# Copyright The Lightning AI 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.
from unittest import mock
from unittest.mock import Mock

import pytest
import torch

from lightning.fabric.plugins import FSDPPrecision
from tests_fabric.helpers.runif import RunIf


@mock.patch("lightning.fabric.plugins.precision.fsdp._TORCH_GREATER_EQUAL_1_12", False)
def test_fsdp_precision_support(*_):
    with pytest.raises(NotImplementedError, match="`FSDPPrecision` is supported from PyTorch v1.12.0"):
        FSDPPrecision(precision="16-mixed")


@RunIf(min_torch="1.12")
@pytest.mark.parametrize(
    ("precision", "expected"),
    [
        ("16-mixed", (torch.float32, torch.float16, torch.float16)),
        ("bf16-mixed", (torch.float32, torch.bfloat16, torch.bfloat16)),
        # TODO: add 16-true and bf16-true once supported
    ],
)
def test_fsdp_precision_config(precision, expected):
    plugin = FSDPPrecision(precision=precision)
    config = plugin.mixed_precision_config

    assert config.param_dtype == expected[0]
    assert config.buffer_dtype == expected[1]
    assert config.reduce_dtype == expected[2]


@RunIf(min_torch="1.12")
def test_fsdp_precision_default_scaler():
    from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

    precision = FSDPPrecision(precision="16-mixed")
    assert isinstance(precision.scaler, ShardedGradScaler)


@RunIf(min_torch="1.12")
def test_fsdp_precision_scaler_with_bf16():
    with pytest.raises(ValueError, match="`precision='bf16-mixed'` does not use a scaler"):
        FSDPPrecision(precision="bf16-mixed", scaler=Mock())

    precision = FSDPPrecision(precision="bf16-mixed")
    assert precision.scaler is None


@RunIf(min_torch="1.12", min_cuda_gpus=1)
def test_fsdp_precision_forward_context():
    """Test to ensure that the context manager correctly is set to bfloat16."""
    precision = FSDPPrecision(precision="16-mixed")
    assert isinstance(precision.scaler, torch.cuda.amp.GradScaler)
    assert torch.get_default_dtype() == torch.float32
    with precision.forward_context():
        # check with str due to a bug upstream: https://github.com/pytorch/pytorch/issues/65786
        assert str(torch.get_autocast_gpu_dtype()) in ("torch.float16", "torch.half")

    precision = FSDPPrecision(precision="bf16-mixed")
    assert precision.scaler is None
    with precision.forward_context():
        # check with str due to a bug upstream: https://github.com/pytorch/pytorch/issues/65786
        assert str(torch.get_autocast_gpu_dtype()) == str(torch.bfloat16)

    context_manager = precision._autocast_context_manager()
    assert isinstance(context_manager, torch.autocast)
    # check with str due to a bug upstream: https://github.com/pytorch/pytorch/issues/65786
    assert str(context_manager.fast_dtype) == str(torch.bfloat16)


@RunIf(min_torch="1.12")
def test_fsdp_precision_backward():
    precision = FSDPPrecision(precision="16-mixed")
    precision.scaler = Mock()
    precision.scaler.scale = Mock(side_effect=(lambda x: x))
    tensor = Mock()
    model = Mock()
    precision.backward(tensor, model, "positional-arg", keyword="arg")
    precision.scaler.scale.assert_called_once_with(tensor)
    tensor.backward.assert_called_once_with("positional-arg", keyword="arg")


@RunIf(min_torch="1.12")
def test_fsdp_precision_optimizer_step_with_scaler():
    precision = FSDPPrecision(precision="16-mixed")
    precision.scaler = Mock()
    optimizer = Mock()

    precision.optimizer_step(optimizer, keyword="arg")
    precision.scaler.step.assert_called_once_with(optimizer, keyword="arg")
    precision.scaler.update.assert_called_once()


@RunIf(min_torch="1.12")
def test_fsdp_precision_optimizer_step_without_scaler():
    precision = FSDPPrecision(precision="bf16-mixed")
    assert precision.scaler is None
    optimizer = Mock()

    precision.optimizer_step(optimizer, keyword="arg")
    optimizer.step.assert_called_once_with(keyword="arg")


@RunIf(min_torch="1.12")
def test_invalid_precision_with_fsdp_precision():
    FSDPPrecision("16-mixed")
    FSDPPrecision("bf16-mixed")

    with pytest.raises(ValueError, match="is not supported in FSDP. `precision` must be one of"):
        FSDPPrecision(precision="64-true")
