_8bit_optimizer_skip_embedding(self):
        model = BasicTextGenerationModel(8, 4)
        with tempfile.TemporaryDirectory() as tmp_dir:
            for name_optim in ["rmsprop_bnb_8bit", "adamw_8bit"]:
                args = TrainingArguments(
                    output_dir=tmp_dir,
                    report_to="none",
                    optim=name_optim,
                )
                trainer = Trainer(model=model, args=args)
                optimizer = trainer.create_optimizer()
                modules = optimizer.mng.module_weight_config_triple
                self.assertNotEqual(len(modules), 0)
                module, name, config = modules[0]
                self.assertIsInstance(module, torch.nn.Embedding)
                self.assertEqual(name, "weight")
                self.assertDictEqual(config, {"optim_bits": 32})
