argbind - sweep_argbind.py test failure

______________________________________________________________________ test_example[/home/brian/workspace/python-argbind-git-aur/src/python-argbind-git/examples/mnist/sweep_argbind.py] _______________________________________________________________________

path = '/home/brian/workspace/python-argbind-git-aur/src/python-argbind-git/examples/mnist/sweep_argbind.py'

    @pytest.mark.parametrize("path", paths)
    def test_example(path):
        # Get help text
        help_args = []

        if "groups" in path:
            help_args.append("evaluate")
        output = subprocess.run(["python", path] + help_args + ["-h"],
            stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        output = output.stdout.decode('utf-8')

        _path = path.split('examples/')[-1] + '.help'
        output_path = regression_path / _path
        # Ignore the bind_module ones for help text, as the PyTorch docstrings
        # might change on us, causing tests to fail.
        if "bind_module" not in path:
>           check(output, output_path)

tests/test_examples.py:46:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

output = 'Experiment 1, batch size is 16, learning rate is 0.1.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000...ool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\n'
output_path = PosixPath('/home/brian/workspace/python-argbind-git-aur/src/python-argbind-git/tests/regression/mnist/sweep_argbind.py.help')

    def check(output, output_path):
        if not os.path.exists(output_path) or OVERWRITE:
            output_path.parent.mkdir(exist_ok=True)
            with open(output_path, 'w') as f:
                f.write(output)
        else:
            with open(output_path, 'r') as f:
                reg_output = f.read()
>           assert output == reg_output
E           AssertionError: assert 'Experiment 1, batch size is 16, learning rate is 0.1.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run :bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\nDownloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\nFailed to download (trying next):\nHTTP Error 403: Forbidden\n\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\nExtracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n\nDownloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\nFailed to download (trying next):\nHTTP Error 403: Forbidden\n\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\nExtracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n\nDownloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\nFailed to download (trying next):\nHTTP Error 403: Forbidden\n\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\nExtracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n\nDownloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\nFailed to download (trying next):\nHTTP Error 403: Forbidden\n\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\nDownloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\nExtracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n\n\nExperiment 2, batch size is 16, learning rate is 0.5.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 3, batch size is 16, learning rate is 1.0.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 4, batch size is 32, learning rate is 0.1.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 5, batch size is 32, learning rateis 0.5.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 6, batch size is 32, learning rate is 1.0.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 7, batch size is 64, learning rate is 0.1.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 8, batch size is 64, learning rate is 0.5.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 9, batch size is 64, learning rate is 1.0.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\n' == 'Experiment1, batch size is 16, learning rate is 0.1.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval: int = 10\n  save_model : bool = False\n)\n\nExperiment 2, batch size is 16, learning rate is 0.5.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\ndry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 3, batch size is 16, learning rate is 1.0.\nmain(\n  batch_size : int = 16\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 4, batch size is 32, learning rate is 0.1.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 5, batch size is 32, learning rate is 0.5.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 6, batch size is 32, learning rate is 1.0.\nmain(\n  batch_size : int = 32\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 7, batch size is 64, learning rate is 0.1.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.1\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 8, batch size is 64, learning rate is 0.5.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 0.5\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\nExperiment 9, batch size is 64, learning rate is 1.0.\nmain(\n  batch_size : int = 64\n  test_batch_size : int = 1000\n  epochs : int = 0\n  lr : float = 1.0\n  gamma : float = 0.7\n  no_cuda : bool = False\n  dry_run : bool = False\n  seed : int = 1\n  log_interval : int = 10\n  save_model : bool = False\n)\n\n'
E
E               Experiment 1, batch size is 16, learning rate is 0.1.
E               main(
E                 batch_size : int = 16
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.1
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E             + Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
E             + Failed to download (trying next):
E             + HTTP Error 403: Forbidden
E             +
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
E             + Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
E             +
E             + Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
E             + Failed to download (trying next):
E             + HTTP Error 403: Forbidden
E             +
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
E             + Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
E             +
E             + Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
E             + Failed to download (trying next):
E             + HTTP Error 403: Forbidden
E             +
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
E             + Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
E             +
E             + Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
E             + Failed to download (trying next):
E             + HTTP Error 403: Forbidden
E             +
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
E             + Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
E             + Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
E             +
E
E               Experiment 2, batch size is 16, learning rate is 0.5.
E               main(
E                 batch_size : int = 16
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.5
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 3, batch size is 16, learning rate is 1.0.
E               main(
E                 batch_size : int = 16
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 1.0
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 4, batch size is 32, learning rate is 0.1.
E               main(
E                 batch_size : int = 32
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.1
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 5, batch size is 32, learning rate is 0.5.
E               main(
E                 batch_size : int = 32
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.5
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 6, batch size is 32, learning rate is 1.0.
E               main(
E                 batch_size : int = 32
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 1.0
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 7, batch size is 64, learning rate is 0.1.
E               main(
E                 batch_size : int = 64
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.1
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 8, batch size is 64, learning rate is 0.5.
E               main(
E                 batch_size : int = 64
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 0.5
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )
E
E               Experiment 9, batch size is 64, learning rate is 1.0.
E               main(
E                 batch_size : int = 64
E                 test_batch_size : int = 1000
E                 epochs : int = 0
E                 lr : float = 1.0
E                 gamma : float = 0.7
E                 no_cuda : bool = False
E                 dry_run : bool = False
E                 seed : int = 1
E                 log_interval : int = 10
E                 save_model : bool = False
E               )

tests/test_examples.py:28: AssertionError

---------- coverage: platform linux, python 3.12.4-final-0 -----------
Name                  Stmts   Miss  Cover   Missing
---------------------------------------------------
argbind/__init__.py       1      1     0%   1
argbind/argbind.py      314    314     0%   1-499
---------------------------------------------------
TOTAL                   315    315     0%
Coverage XML written to file coverage.xml