Assertions#

ivy_tests.test_ivy.helpers.assertions.assert_all_close(ret_np, ret_from_gt_np, backend, rtol=1e-05, atol=1e-08, ground_truth_backend='TensorFlow')[source]#

Match the ret_np and ret_from_gt_np inputs element-by-element to ensure that they are the same.

Parameters:

  • ret_np – Return from the framework to test. Ivy Container or Numpy Array.

  • ret_from_gt_np – Return from the ground truth framework. Ivy Container or Numpy Array.

  • rtol (default: 1e-05) – Relative Tolerance Value.

  • atol (default: 1e-08) – Absolute Tolerance Value.

  • ground_truth_backend (default: 'TensorFlow') – Ground Truth Backend Framework.

Returns:

None if the test passes, else marks the test as failed.

ivy_tests.test_ivy.helpers.assertions.assert_same_type(ret_from_target, ret_from_gt, backend_to_test, gt_backend)[source]#

Assert that the return types from the target and ground truth frameworks are the same.

checks with a string comparison because with_backend returns different objects. Doesn’t check recursively.

ivy_tests.test_ivy.helpers.assertions.assert_same_type_and_shape(values, this_key_chain=None)[source]#
ivy_tests.test_ivy.helpers.assertions.check_unsupported_device(*, fn, input_device, all_as_kwargs_np)[source]#

Check whether a function does not support a given device.

Parameters:

  • fn – The function to check.

  • input_device – The backend device.

  • all_as_kwargs_np – All arguments in Numpy Format, to check for the presence of dtype argument.

Returns:

True if the function does not support the given device, False otherwise.

ivy_tests.test_ivy.helpers.assertions.check_unsupported_device_and_dtype(*, fn, device, input_dtypes, all_as_kwargs_np)[source]#

Check whether a function does not support a given device or data types.

Parameters:

  • fn – The function to check.

  • device – The backend device to check.

  • input_dtypes – data-types of the input arguments and keyword-arguments.

  • all_as_kwargs_np – All arguments in Numpy Format, to check for the presence of dtype argument.

Returns:

  • True if the function does not support both the device and any data type, False

  • otherwise.

ivy_tests.test_ivy.helpers.assertions.check_unsupported_dtype(*, fn, input_dtypes, all_as_kwargs_np)[source]#

Check whether a function does not support the input data types or the output data type.

Parameters:

  • fn – The function to check.

  • input_dtypes – data-types of the input arguments and keyword-arguments.

  • all_as_kwargs_np – All arguments in Numpy Format, to check for the presence of dtype argument.

Returns:

  • True if the function does not support the given input or output data types, False

  • otherwise.

ivy_tests.test_ivy.helpers.assertions.test_unsupported_function(*, fn, args, kwargs)[source]#

Test a function with an unsupported datatype to raise an exception.

Parameters:

  • fn – callable function to test.

  • args – arguments to the function.

  • kwargs – keyword-arguments to the function.

ivy_tests.test_ivy.helpers.assertions.value_test(*, ret_np_flat, ret_np_from_gt_flat, rtol=None, atol=1e-06, specific_tolerance_dict=None, backend, ground_truth_backend='TensorFlow')[source]#

Perform a value test for matching the arrays in ret_np_flat and ret_from_np_gt_flat.

Parameters:

  • ret_np_flat – A list (flattened) containing Numpy arrays. Return from the framework to test.

  • ret_np_from_gt_flat – A list (flattened) containing Numpy arrays. Return from the ground truth framework.

  • rtol (default: None) – Relative Tolerance Value.

  • atol (default: 1e-06) – Absolute Tolerance Value.

  • specific_tolerance_dict (default: None) – (Optional) Dictionary of specific rtol and atol values according to the dtype.

  • ground_truth_backend (default: 'TensorFlow') – Ground Truth Backend Framework.

Returns:

None if the value test passes, else marks the test as failed.