ml_switcheroo.testing.fuzzer
============================

.. py:module:: ml_switcheroo.testing.fuzzer

.. autoapi-nested-parse::

   Input Generation Engine for Semantics Verification.

   This module provides the `InputFuzzer`, responsible for creating randomized
   NumPy arrays, scalars, and complex container structures to feed into Framework A
   and Framework B during behavioral verification tests.

   Updates:
       - Support for `dtype` constraints in array generation (Limitation #2 Fix).



Classes
-------

.. autoapisummary::

   ml_switcheroo.testing.fuzzer.InputFuzzer


Module Contents
---------------

.. py:class:: InputFuzzer(seed_shape: Optional[Tuple[int, Ellipsis]] = None)

   Generates dummy inputs (Arrays, Scalars, Containers) for equivalence testing.


   .. py:attribute:: MAX_RECURSION_DEPTH
      :value: 3



   .. py:attribute:: max_depth
      :value: 3



   .. py:method:: generate_inputs(params: List[str], hints: Optional[Dict[str, str]] = None, constraints: Optional[Dict[str, Dict]] = None) -> Dict[str, Any]

      Creates a dictionary of `{arg_name: value}`.

      Resolves symbolic dimensions across the entire parameter set. For example,
      if hints are `{'x': "Array['N']", 'y': "Array['N']"}`, both arrays will
      have matching lengths.

      :param params: List of argument names to generate (e.g. `['x', 'axis']`).
      :param hints: Dictionary of `{arg_name: type_string}` derived from Spec.
      :param constraints: Dictionary of `{arg_name: {min, max, options, dtype}}`.

      :returns: Randomized inputs ready for Framework adaptation.
      :rtype: Dict[str, Any]



   .. py:method:: adapt_to_framework(kwargs: Dict[str, Any], framework: str) -> Dict[str, Any]

      Converts Numpy inputs to framework-specific tensor types.

      Delegates to registered adapters (e.g., `TorchAdapter`, `JaxAdapter`).

      :param kwargs: Input dictionary with Numpy values.
      :param framework: Key of the framework (e.g., "torch", "jax").

      :returns: Dict with framework-specific tensors.