AttributeError: module 'importlib_resources' has no attribute 'is_resource'

[tungts1101]

Instructions To Reproduce the 🐛 Bug:

1. what changes you made (git diff) or what code you wrote: I haven't changed anything after install.
2. what exact command you run: Run the example SimCLR-1GPU:
   python tools/run_distributed_engines.py hydra.verbose=true config=configs/config/test/integration_test/quick_simclr config.DATA.TRAIN.DATA_SOURCES=[datasource_path]
3. what you observed (including full logs):

Traceback (most recent call last):
  File "tools/run_distributed_engines.py", line 57, in <module>
    hydra_main(overrides=overrides)
  File "tools/run_distributed_engines.py", line 33, in hydra_main
    cfg = compose_hydra_configuration(overrides)
  File "/home/shashank/tung/vissl/vissl/utils/hydra_config.py", line 125, in compose_hydra_configuration
    return compose("defaults", overrides=overrides)
  File "/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/hydra/experimental/compose.py", line 31, in compose
    cfg = gh.hydra.compose_config(
  File "/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 505, in compose_config
    self.config_loader.ensure_main_config_source_available()
  File "/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/hydra/_internal/config_loader_impl.py", line 120, in ensure_main_config_source_available
    if not source.available():
  File "/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/hydra/_internal/core_plugins/importlib_resources_config_source.py", line 72, in available
    ret = resources.is_resource(self.path, "__init__.py")  # type:ignore
AttributeError: module 'importlib_resources' has no attribute 'is_resource'

4. please simplify the steps as much as possible so they do not require additional resources to
   run, such as a private dataset.

Expected behavior:

If there are no obvious error in "what you observed" provided above,
please tell us the expected behavior.

Environment:

Provide your environment information using the following command:

/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/torch/cuda/__init__.py:104: UserWarning: 
NVIDIA A100 80GB PCIe with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.
If you want to use the NVIDIA A100 80GB PCIe GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

  warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
-------------------  -------------------------------------------------------------------------------------------
sys.platform         linux
Python               3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]
numpy                1.19.5
Pillow               9.3.0
vissl                0.1.6 @/home/shashank/tung/vissl/vissl
GPU available        True
GPU 0,1              NVIDIA A100 80GB PCIe
CUDA_HOME            /usr
torchvision          0.9.1 @/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/torchvision
hydra                1.0.7 @/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/hydra
classy_vision        0.7.0.dev @/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/classy_vision
tensorboard          2.14.0
apex                 0.1 @/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/apex
cv2                  4.9.0
PyTorch              1.8.1 @/home/shashank/anaconda3/envs/tung_ssl/lib/python3.8/site-packages/torch
PyTorch debug build  False
-------------------  -------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

CPU info:
----------------------------------  --------------------------------------------------------------------------------------------------------
Architecture                        x86_64
CPU op-mode(s)                      32-bit, 64-bit
Byte Order                          Little Endian
Address sizes                       43 bits physical, 48 bits virtual
CPU(s)                              128
On-line CPU(s) list                 0-127
Thread(s) per core                  1
Core(s) per socket                  64
Socket(s)                           2
NUMA node(s)                        2
Vendor ID                           AuthenticAMD
CPU family                          23
Model                               49
Model name                          AMD EPYC 7742 64-Core Processor
Stepping                            0
Frequency boost                     enabled
CPU MHz                             1499.869
CPU max MHz                         2250.0000
CPU min MHz                         1500.0000
BogoMIPS                            4500.00
Virtualization                      AMD-V
L1d cache                           4 MiB
L1i cache                           4 MiB
L2 cache                            64 MiB
L3 cache                            512 MiB
NUMA node0 CPU(s)                   0-63
NUMA node1 CPU(s)                   64-127
Vulnerability Gather data sampling  Not affected
Vulnerability Itlb multihit         Not affected
Vulnerability L1tf                  Not affected
Vulnerability Mds                   Not affected
Vulnerability Meltdown              Not affected
Vulnerability Mmio stale data       Not affected
Vulnerability Retbleed              Vulnerable
Vulnerability Spec store bypass     Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1            Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2            Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds                 Not affected
Vulnerability Tsx async abort       Not affected
----------------------------------  --------------------------------------------------------------------------------------------------------

When to expect Triage

VISSL devs and contributors aim to triage issues asap however, as a general guideline, we ask users to expect triaging in 1-2 weeks.