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

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

.. autoapi-nested-parse::

   Core Engine of the Input Fuzzer (Hypothesis Integration).

   This module provides the `InputFuzzer` facade which now delegates generation logic
   to Hypothesis Strategies. It maintains backward compatibility for casual usage via `generate_inputs`.



Classes
-------

.. autoapisummary::

   ml_switcheroo.testing.fuzzer.core.InputFuzzer


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

.. py:class:: InputFuzzer

   Facade for creating Hypothesis strategies based on Semantic Spec.


   .. py:method:: build_strategies(params: List[str], hints: Optional[Dict[str, str]] = None, constraints: Optional[Dict[str, Dict]] = None) -> Dict[str, hypothesis.strategies.SearchStrategy]

      Constructs a dictionary of Hypothesis strategies for the given parameters.
      Automatically handles shared symbolic dimensions (e.g. Array['N']).

      :param params: List of argument names.
      :param hints: Mapping of name -> type string.
      :param constraints: Mapping of name -> dict constraints (min, max, etc).

      :returns: Strategies ready to be fed into @given.
      :rtype: Dict[str, Strategy]



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

      Delegates to Framework Adapter to convert Numpy/Native inputs to Tensors.

      :param kwargs: Dictionary of input values.
      :param framework: Target framework key (e.g. 'torch').

      :returns: Dict with converted values.