# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

from pathlib import Path
from typing import Dict

import numpy as np

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_multi_xpu,
    require_torch_neuroncore,
    require_torch_npu,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


logger = logging.get_logger(__name__)


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

    from transformers import Trainer

    class DummyDataset(Dataset):
        def __init__(self, length: int = 101):
            self.length = length

        def __len__(self):
            return self.length

        def __getitem__(self, i) -> int:
            return i

    class DummyDataCollator:
        def __call__(self, features):
            return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}

    class DummyModel(nn.Module):
        def __init__(self):
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            self.fc = nn.Linear(120, 80)

        def forward(self, input_ids, labels=None):
            if labels is not None:
                return torch.tensor(0.0, device=input_ids.device), input_ids
            else:
                return input_ids

    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 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 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


class TestTrainerDistributedNeuronCore(TestCasePlus):
    @require_torch_neuroncore
    def test_trainer(self):
        distributed_args = f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"--output_dir {output_dir}".split()
        cmd = ["torchrun"] + distributed_args + args
        execute_subprocess_async(cmd, env=self.get_env())
        # successful return here == success - any errors would have caused an error in the sub-call


class TestTrainerDistributedNPU(TestCasePlus):
    @require_torch_npu
    def test_trainer(self):
        distributed_args = f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"--output_dir {output_dir}".split()
        cmd = ["torchrun"] + distributed_args + args
        execute_subprocess_async(cmd, env=self.get_env())
        # successful return here == success - any errors would have caused an error in the sub-call


class TestTrainerDistributed(TestCasePlus):
    @require_torch_multi_gpu
    def test_trainer(self):
        distributed_args = f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"--output_dir {output_dir}".split()
        cmd = ["torchrun"] + distributed_args + args
        execute_subprocess_async(cmd, env=self.get_env())
        # successful return here == success - any errors would have caused an error in the sub-call


@require_torch_multi_xpu
class TestTrainerDistributedXPU(TestCasePlus):
    def test_trainer(self):
        distributed_args = f"""--nproc_per_node={torch.xpu.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"--output_dir {output_dir}".split()
        cmd = ["torchrun"] + distributed_args + args
        execute_subprocess_async(cmd, env=self.get_env())
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
    # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
    #
    # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

    parser = HfArgumentParser((TrainingArguments,))
    training_args = parser.parse_args_into_dataclasses()[0]

    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
    )

    # Essentially, what we want to verify in the distributed case is that we get all samples back,
    # in the right order. (this is crucial for prediction for instance)
    for dataset_length in [101, 40, 7]:
        dataset = DummyDataset(dataset_length)

        def compute_metrics(p: EvalPrediction) -> Dict:
            sequential = list(range(len(dataset)))
            success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
            if not success and training_args.local_rank == 0:
                logger.warning(
                    "Predictions and/or labels do not match expected results:\n  - predictions: "
                    f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"
                )
            return {"success": success}

        trainer = Trainer(
            model=DummyModel(),
            args=training_args,
            data_collator=DummyDataCollator(),
            eval_dataset=dataset,
            compute_metrics=compute_metrics,
        )
        metrics = trainer.evaluate()
        logger.info(metrics)
        if metrics["eval_success"] is not True:
            logger.error(metrics)
            exit(1)

        p = trainer.predict(dataset)
        logger.info(p.metrics)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics)
            exit(1)

        trainer.args.eval_accumulation_steps = 2

        metrics = trainer.evaluate()
        logger.info(metrics)
        if metrics["eval_success"] is not True:
            logger.error(metrics)
            exit(1)

        p = trainer.predict(dataset)
        logger.info(p.metrics)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics)
            exit(1)

        trainer.args.eval_accumulation_steps = None

    # Check that saving does indeed work with temp dir rotation
    # If this fails, will see a FileNotFoundError
    model = RegressionModel()
    training_args.max_steps = 1
    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
    sched = torch.optim.lr_scheduler.LambdaLR(opt, lambda x: 1)
    trainer = Trainer(
        model, training_args, optimizers=(opt, sched), data_collator=DummyDataCollator(), eval_dataset=dataset
    )
    trainer._save_checkpoint(model=None, trial=None)
    # Check that the temp folder does not exist
    assert not (Path(training_args.output_dir) / "tmp-checkpoint-0").exists()
    assert (Path(training_args.output_dir) / "checkpoint-0").exists()

    # Check that `dispatch_batches=False` will work on a finite iterable dataset

    train_dataset = FiniteIterableDataset(label_names=["labels", "extra"], length=1)

    model = RegressionModel()
    training_args.per_device_train_batch_size = 1
    training_args.max_steps = 1
    training_args.dispatch_batches = False
    trainer = Trainer(model, training_args, train_dataset=train_dataset)
    trainer.train()
