"""
This file handles state-space of the data training/generation process i.e Upon specification of the pre/post conditiions
"""
"""
This file handles state-space of the data training/generation process i.e Upon specification of the pre/post conditions,
The specifications for this are as follows (within an entry of the configuration)
    {
        "state":{
            "pre":[{"approximate":{"field":"int"}},{"newdate":{"field":"format"}}],"post":[{"limit":10}]
        }
    }
"""
import importlib
import importlib.util
import sys
from datetime import datetime
from data.maker.state.default import *
import os


class State :
    @staticmethod
    def apply(_data,lpointers):
        """
        This function applies a pipeline against a given data-frame, the calling code must decide whether it is a pre/post
        :_data  data-frame
        :_lpointers functions modules returned by instance (module,_args)
        """
        for _item in lpointers :
            if _item is None :
                continue
            
            pointer = _item['module']
            _args = _item['args']
            
            _data = pointer(_data,_args)
        return _data
    @staticmethod
    def instance(_args):
        pre = []
        post=[]
       
        out  = {}
        for key in _args :
            #
            # If the item has a path property is should be ignored
            path  = _args[key]['path'] if 'path' in _args[key] else ''
            out[key] = [ State._build(dict(_item,**{'path':path})) if 'path' not in _item else State._build(_item) for _item in _args[key]['pipeline']]
            
        return out
        # if 'pre' in _args:
        #     path  = _args['pre']['path'] if 'path' in _args['pre'] else ''
            
        #     pre = [ State._build(dict(_item,**{'path':path})) for _item in _args['pre']['pipeline']]
        # else:
        #     path  = _args['post']['path'] if 'path' in _args['post'] else ''

        #     post = [ State._build(dict(_item,**{'path':path})) for _item in _args['post']['pipeline']]
        # return {'pre':pre,'post':post}
   
    @staticmethod
    def _extract(_entry):
        
        _name = list(set(_entry.keys()) - set(['path']) )
        _name = _name[0]
        path = _entry['path'] if 'path' in _entry and os.path.exists(_entry['path']) else ''
        return {"module": _name,"args": _entry[_name],'name':_name,'path':path}
        pass
    @staticmethod
    def _build(_args):
        
        _info = State._extract(_args)
        # _info = dict(_args,**_info)
        
        _info['module'] = State._instance(_info)
        return _info if _info['module'] is not None else None
            
    @staticmethod
    def _instance(_args):
        """
            :path   optional path of the file on disk
            :module   name of the function
        """
        
        _name = _args['module']
        
        if 'path' in _args and os.path.exists(_args['path']):
            path= _args['path']
            
            spec = importlib.util.spec_from_file_location(_name, path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
        else:
            #
            # Probably calling a built-in module (should be in this file)
            
            module = sys.modules['data.maker.state.default'] 
       
        return getattr(module,_name) if hasattr(module,_name) else None
 
#
# Adding a few custom functions that should be able to help ....
# These functions can be called without specifying a path
#