# 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.

import math
import os
import re
import sys
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    backend_device_count,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_non_xpu,
    require_torch,
    require_torch_gpu,
    require_torch_multi_accelerator,
    require_torch_non_multi_accelerator,
    slow,
    torch_device,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


bindir = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"{bindir}/../../examples/pytorch/translation"):
    from run_translation import main  # noqa


set_seed(42)
MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
MBART_TINY = "sshleifer/tiny-mbart"


@require_torch
class TestTrainerExt(TestCasePlus):
    def run_seq2seq_quick(
        self,
        distributed=False,
        extra_args_str=None,
        predict_with_generate=True,
        do_train=True,
        do_eval=True,
        do_predict=True,
        n_gpus_to_use=None,
    ):
        output_dir = self.run_trainer(
            eval_steps=1,
            max_len=12,
            model_name=MBART_TINY,
            num_train_epochs=1,
            distributed=distributed,
            extra_args_str=extra_args_str,
            predict_with_generate=predict_with_generate,
            do_train=do_train,
            do_eval=do_eval,
            do_predict=do_predict,
            n_gpus_to_use=n_gpus_to_use,
        )
        logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history

        if not do_eval:
            self.skipTest(reason="do_eval is False")

        eval_metrics = [log for log in logs if "eval_loss" in log.keys()]

        first_step_stats = eval_metrics[0]
        if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            last_step_stats = eval_metrics[-1]
            assert isinstance(last_step_stats["eval_bleu"], float)
            assert not math.isnan(float(last_step_stats["eval_loss"])), "eval_loss must not be `nan`"

    @require_torch_non_multi_accelerator
    def test_run_seq2seq_no_dist(self):
        self.run_seq2seq_quick()

    # verify that the trainer can handle non-distributed with n_gpu > 1
    @require_torch_multi_accelerator
    def test_run_seq2seq_dp(self):
        self.run_seq2seq_quick(distributed=False)

    # verify that the trainer can handle distributed with n_gpu > 1
    @require_torch_multi_accelerator
    def test_run_seq2seq_ddp(self):
        self.run_seq2seq_quick(distributed=True)

    @require_non_xpu
    @require_apex
    @require_torch_gpu
    def test_run_seq2seq_apex(self):
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seq2seq_quick(distributed=True, extra_args_str="--fp16 --fp16_backend=apex")
        # test 2nd time - was getting eval_loss': nan'
        # to reproduce the problem set distributed=False
        self.run_seq2seq_quick(distributed=True, extra_args_str="--fp16 --fp16_backend=apex")

    @parameterized.expand(["base", "low", "high", "mixed"])
    @require_torch_multi_accelerator
    def test_trainer_log_level_replica(self, experiment_id):
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        experiments = {
            # test with the default log_level - should be info and thus log info once
            "base": {"extra_args_str": "", "n_matches": 1},
            # test with low log_level and log_level_replica - should be noisy on all processes
            # now the info string should appear twice on 2 processes
            "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
            # test with high log_level and low log_level_replica
            # now the info string should appear once only on the replica
            "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
            # test with high log_level and log_level_replica - should be quiet on all processes
            "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
        }

        data = experiments[experiment_id]
        kwargs = {
            "distributed": True,
            "predict_with_generate": False,
            "do_eval": False,
            "do_predict": False,
            "n_gpus_to_use": 2,
        }
        log_info_string = "Running training"
        with CaptureStderr() as cl:
            self.run_seq2seq_quick(**kwargs, extra_args_str=data["extra_args_str"])
        n_matches = len(re.findall(log_info_string, cl.err))
        self.assertEqual(n_matches, data["n_matches"])

    @slow
    def test_run_seq2seq(self):
        output_dir = self.run_trainer(
            eval_steps=2,
            max_len=128,
            model_name=MARIAN_MODEL,
            learning_rate=3e-4,
            num_train_epochs=10,
            distributed=False,
        )

        # Check metrics
        logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
        eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
        first_step_stats = eval_metrics[0]
        last_step_stats = eval_metrics[-1]

        assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
        assert isinstance(last_step_stats["eval_bleu"], float)

        # test if do_predict saves generations and metrics
        contents = os.listdir(output_dir)
        contents = {os.path.basename(p) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents

    @slow
    @require_bitsandbytes
    def test_run_seq2seq_bnb(self):
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(optim: str) -> Tuple[int, float]:
            extra_args = "--skip_memory_metrics 0"

            output_dir = self.run_trainer(
                max_len=128,
                model_name=MARIAN_MODEL,
                learning_rate=3e-4,
                num_train_epochs=1,
                optim=optim,
                distributed=True,  # force run in a new process
                extra_args_str=extra_args,
                do_eval=False,
                do_predict=False,
                n_gpus_to_use=1,  # to allow deterministic fixed memory usage
            )

            # Check metrics
            logs = TrainerState.load_from_json(Path(output_dir, "trainer_state.json")).log_history
            gpu_peak_mem_mb = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20)
            gpu_alloc_mem_mb = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20)

            loss = logs[0]["train_loss"]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        gpu_peak_mem_orig, gpu_alloc_mem_orig, loss_orig = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
        gpu_peak_mem_bnb, gpu_alloc_mem_bnb, loss_bnb = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

        gpu_alloc_mem_diff = gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        gpu_total_mem_orig = gpu_peak_mem_orig + gpu_alloc_mem_orig
        gpu_total_mem_bnb = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        gpu_total_mem_diff = gpu_total_mem_orig - gpu_total_mem_bnb

        # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
        # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
        # in 2 bytes and the diff in optim memory usage is derived as so:
        #
        # - normal 25*8=~200MB (8 bytes per param)
        # - bnb    25*2= ~50MB (2 bytes per param)
        #
        # Thus we should expect ~150MB total memory saved.
        #
        # Peak memory should be the same - the total should be different by about that same margin
        #
        # After leaving a small margin to accommodate for differences between gpus let's check
        # that we have at least 120MB in savings
        expected_savings = 120

        # uncomment the following if this test starts failing - requires py38 for a new print feature
        # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
        # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
        # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
        # print(f"{gpu_alloc_mem_diff=}MB")
        # print(f"{gpu_peak_mem_diff=}MB")
        # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
        # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

        self.assertGreater(
            gpu_alloc_mem_diff,
            expected_savings,
            "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
            f" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
            f" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB",
        )

        self.assertGreater(
            gpu_total_mem_diff,
            expected_savings,
            "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
            f" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
            f" gpu_total_mem_bnb={gpu_total_mem_bnb}MB",
        )

        self.assertEqual(
            loss_orig, loss_bnb, f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"
        )

    def run_trainer(
        self,
        max_len: int,
        model_name: str,
        num_train_epochs: int,
        learning_rate: float = 3e-3,
        optim: str = "adafactor",
        distributed: bool = False,
        extra_args_str: str = None,
        eval_steps: int = 0,
        predict_with_generate: bool = True,
        do_train: bool = True,
        do_eval: bool = True,
        do_predict: bool = True,
        n_gpus_to_use: int = None,
    ):
        data_dir = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
        output_dir = self.get_auto_remove_tmp_dir()
        args_train = f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(num_train_epochs)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(eval_steps)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
            --report_to none
        """.split()

        args_eval = f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --eval_strategy steps
            --eval_steps {str(eval_steps)}
        """.split()

        args_predict = """
            --do_predict
        """.split()

        args = []
        if do_train:
            args += args_train

        if do_eval:
            args += args_eval

        if do_predict:
            args += args_predict

        if predict_with_generate:
            args += "--predict_with_generate".split()

        if do_train:
            if optim == "adafactor":
                args += "--adafactor".split()
            else:
                args += f"--optim {optim}".split()

        if extra_args_str is not None:
            args += extra_args_str.split()

        if distributed:
            if n_gpus_to_use is None:
                n_gpus_to_use = backend_device_count(torch_device)
            master_port = get_torch_dist_unique_port()
            distributed_args = f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
            cmd = [sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(cmd, env=self.get_env())
        else:
            testargs = ["run_translation.py"] + args
            with patch.object(sys, "argv", testargs):
                main()

        return output_dir
