
    G/Ph
                          d Z ddlZdgZddZdS )z!
Introspection helper functions.
    Nopt_func_infoc                   	
 ddl m}m	 | 5t          j        |           

fd|                                D             }n|}|ut          j        |          i }|                                D ]I\  }}i }|                                D ]&\  }}t          	fd|D                       r|||<   '|r|||<   Jn|}|S )am  
    Returns a dictionary containing the currently supported CPU dispatched
    features for all optimized functions.

    Parameters
    ----------
    func_name : str (optional)
        Regular expression to filter by function name.

    signature : str (optional)
        Regular expression to filter by data type.

    Returns
    -------
    dict
        A dictionary where keys are optimized function names and values are
        nested dictionaries indicating supported targets based on data types.

    Examples
    --------
    Retrieve dispatch information for functions named 'add' or 'sub' and
    data types 'float64' or 'float32':

    >>> import numpy as np
    >>> dict = np.lib.introspect.opt_func_info(
    ...     func_name="add|abs", signature="float64|complex64"
    ... )
    >>> import json
    >>> print(json.dumps(dict, indent=2))
        {
          "absolute": {
            "dd": {
              "current": "SSE41",
              "available": "SSE41 baseline(SSE SSE2 SSE3)"
            },
            "Ff": {
              "current": "FMA3__AVX2",
              "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
            },
            "Dd": {
              "current": "FMA3__AVX2",
              "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
            }
          },
          "add": {
            "ddd": {
              "current": "FMA3__AVX2",
              "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
            },
            "FFF": {
              "current": "FMA3__AVX2",
              "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
            }
          }
        }

    r   )__cpu_targets_info__dtypeNc                 F    i | ]\  }}                     |          ||S  )search).0kvfunc_patterns      T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/numpy/lib/introspect.py
<dictcomp>z!opt_func_info.<locals>.<dictcomp>I   sC     
 
 
Q""1%%
q
 
 
    c                     g | ]:}                     |          p"                      |          j                  ;S r   )r	   name)r
   cr   sig_patterns     r   
<listcomp>z!opt_func_info.<locals>.<listcomp>V   sY          &&q)) 6&&uuQxx}55  r   )numpy._core._multiarray_umathr   r   recompileitemsany)	func_name	signaturetargetsmatching_funcsmatching_sigsr   r   matching_charscharsr   r   r   s            @@@r   r   r   	   sH   t        z),,
 
 
 
$]]__
 
 

 !j++"((** 
	2 
	2DAqN"#'')) 4 4w      #     4
 -4N5) 2#1a 
	2 'r   )NN)__doc__r   __all__r   r   r   r   <module>r$      sF     
			
W W W W W Wr   