from typing import Any, List, OrderedDict

import torch
import torch.distributed as dist
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.testing import assert_close


def assert_equal(a: Tensor, b: Tensor):
    assert torch.all(a == b), f'expected a and b to be equal but they are not, {a} vs {b}'


def assert_not_equal(a: Tensor, b: Tensor):
    assert not torch.all(a == b), f'expected a and b to be not equal but they are, {a} vs {b}'


def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3):
    assert_close(a, b, rtol=rtol, atol=atol)


def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
    # all gather tensors from different ranks
    world_size = dist.get_world_size(process_group)
    tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
    dist.all_gather(tensor_list, tensor, group=process_group)

    # check if they are equal one by one
    for i in range(world_size - 1):
        a = tensor_list[i]
        b = tensor_list[i + 1]
        assert torch.all(a == b), f'expected tensors on rank {i} and {i + 1} to be equal but they are not, {a} vs {b}'


def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True):
    for k, v in d1.items():
        if isinstance(v, dict):
            check_state_dict_equal(v, d2[k])
        elif isinstance(v, list):
            for i in range(len(v)):
                if isinstance(v[i], torch.Tensor):
                    if not ignore_device:
                        v[i] = v[i].to("cpu")
                        d2[k][i] = d2[k][i].to("cpu")
                    assert torch.equal(v[i], d2[k][i])
                else:
                    assert v[i] == d2[k][i]
        elif isinstance(v, torch.Tensor):
            if not ignore_device:
                v = v.to("cpu")
                d2[k] = d2[k].to("cpu")
            assert torch.equal(v, d2[k])
        else:
            assert v == d2[k]


def assert_hf_output_close(out1: Any,
                           out2: Any,
                           ignore_keys: List[str] = None,
                           track_name: str = "",
                           atol=1e-5,
                           rtol=1e-5):
    """
    Check if two outputs from huggingface are equal.

    Args:
        out1 (Any): the first output
        out2 (Any): the second output
        ignore_keys (List[str]): the keys to ignore when comparing two dicts
        track_name (str): the name of the value compared, used to track the path
    """
    if isinstance(out1, dict) and isinstance(out2, dict):
        # if two values are dict
        # we recursively check the keys
        assert set(out1.keys()) == set(out2.keys())
        for k in out1.keys():
            if ignore_keys is not None and k in ignore_keys:
                continue
            assert_hf_output_close(out1[k],
                                   out2[k],
                                   track_name=f"{track_name}.{k}",
                                   ignore_keys=ignore_keys,
                                   atol=atol,
                                   rtol=rtol)
    elif isinstance(out1, (list, tuple)) and isinstance(out2, (list, tuple)):
        # if two values are list
        # we recursively check the elements
        assert len(out1) == len(out2)
        for i in range(len(out1)):
            assert_hf_output_close(out1[i],
                                   out2[i],
                                   track_name=f"{track_name}.{i}",
                                   ignore_keys=ignore_keys,
                                   atol=atol,
                                   rtol=rtol)
    elif isinstance(out1, Tensor) and isinstance(out2, Tensor):
        if out1.shape != out2.shape:
            raise AssertionError(f"{track_name}: shape mismatch: {out1.shape} vs {out2.shape}")
        assert torch.allclose(
            out1, out2, atol=atol, rtol=rtol
        ), f"{track_name}: tensor value mismatch\nvalue 1: {out1}\nvalue 2: {out2}, mean error: {torch.abs(out1 - out2).mean()}"
    else:
        assert out1 == out2, f"{track_name}: value mismatch.\nout1: {out1}\nout2: {out2}"