#!/usr/bin/env python

# Copyright 2023 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 torch

from accelerate import PartialState
from accelerate.test_utils.testing import assert_exception
from accelerate.utils.dataclasses import DistributedType
from accelerate.utils.operations import (
    DistributedOperationException,
    broadcast,
    copy_tensor_to_devices,
    gather,
    gather_object,
    pad_across_processes,
    reduce,
)


def create_tensor(state):
    return (torch.arange(state.num_processes) + 1.0 + (state.num_processes * state.process_index)).to(state.device)


def test_gather(state):
    tensor = create_tensor(state)
    gathered_tensor = gather(tensor)
    assert gathered_tensor.tolist() == list(range(1, state.num_processes**2 + 1))


def test_gather_object(state):
    # Gather objects in TorchXLA is not supported.
    if state.distributed_type == DistributedType.XLA:
        return
    obj = [state.process_index]
    gathered_obj = gather_object(obj)
    assert len(gathered_obj) == state.num_processes, f"{gathered_obj}, {len(gathered_obj)} != {state.num_processes}"
    assert gathered_obj == list(range(state.num_processes)), f"{gathered_obj} != {list(range(state.num_processes))}"


def test_gather_non_contigous(state):
    # Skip this test because the 'is_contiguous' function of XLA tensor always returns True.
    if state.distributed_type == DistributedType.XLA:
        return
    # Create a non-contiguous tensor
    tensor = torch.arange(12).view(4, 3).t().to(state.device)
    assert not tensor.is_contiguous()
    # Shouldn't error out
    _ = gather(tensor)


def test_broadcast(state):
    tensor = create_tensor(state)
    broadcasted_tensor = broadcast(tensor)
    assert broadcasted_tensor.shape == torch.Size([state.num_processes])
    assert broadcasted_tensor.tolist() == list(range(1, state.num_processes + 1))


def test_pad_across_processes(state):
    # We need to pad the tensor with one more element if we are the main process
    # to ensure that we can pad
    if state.is_main_process:
        tensor = torch.arange(state.num_processes + 1).to(state.device)
    else:
        tensor = torch.arange(state.num_processes).to(state.device)
    padded_tensor = pad_across_processes(tensor)
    assert padded_tensor.shape == torch.Size([state.num_processes + 1])
    if not state.is_main_process:
        assert padded_tensor.tolist() == list(range(0, state.num_processes)) + [0]


def test_reduce_sum(state):
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    tensor = create_tensor(state)
    reduced_tensor = reduce(tensor, "sum")
    truth_tensor = torch.tensor([4.0, 6]).to(state.device)
    assert torch.allclose(reduced_tensor, truth_tensor), f"{reduced_tensor} != {truth_tensor}"


def test_reduce_mean(state):
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    tensor = create_tensor(state)
    reduced_tensor = reduce(tensor, "mean")
    truth_tensor = torch.tensor([2.0, 3]).to(state.device)
    assert torch.allclose(reduced_tensor, truth_tensor), f"{reduced_tensor} != {truth_tensor}"


def test_op_checker(state):
    # Must be in a distributed state, and gathering is currently not supported in TorchXLA.
    if state.distributed_type in [DistributedType.NO, DistributedType.XLA]:
        return
    state.debug = True
    # `pad_across_processes`
    if state.process_index == 0:
        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
    else:
        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4, 5]]]).to(state.device)}

    with assert_exception(DistributedOperationException):
        pad_across_processes(data, dim=0)

    # `reduce`
    if state.process_index == 0:
        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
    else:
        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)}

    with assert_exception(DistributedOperationException):
        reduce(data)

    # `broadcast`
    if state.process_index == 0:
        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
    else:
        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)}

    with assert_exception(DistributedOperationException):
        broadcast(data)

    state.debug = False


def test_copy_tensor_to_devices(state):
    if state.distributed_type not in [DistributedType.MULTI_GPU, DistributedType.XLA]:
        return
    if state.is_main_process:
        tensor = torch.tensor([1, 2, 3], dtype=torch.int).to(state.device)
    else:
        tensor = None
    tensor = copy_tensor_to_devices(tensor)
    assert torch.allclose(tensor, torch.tensor([1, 2, 3], dtype=torch.int, device=state.device))


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


def main():
    state = PartialState()
    state.print(f"State: {state}")
    state.print("testing gather")
    test_gather(state)
    state.print("testing gather_object")
    test_gather_object(state)
    state.print("testing gather non-contigous")
    test_gather_non_contigous(state)
    state.print("testing broadcast")
    test_broadcast(state)
    state.print("testing pad_across_processes")
    test_pad_across_processes(state)
    state.print("testing reduce_sum")
    test_reduce_sum(state)
    state.print("testing reduce_mean")
    test_reduce_mean(state)
    state.print("testing op_checker")
    test_op_checker(state)
    state.print("testing sending tensors across devices")
    test_copy_tensor_to_devices(state)


if __name__ == "__main__":
    main()