import re
import spacy
import torch

class Tree:
    def __init__(self, val=None, formula=None):
        if val:
            self.val = val
            self.children = []
        if formula:
            root = self.parse(formula)
            self.val = root.val
            self.children = root.children

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            # NOTE: this was a huge fuckup
            # if m.group(1) not in '&|$~':
            #     return Tree(val=m.group(0))
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1

            splits.append(subformula[start:end + 1])
            root.children = [ self.parse(sub) for sub in splits ]
            return root
        return Tree(val=formula)

    def flatten(self):
        """
        flatten a tree into string
        """
        if len(self.children) == 0:
            return self.val
        params = (',').join([ child.flatten() for child in self.children ])
        return '%s(%s)' % (self.val, params)

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = (' , ').join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()

        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [
         self.val]
        for child in self.children:
            ret += [ '\t' + child_s for child_s in str(child).split('\n') ]

        return ('\n').join(ret)

nlp = spacy.load('en')

class DepTree:
    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab

        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]

            self.input = torch.tensor(self.idx,
                                      dtype=torch.long,
                                      device=self.device)

            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child,
                                             src_vocab=src_vocab,
                                             device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise

20cedc6dd05ac848fc7f9c6025fb296b663e0e4e
<mask token>


class Tree:
    <mask token>

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1
            splits.append(subformula[start:end + 1])
            root.children = [self.parse(sub) for sub in splits]
            return root
        return Tree(val=formula)
    <mask token>

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = ' , '.join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()
        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [self.val]
        for child in self.children:
            ret += [('\t' + child_s) for child_s in str(child).split('\n')]
        return '\n'.join(ret)


<mask token>


class DepTree:

    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab
        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]
            self.input = torch.tensor(self.idx, dtype=torch.long, device=
                self.device)
            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child, src_vocab=
                    src_vocab, device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise

<mask token>


class Tree:
    <mask token>

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1
            splits.append(subformula[start:end + 1])
            root.children = [self.parse(sub) for sub in splits]
            return root
        return Tree(val=formula)

    def flatten(self):
        """
        flatten a tree into string
        """
        if len(self.children) == 0:
            return self.val
        params = ','.join([child.flatten() for child in self.children])
        return '%s(%s)' % (self.val, params)

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = ' , '.join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()
        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [self.val]
        for child in self.children:
            ret += [('\t' + child_s) for child_s in str(child).split('\n')]
        return '\n'.join(ret)


<mask token>


class DepTree:

    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab
        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]
            self.input = torch.tensor(self.idx, dtype=torch.long, device=
                self.device)
            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child, src_vocab=
                    src_vocab, device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise

<mask token>


class Tree:

    def __init__(self, val=None, formula=None):
        if val:
            self.val = val
            self.children = []
        if formula:
            root = self.parse(formula)
            self.val = root.val
            self.children = root.children

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1
            splits.append(subformula[start:end + 1])
            root.children = [self.parse(sub) for sub in splits]
            return root
        return Tree(val=formula)

    def flatten(self):
        """
        flatten a tree into string
        """
        if len(self.children) == 0:
            return self.val
        params = ','.join([child.flatten() for child in self.children])
        return '%s(%s)' % (self.val, params)

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = ' , '.join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()
        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [self.val]
        for child in self.children:
            ret += [('\t' + child_s) for child_s in str(child).split('\n')]
        return '\n'.join(ret)


nlp = spacy.load('en')


class DepTree:

    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab
        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]
            self.input = torch.tensor(self.idx, dtype=torch.long, device=
                self.device)
            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child, src_vocab=
                    src_vocab, device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise

import re
import spacy
import torch


class Tree:

    def __init__(self, val=None, formula=None):
        if val:
            self.val = val
            self.children = []
        if formula:
            root = self.parse(formula)
            self.val = root.val
            self.children = root.children

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1
            splits.append(subformula[start:end + 1])
            root.children = [self.parse(sub) for sub in splits]
            return root
        return Tree(val=formula)

    def flatten(self):
        """
        flatten a tree into string
        """
        if len(self.children) == 0:
            return self.val
        params = ','.join([child.flatten() for child in self.children])
        return '%s(%s)' % (self.val, params)

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = ' , '.join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()
        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [self.val]
        for child in self.children:
            ret += [('\t' + child_s) for child_s in str(child).split('\n')]
        return '\n'.join(ret)


nlp = spacy.load('en')


class DepTree:

    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab
        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]
            self.input = torch.tensor(self.idx, dtype=torch.long, device=
                self.device)
            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child, src_vocab=
                    src_vocab, device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise

import re
import spacy
import torch

class Tree:
    def __init__(self, val=None, formula=None):
        if val:
            self.val = val
            self.children = []
        if formula:
            root = self.parse(formula)
            self.val = root.val
            self.children = root.children

    def parse(self, formula):
        m = re.search('(.*?)\\((.*)\\)', formula)
        if m:
            # NOTE: this was a huge fuckup
            # if m.group(1) not in '&|$~':
            #     return Tree(val=m.group(0))
            root = Tree(val=m.group(1))
            subformula = m.group(2)
            splits = []
            paren_count = 0
            start = 0
            for end in range(len(subformula)):
                if subformula[end] == '(':
                    paren_count += 1
                elif subformula[end] == ')':
                    paren_count -= 1
                elif paren_count == 0:
                    if subformula[end] == ',':
                        splits.append(subformula[start:end])
                        start = end + 1

            splits.append(subformula[start:end + 1])
            root.children = [ self.parse(sub) for sub in splits ]
            return root
        return Tree(val=formula)

    def flatten(self):
        """
        flatten a tree into string
        """
        if len(self.children) == 0:
            return self.val
        params = (',').join([ child.flatten() for child in self.children ])
        return '%s(%s)' % (self.val, params)

    def inorder(self):
        """
        generate the sequence inorder (include the non-terminals)
        """
        if len(self.children) == 0:
            return ['%s' % self.val]
        params = (' , ').join([' <N> '] * len(self.children))
        inorder = ['%s ( %s ) ' % (self.val, params)]
        for child in self.children:
            inorder += child.inorder()

        return inorder

    def __str__(self):
        """
        print a tree in hierarchical structure
        """
        if len(self.children) == 0:
            return self.val
        ret = [
         self.val]
        for child in self.children:
            ret += [ '\t' + child_s for child_s in str(child).split('\n') ]

        return ('\n').join(ret)

nlp = spacy.load('en')

class DepTree:
    def __init__(self, sent=None, node=None, src_vocab=None, device='cpu'):
        self.device = device
        self.src_vocab = src_vocab

        if sent:
            doc = nlp(unicode(sent))
            node = self.get_root(doc)
        if node:
            self.val = node.text.lower()
            if self.val not in self.src_vocab.vocab:
                self.val = '<UNK>'
            self.idx = src_vocab.word_to_index[self.val]

            self.input = torch.tensor(self.idx,
                                      dtype=torch.long,
                                      device=self.device)

            self.children = []
            for child in node.children:
                self.children.append(DepTree(node=child,
                                             src_vocab=src_vocab,
                                             device=self.device))

    def get_root(self, doc):
        for token in doc:
            if token.dep_ == 'ROOT':
                return token
        raise
