import pandas as pd
import numpy as np

def has_vectors(doc):
  return np.all([token.has_vector for token in doc])

def extract_doc_aspects(doc):

    prod_pronouns = ['it','this','they','these']

    rule1_pairs = []
    rule2_pairs = []
    rule3_pairs = []
    rule4_pairs = []
    rule5_pairs = []
    rule6_pairs = []
    rule7_pairs = []

    for token in doc:
        if token.text == 'product':
          continue

        ## FIRST RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect
        ## RULE = M is child of A with a relationship of amod
        A = "999999"
        M = "999999"
        if token.dep_ == "amod" and not token.is_stop:
            M = token.text
            A = token.head.text

            # add adverbial modifier of adjective (e.g. 'most comfortable headphones')
            M_children = token.children
            for child_m in M_children:
                if(child_m.dep_ == "advmod"):
                    M_hash = child_m.text
                    M = M_hash + " " + M
                    break

            # negation in adjective, the "no" keyword is a 'det' of the noun (e.g. no interesting characters)
            A_children = token.head.children
            for child_a in A_children:
                if(child_a.dep_ == "det" and child_a.text == 'no'):
                    neg_prefix = 'not'
                    M = neg_prefix + " " + M
                    break

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict1 = {"noun" : A, "adj" : M, "rule" : 1}
            rule1_pairs.append(dict1)


        # # SECOND RULE OF DEPENDANCY PARSE -
        # # M - Sentiment modifier || A - Aspect
        # Direct Object - A is a child of something with relationship of nsubj, while
        # M is a child of the same something with relationship of dobj
        # Assumption - A verb will have only one NSUBJ and DOBJ
        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children :
            if(child.dep_ == "nsubj" and not child.is_stop):
                A = child.text

            if((child.dep_ == "dobj" and child.pos_ == "ADJ") and not child.is_stop):
                M = child.text

            if(child.dep_ == "neg"):
                neg_prefix = child.text
                add_neg_pfx = True

        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict2 = {"noun" : A, "adj" : M, "rule" : 2}
            rule2_pairs.append(dict2)


        ## THIRD RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect
        ## Adjectival Complement - A is a child of something with relationship of nsubj, while
        ## M is a child of the same something with relationship of acomp
        ## Assumption - A verb will have only one NSUBJ and DOBJ
        ## "The sound of the speakers would be better. The sound of the speakers could be better" - handled using AUX dependency

        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children :
            if(child.dep_ == "nsubj" and not child.is_stop):
                A = child.text

            if(child.dep_ == "acomp" and not child.is_stop):
                M = child.text

            # example - 'this could have been better' -> (this, not better)
            if(child.dep_ == "aux" and child.tag_ == "MD"):
                neg_prefix = "not"
                add_neg_pfx = True

            if(child.dep_ == "neg"):
                neg_prefix = child.text
                add_neg_pfx = True

        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict3 = {"noun" : A, "adj" : M, "rule" : 3}
            rule3_pairs.append(dict3)


        ## FOURTH RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect

        #Adverbial modifier to a passive verb - A is a child of something with relationship of nsubjpass, while
        # M is a child of the same something with relationship of advmod

        #Assumption - A verb will have only one NSUBJ and DOBJ

        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children :
            if((child.dep_ == "nsubjpass" or child.dep_ == "nsubj") and not child.is_stop):
                A = child.text

            if(child.dep_ == "advmod" and not child.is_stop):
                M = child.text
                M_children = child.children
                for child_m in M_children:
                    if(child_m.dep_ == "advmod"):
                        M_hash = child_m.text
                        M = M_hash + " " + child.text
                        break

            if(child.dep_ == "neg"):
                neg_prefix = child.text
                add_neg_pfx = True

        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict4 = {"noun" : A, "adj" : M, "rule" : 4}
            rule4_pairs.append(dict4)

        ## FIFTH RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect

        #Complement of a copular verb - A is a child of M with relationship of nsubj, while
        # M has a child with relationship of cop

        #Assumption - A verb will have only one NSUBJ and DOBJ

        children = token.children
        A = "999999"
        buf_var = "999999"
        for child in children :
            if(child.dep_ == "nsubj" and not child.is_stop):
                A = child.text

            if(child.dep_ == "cop" and not child.is_stop):
                buf_var = child.text

        if(A != "999999" and buf_var != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict5 = {"noun" : A, "adj" : token.text, "rule" : 5}
            rule5_pairs.append(dict5)


        ## SIXTH RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect
        ## Example - "It ok", "ok" is INTJ (interjections like bravo, great etc)

        children = token.children
        A = "999999"
        M = "999999"
        if(token.pos_ == "INTJ" and not token.is_stop):
            for child in children :
                if(child.dep_ == "nsubj" and not child.is_stop):
                    A = child.text
                    M = token.text

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict6 = {"noun" : A, "adj" : M, "rule" : 6}
            rule6_pairs.append(dict6)

        ## SEVENTH RULE OF DEPENDANCY PARSE -
        ## M - Sentiment modifier || A - Aspect
        ## ATTR - link between a verb like 'be/seem/appear' and its complement
        ## Example: 'this is garbage' -> (this, garbage)

        children = token.children
        A = "999999"
        M = "999999"
        add_neg_pfx = False
        for child in children :
            if(child.dep_ == "nsubj" and not child.is_stop):
                A = child.text

            if((child.dep_ == "attr") and not child.is_stop):
                M = child.text

            if(child.dep_ == "neg"):
                neg_prefix = child.text
                add_neg_pfx = True

        if (add_neg_pfx and M != "999999"):
            M = neg_prefix + " " + M

        if(A != "999999" and M != "999999"):
            if A in prod_pronouns :
                A = "product"
            dict7 = {"noun" : A, "adj" : M, "rule" : 7}
            rule7_pairs.append(dict7)

    aspects = []

    aspects = rule1_pairs + rule2_pairs + rule3_pairs +rule4_pairs +rule5_pairs + rule6_pairs + rule7_pairs

    return aspects

def extract_aspects(nlp, reviews):
  aspects = []

  data = ([
    (x[1], x[0]) for x in reviews['text_cleaned'].reset_index().to_numpy()
  ])

  for doc, review_id in nlp.pipe(data, as_tuples=True):
    doc_aspects = extract_doc_aspects(doc)
    doc_aspects = [
        [review_id, aspect['noun'], aspect['adj'], aspect['rule']] 
        for aspect in doc_aspects if not aspect['noun'].lower().startswith('product')
    ]
    # filter aspects with out of vocubalary nouns
    doc_aspects = [
        doc_aspect for doc_aspect in doc_aspects 
        if has_vectors(nlp(doc_aspect[1]))
    ]
    aspects.extend(doc_aspects)

  aspects = pd.DataFrame(aspects, columns=['review_id', 'aspect', 'opinion', 'rule'])

  return aspects