import torch
from torch._meta_registrations import register_meta
from torch._prims_common import check

aten = torch.ops.aten


# since we fix the torch version to 1.13.1, we have to add unimplemented meta ops
# all these functions are from here https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py
@register_meta([aten.convolution_backward.default])
def meta_convolution_backward(
    grad_output_,
    input_,
    weight_,
    bias_sizes_opt,
    stride,
    padding,
    dilation,
    transposed,
    output_padding,
    groups,
    output_mask,
):
    # High level logic taken from slow_conv3d_backward_cpu which should
    # be representative of all convolution_backward impls
    backend_grad_input = None
    backend_grad_weight = None
    backend_grad_bias = None

    if output_mask[0]:
        backend_grad_input = grad_output_.new_empty(input_.size())
    if output_mask[1]:
        backend_grad_weight = grad_output_.new_empty(weight_.size())
    if output_mask[2]:
        backend_grad_bias = grad_output_.new_empty(bias_sizes_opt)

    return (backend_grad_input, backend_grad_weight, backend_grad_bias)


@register_meta(aten._adaptive_avg_pool2d_backward.default)
def meta__adaptive_avg_pool2d_backward(grad_out, self):
    ndim = grad_out.ndim
    for i in range(1, ndim):
        check(
            grad_out.size(i) > 0,
            lambda: f"adaptive_avg_pool2d_backward(): Expected grad_output to have non-zero \
                      size for non-batch dimensions, {grad_out.shape} with dimension {i} being empty",
        )
    check(
        ndim == 3 or ndim == 4,
        lambda: f"adaptive_avg_pool2d_backward(): Expected 3D or 4D tensor, but got {self.shape}",
    )
    check(
        self.dtype == grad_out.dtype,
        lambda: f"expected dtype {self.dtype} for `grad_output` but got dtype {grad_out.dtype}",
    )
    return self.new_empty(self.shape)