import enum
import subprocess
import spacy
import pyinflect
from typing import List, Union, Tuple

# BES	auxiliary “be”		Let it **be**.
# HVS	forms of “have”		I**’ve** seen the Queen
# MD	verb, modal auxiliary	VerbType=mod	This **could** work.
# VB	verb, base form	VerbForm=inf	I want to **go**.
# VBD	verb, past tense	VerbForm=fin Tense=past	This **was** a sentence.
# VBG	verb, gerund or present participle	VerbForm=part Tense=pres Aspect=prog	I am **going**.
# VBN	verb, past participle	VerbForm=part Tense=past Aspect=perf	The treasure was **lost**.
# VBP	verb, non-3rd person singular present	VerbForm=fin Tense=pres	I **want** to go.
# VBZ	verb, 3rd person singular present	VerbForm=fin Tense=pres Number=sing Person=3	He **wants** to go.

class Tense(enum.Enum):
    simple_present  = {
        'aux':[None,'VBZ'], 
        'main':['VBZ','VBP', 'VB'], 
        'tobe':{'NN':'is{}','NNS':'are{}'}
    }
    simple_past     = {
        'aux':[None, 'VBD'], 
        'main':['VBD', 'VB'], 
        'tobe':{'NN':'was{}','NNS':'were{}'}
    }
    future_simple   = {
        'aux':['MD'], 
        'main':['VB'], 
        'tobe':{'NN':'will{} be','NNS':'will{} be'}
    }
    present_cont    = {
        'aux':['VBP','VBZ'],
        'main':['VBG'], 
        'tobe':{'NN':'is{} being','NNS':'are{} being'}
    }
    past_cont       = {
        'aux':['VBD'],
        'main':['VBG'], 
        'tobe':{'NN':'was{} being','NNS':'were{} being'}
    }
    present_perfect = {
        'aux':['VBP','VBZ'],
        'main':['VBN'], 
        'tobe':{'NN':'has{} been','NNS':'have{} been'}
    }

class Parser:
    def __init__(
        self
    ) -> None:
        
        self.parser = None
        self.__init_parser(model="en_core_web_sm")

    def __init_parser(
        self,
        model: str
    ) -> None:

        self.parser = None
        try:
            self.parser = spacy.load(model)
        except:
            print(f"* Downloading {model} model...")
            _ = subprocess.Popen(
                f"python -m spacy download {model}", 
                stdout=subprocess.PIPE, 
                shell=True).communicate()

            self.parser = spacy.load(model)
    
    def verb2participle(
        self,
        verb: str
    ) -> str:

        tk = self.parser(verb)[0]
        return tk._.inflect('VBN')
    
    def subj2obj(
        self,
        pronoun: str
    ) -> str:
        """
        Convert Subject pronouns to Object pronouns.
        """
        mapping = {"i":"me","you":"you","we":"us","they":"them","he":"him","she":"her", "it":"it"}
        return mapping.get(pronoun.lower(), None)
    
    def get_gramatical_number(
        self,
        dobj_data: List[List[Tuple[str,str,str]]]
    ) -> Union[str, None]:

        result = [tag for _,dep,tag in dobj_data if dep == 'dobj']
        if len(result) == 0:
            result = None
        else:
            result = result[0].replace('NNP', 'NN')

        return result
    
    def get_verbal_tense(
        self,
        verb_data: List[List[Tuple[str,str,str,int]]]
    ) -> Union[str, None]:
        
        aux, neg, root = verb_data

        root = root[0][2] if len(root) > 0 else None
        aux = aux[0][2] if len(aux) > 0 else None

        tense_name = None
        for tense in Tense:
            if aux in tense.value['aux'] and root in tense.value['main']:
                tense_name = tense.name
                break
        
        return tense_name