def find_dependencies(self, **kwargs):
"""
Returns the string with dependencies identified
using the given parser.
"""
dependency_finder = Dependency()
return dependency_finder.find_dependencies(text=kwargs.get("text"), parser=kwargs.get("parser"), response_type=kwargs.get("response_type"))
def handle_dependency(dependency: Dependency):
try:
if not dependency.is_installed():
dependency.install()
return
except:
logging.warn("Install failed, attempting to update", dependency.name)
try:
dependency.update()
except:
logging.error("Failed to install dependency:", dependency)
def find_dependencies(self, **kwargs):
"""
Returns the string with dependencies identified
using the given parser.
"""
dependency_finder = Dependency()
return dependency_finder.find_dependencies(
text=kwargs.get("text"),
parser=kwargs.get("parser"),
response_type=kwargs.get("response_type"))
def main():
argv = docopt(
doc=__doc__.format(os.path.basename(sys.argv[0])),
argv=sys.argv[1:],
version=__version__
)
if argv['FILE']:
dependency = Dependency()
dependency.copyPom(os.path.abspath(argv['FILE']))
dependency.getDependenciesandScan()
shutil.rmtree(dependency.tempdir)
def parse_requirements(requirements, courses_taken):
if type(requirements) == Course:
if requirements.code in courses_taken:
return None
else:
return requirements
c1 = parse_requirements(requirements.children[0], courses_taken)
c2 = parse_requirements(requirements.children[1], courses_taken)
if requirements.logic == "and":
if c1 == None and c2 == None:
return None
elif c1 == None:
return c2
elif c2 == None:
return c1
ret = Dependency(give_id=False)
ret.add_children("&", c1, c2, find_children_id=False)
return ret
elif requirements.logic == "or":
if c1 == None or c2 == None:
return None
ret = Dependency(give_id=False)
ret.add_children("|", c1, c2, find_children_id=False)
return ret
def scan_file(file):
with open(f"{file}.txt") as text_gradle_file:
lines = text_gradle_file.readlines()
dependencies = []
changes = []
dependency_dump = []
new_dependencies = get_new_versions()
for line in lines:
if "compile" in line:
extracted_elements = re.findall(r"'([^']+)'", line)
if len(extracted_elements) > 0:
dependency = Dependency(extracted_elements[0],
extracted_elements[1],
extracted_elements[2])
dependencies.append(dependency)
dependency_dump.append(dependency.name)
for current_dependency in dependencies:
for new_dependency in new_dependencies:
if current_dependency.name == new_dependency.name and current_dependency.group == new_dependency.group:
if not current_dependency.version == new_dependency.version:
if new_version_is_higher(new_dependency.version,
current_dependency.version):
changes.append(
build_log_entry(f"{file}.gradle",
current_dependency.name,
current_dependency.version,
new_dependency.version))
current_dependency.version = new_dependency.version
update_dependency_dump(file, dependency_dump)
update_change_log(changes)
apply_new_versions(dependencies, file)
def handle_dependencies(dependencies_config: List[Dict[str,
List[str] or str]]):
for dep in dependencies_config:
dependency = Dependency(dep["name"],
check_in_dict_failback("install", dep),
check_in_dict_failback("update", dep),
check_in_dict_failback("check", dep))
handle_dependency(dependency)
def end(self, tag):
self.tag = ''
if tag == 'sentences':
if self.parse_sent:
self.parse_sent = False
elif tag == 'sentence':
if self.parse_sent:
if self.sent is not None:
self.sents.append(deepcopy(self.sent))
self.sent = None
elif tag == 'token':
# map corenlp ner tags to coerse grained ner tags
token = Token(self.word,
self.lemma,
self.pos,
ner=convert_corenlp_ner_tag(self.ner))
self.sent.add_token(deepcopy(token))
self.word = ''
self.lemma = ''
self.pos = ''
self.ner = ''
elif tag == 'dependencies':
if self.parse_dep:
self.parse_dep = False
elif tag == 'dep':
if self.parse_dep:
if not self.copied_dep:
if self.dep_label != 'root':
dep = Dependency(self.dep_label, self.gov_idx,
self.dep_idx, self.extra)
self.sent.add_dep(deepcopy(dep))
else:
self.copied_dep = False
self.dep_label = ''
self.gov_idx = -1
self.dep_idx = -1
self.extra = False
elif tag == 'coreference':
if self.parse_coref:
if self.coref is not None:
self.corefs.append(deepcopy(self.coref))
self.coref = None
else:
self.parse_coref = False
elif tag == 'mention':
mention = Mention(self.sent_idx,
self.start_token_idx,
self.end_token_idx,
head_token_idx=self.head_token_idx,
rep=self.rep,
text=self.text.encode('ascii', 'ignore'))
self.coref.add_mention(deepcopy(mention))
self.sent_idx = -1
self.start_token_idx = -1
self.end_token_idx = -1
self.head_token_idx = -1
self.rep = False
self.text = ''
def extract_verb_phrases(self, **kwargs):
"""
Returns the verb phrases found within
a text.
"""
# Instantiating variables
verb_phrases = []
verb_phrase_extractor = Dependency()
if kwargs.get("parser").lower() != "nltk":
# Getting verb phrases using the passed parser (Pattern or NLPNET)
dependencies = verb_phrase_extractor.find_dependencies(
text=kwargs.get("text"), parser=kwargs.get("parser")).split()
for dependency_index in range(0, len(dependencies)):
# Check to see if the word is a verb or part of a verb phrase
if "VP-" in dependencies[dependency_index]:
# Check to see if the last word was part of a verb phrase
if dependency_index != 0 and "VP-" in dependencies[
dependency_index - 1]:
# Since it was, append this verb onto the last verb phrase
verb_phrases[len(verb_phrases)] += " " + re.sub(
'/.*', '', dependencies[dependency_index])
# Continue on to the next word in the sentence
continue
# Remove the ending information, leaving the word
cleaned_word = re.sub('/.*', '',
dependencies[dependency_index])
# Add the word to the verb phrase
verb_phrases.append(cleaned_word)
else:
# Getting verb phrases using the NLTK parser
dependencies = verb_phrase_extractor.find_dependencies(
text=kwargs.get("text"), parser=kwargs.get("parser"))
# Adding the verbs to the verb_phrases
verb_phrases.append(dependencies[1][1])
# Returning the found verb_phrases
return verb_phrases
def __init__(self, mode='all'):
super(JointMultiTaskModel, self).__init__()
# models
self.lang_model = CharacterLanguageModel()
self.postag = POSTag()
self.chunking = Chunking()
self.dependency = Dependency()
self.sentiment = SentimentClassification()
# Mode - all or module_name
self.mode = mode
def extract_slot(self, nlp, sentence, target_slot):
sentence = str.lower(sentence)
tokens_nlp = nlp.word_tokenize(sentence)
dependency_nlp = nlp.dependency_parse(sentence)
dependency = Dependency(sentence, dependency_nlp, tokens_nlp)
self.current_rules_set.update(dependency)
fillers = self.current_rules_set.apply(target_slot)
return zip(self.current_frame.listslots(), fillers)
def read_dependencies(filename):
cparse = ConfigParser()
cparse.read(filename)
dependencies = {}
for dep_name in cparse.sections():
try:
libs = cparse.get(dep_name, "libs")
cheads = cparse.get(dep_name, "check_headers")
except KeyError:
print "Incomplete dependency spec for {0}, (needs libs & check_headers)".format(dep_name)
dependencies[dep_name] = Dependency(dep_name, libs, cheads)
return dependencies
def extract_verb_phrases(self, **kwargs):
"""
Returns the verb phrases found within
a text.
"""
# Instantiating variables
verb_phrases = []
verb_phrase_extractor = Dependency()
if kwargs.get("parser").lower() != "nltk":
# Getting verb phrases using the passed parser (Pattern or NLPNET)
dependencies = verb_phrase_extractor.find_dependencies(text=kwargs.get("text"), parser=kwargs.get("parser")).split()
for dependency_index in range(0, len(dependencies)):
# Check to see if the word is a verb or part of a verb phrase
if "VP-" in dependencies[dependency_index]:
# Check to see if the last word was part of a verb phrase
if dependency_index != 0 and "VP-" in dependencies[dependency_index - 1]:
# Since it was, append this verb onto the last verb phrase
verb_phrases[len(verb_phrases)] += " " + re.sub('/.*', '', dependencies[dependency_index])
# Continue on to the next word in the sentence
continue
# Remove the ending information, leaving the word
cleaned_word = re.sub('/.*', '', dependencies[dependency_index])
# Add the word to the verb phrase
verb_phrases.append(cleaned_word)
else:
# Getting verb phrases using the NLTK parser
dependencies = verb_phrase_extractor.find_dependencies(text=kwargs.get("text"), parser=kwargs.get("parser"))
# Adding the verbs to the verb_phrases
verb_phrases.append(dependencies[1][1])
# Returning the found verb_phrases
return verb_phrases
def install_brew():
try:
dep = Dependency(
"brew",
"/usr/bin/ruby -e $(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)",
"brew upgrade && brew upgrade")
if dep.is_installed():
dep.update()
else:
dep.install()
except Exception as e:
logging.error("Error installing brew, possible network error", e)
def get_new_versions():
vulnerabilities_url = os.getenv("VULNERABILITIES_ENDPOINT",
"http://localhost:5000/vulnerabilities")
response = requests.get(vulnerabilities_url)
if response.status_code == 200:
response_content = json.loads(response.content.decode("UTF-8"))
new_versions = []
for dep in response_content["vulnerabilities"]:
new_versions.append(
Dependency(dep.get("group"), dep.get("name"),
dep.get("version")))
return new_versions
else:
raise Exception(f"Error, HTTP status code: {response.status_code}")
def main():
argv = docopt(doc=__doc__.format(os.path.basename(sys.argv[0])),
argv=sys.argv[1:],
version=__version__)
if argv['FILE']:
dependency = Dependency()
dependency.copyPom(os.path.abspath(argv['FILE']))
dependency.getDependenciesandScan()
shutil.rmtree(dependency.tempdir)
def load_ctb(data_path: str):
file_names: List[str] = os.listdir(data_path)
ctb_files: List[str] = [data_path + fle for fle in file_names]
# id, form, tag, head, rel
sentence: List[Dependency] = []
for ctb_file in ctb_files:
with open(ctb_file, 'r', encoding='utf-8') as f:
# data example: 1 上海 _ NR NR _ 2 nn _ _
for line in f.readlines():
toks = line.split()
if len(toks) == 0:
yield sentence
sentence = []
elif len(toks) == 10:
dep = Dependency(toks[0], toks[1], toks[3], toks[6],
toks[7])
sentence.append(dep)
from dependency import Dependency
from metrics import UAS
language = 'Korean'
langcode = 'ko'
PUD_CONLL = f"/net/data/universal-dependencies-2.6/UD_{language}-PUD/{langcode}_pud-ud-test.conllu"
PUD_TOKENS = f"/net/data/universal-dependencies-2.6/UD_{language}-PUD/{langcode}_pud-ud-test.txt"
dependency_trees = Dependency(PUD_CONLL, PUD_TOKENS)
uas_metric = UAS(dependency_trees)
left_branching = [
list(zip(range(1, len(toks)), range(0,
len(toks) - 1))) + [(0, -1)]
for toks in dependency_trees.tokens
]
right_branching = [
list(zip(range(0,
len(toks) - 1), range(1, len(toks)))) +
[(len(toks) - 1, -1)] for toks in dependency_trees.tokens
]
uas_metric.reset_state()
uas_metric(range(1000), left_branching)
print("UAS left branching: ", uas_metric.result() * 100)
uas_metric.reset_state()
uas_metric(range(1000), right_branching)
print("UAS right branching: ", uas_metric.result() * 100)
gr_right_branching = right_branching
ax.set_yticks(np.arange(len(sentence_tokens)))
ax.set_xticks(np.arange(len(sentence_tokens)))
ax.set_xticklabels(sentence_tokens, rotation=90)
ax.set_yticklabels(sentence_tokens)
ax.set_ylim(top=-0.5, bottom=len(sentence_tokens) - 0.5)
plt.savefig(out_file, dpi=300, format='pdf')
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("attentions", type=str, help="NPZ file with attentions")
ap.add_argument("tokens", type=str, help="Labels (tokens) separated by spaces")
ap.add_argument("conll", type=str, help="Conll file for head selection.")
ap.add_argument("-layer_idcs", nargs="*", type=int, default=[5, 3], help = "layer indices to plot")
ap.add_argument("-head_idcs", nargs="*", type=int, default=[4, 9], help="head indices to plot")
ap.add_argument("-s", "--sentences", nargs='*', type=int, default=list(range(10)), help="Only use the specified sentences; 0-based")
ap.add_argument("-vis-dir", type=str, default="../results", help="Directory where to save head visualizations")
args = ap.parse_args()
dependency_tree = Dependency(args.conll, args.tokens)
bert_attns = AttentionWrapper(args.attentions, dependency_tree.wordpieces2tokens, args.sentences)
for sent_idx, attn_mats in bert_attns:
for l, h in zip(args.layer_idcs, args.head_idcs):
out_file = os.path.join(args.vis_dir, f"L-{l}_H-{h}_sent-{sent_idx}.pdf")
plot_head(attn_mats, dependency_tree.tokens[sent_idx], l, h, out_file)
dependencyList = set()
evaluateDependency = False
skipPattern = re.compile('\[INFO\]\s+\n')
for line in mvnOutput:
if 'The following files have been resolved:' in line:
# This is our starting grid for the dependency
evaluateDependency = True
elif evaluateDependency:
if not skipPattern.match(line):
matchObj = re.match(
r'\[INFO\]\s+([\w.\-_]+):([\w.\-_]+):\w+:([\w.\-_]+):(\w+)',
line)
if matchObj.group(1) in groupToSkip:
continue
dependencyToAdd = Dependency(groupId=matchObj.group(1),
artifactId=matchObj.group(2),
versionId=matchObj.group(3),
scope=matchObj.group(4))
dependencyList.add(dependencyToAdd)
else:
evaluateDependency = False
print 'Unique dependencies ' + str(len(dependencyList)) + '\n'
# Now we have the list of dependencies... let's get the info about them from the maven central
for dependency in dependencyList:
print dependency.name()
urlToHit = URL_CONSTANT + str(dependency.groupId) + "/" + str(
dependency.artifactId) + "/" + str(dependency.versionId)
resp = requests.get(urlToHit)
response = resp.text
soup = BeautifulSoup(response, "html.parser")
def inclusion(self, alpha: set, beta: set):
if beta.issubset(alpha):
# Dependencia (α, β)
return Dependency(alpha, beta)
def pseudo_transitivity(self, fd1: Dependency, fd2: Dependency, gamma: set):
if fd1.beta.issubset(fd2.alpha) and gamma.union(fd1.beta) == fd2.alpha:
# Dependencia (α ∪ γ, ω)
return Dependency(fd1.alpha.union(gamma), fd2.beta)
tokens[34] = ['every', 'day']
sentences[35] = 'each day'
dep[35] = [('ROOT', 0, 2), ('det', 2, 1)]
tokens[35] = ['each', 'day']
sentences[36] = 'every monday'
dep[36] = [('ROOT', 0, 2), ('det', 2, 1)]
tokens[36] = ['every', 'monday']
sentences[37] = 'each monday'
dep[37] = [('ROOT', 0, 2), ('det', 2, 1)]
tokens[37] = ['each', 'monday']
#~~~~~~~ copy/paste generated code from test_generated.py in the upper part.
from rules_cleaning import CleaningRules
from dependency import Dependency
dd = Dependency(sentences[0], dep[0], tokens[0])
rule = CleaningRules(dd)
for i in range(N):
print("~~~~~~~~~~~~\nSentence {}:".format(i))
print(sentences[i])
if len(tokens[i]) == 1:
print("Permanent: ", rule.apply("permanent"))
print("Mode: ", rule.apply("mode"))
print("Time: ", rule.apply("time"))
else:
print(rule.apply(None))
if i == N - 1: break
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("attentions", type=str, help="NPZ file with attentions")
ap.add_argument("tokens", type=str, help="Labels (tokens) separated by spaces")
ap.add_argument("conll", type=str, help="Conll file for head selection.")
ap.add_argument("json", type=str, help="Json file with head ensemble")
# other arguments
ap.add_argument("--report-result", type=str, default=None, help="File where to save the results.")
ap.add_argument("-s", "--sentences", nargs='*', type=int, default=None,
help="Only use the specified sentences; 0-based")
args = ap.parse_args()
dependency = Dependency(args.conll, args.tokens)
head_ensembles = dict()
ensembles_d2p = dict()
ensembles_p2d = dict()
depacc_d2p = dict()
depacc_p2d = dict()
with open(args.json, 'r') as inj:
head_ensembles = json.load(inj)
# considered_relations = (Dependency.LABEL_ALL,)
considered_relations = ('adj-modifier', 'adv-modifier', 'auxiliary', 'compound', 'conjunct', 'determiner',
'noun-modifier', 'num-modifier', 'object', 'other', 'subject', 'cc', 'case', 'mark')
def augmentation(self, fd: Dependency, gamma): # Dependencia (α ∪ γ, β ∪ γ) return Dependency(fd.alpha.union(gamma), fd.beta.union(gamma))
def transitivity(self, fd1: Dependency, fd2: Dependency):
if fd1.beta == fd2.alpha:
# Dependencia (α, γ)
return Dependency(fd1.alpha, fd2.beta)
from dependency import Dependency
#it's comes with python3.3
#já vem com o python3.3
import urllib.request
#you can get it in https://pypi.python.org/pypi/beautifulsoup4
#você pode baixar em https://pypi.python.org/pypi/beautifulsoup4
from bs4 import BeautifulSoup
#you can get it in https://pypi.python.org/pypi/pyutilib.subprocess/3.2
#você pode pegar isso em https://pypi.python.org/pypi/pyutilib.subprocess/3.2
import subprocess
#create a instance of class Dependency
#cria uma instância da classe Dependency
print("Entre com o link do arquivo do site Slackbuilds.org")
s = input()
file = Dependency(s)
#list all dependencies from file
#lista todas dependências do arquivo
dependencies = file.set_dep()
print("Procurando arquivo ... ")
print("Arquivo encontrado: ", file.name)
print(" ")
print("fazendo download build")
num = len(file.down_links) -1
f = subprocess.Popen(["wget", "-c",file.down_links[num]])
f.wait()
name = file.name + ".tar.gz"
print("extraindo")
def analyze(fname, cname='MyContract', funcname='foo()'):
slither = Slither(fname)
myContract = slither.get_contract_from_name(cname)
funcA = myContract.get_function_from_signature(funcname)
# Dependency Analysis
D = Dependency()
D.compute_contract(myContract, slither)
D.dependencies = funcA.context[D.KEY_NON_SSA]
# Refinement Analysis
R = Refinement()
R.compute_contract(myContract, slither, fname)
# Lambda Analysis
lambdas = get_lambda_analysis(fname, myContract, slither)
# For Guard Types, use Dependency Analysis to fetch all vars which affect
# the Guard (i.e. on which the guard depends)
guards = []
for var in R.types[R.Typ.GUARD]:
if var in D.dependencies:
guards += D.dependencies[var]
R.types[R.Typ.GUARD] += guards
# Remove temporary variables and ref vars from types
to_delete = {}
for typ in R.types:
to_delete[typ] = []
if typ != 6 and typ != 7:
for var in R.types[typ]:
if var.name.startswith("REF") or var.name.startswith("TMP"):
to_delete[typ].append(var)
for k,vals in to_delete.items():
for v in vals:
R.types[k].remove(v)
# Remove temporary variables and ref vars from dependencies
to_delete = []
for var in D.dependencies:
if var.name.startswith("REF") or var.name.startswith("TMP"):
to_delete.append(var)
else:
to_delete2 = []
for var2 in D.dependencies[var]:
if var2.name.startswith("REF") or var2.name.startswith("TMP"):
to_delete2.append(var2)
for x in to_delete2: D.dependencies[var].remove(x)
if len(D.dependencies[var]) == 0: to_delete.append(var)
for x in to_delete:
D.dependencies.pop(x, None)
# Fetch written and read types from dependencies
R.types[R.Typ.WRITTEN] += D.dependencies.keys()
R.types[R.Typ.READ] += [x for vals in D.dependencies.values() for x in vals]
# Anything that is an index or guard is also read
R.types[R.Typ.READ] += R.types[R.Typ.INDEX]
R.types[R.Typ.READ] += R.types[R.Typ.GUARD]
R.types[R.Typ.READ] += R.types[R.Typ.GUARDSTART]
R.types[R.Typ.READ] += R.types[R.Typ.GUARDEND]
# Reformat refinement type entries
R_types_formatted = {}
for typ, vrs in R.types.items():
# Special check for lower casing True and False constants
rhs = set(map(lambda v: v.lower() if v=="True" or v=="False" else v,
set(map(str, vrs))))
typ = typ.lower() if typ == "True" or typ == "False" else typ
R_types_formatted[typ] = rhs
R.types = R_types_formatted
# Reformat dependencies entries
dependencies_formatted = {}
for v, vrs in D.dependencies.items():
# Special check for lower casing True and False constants
lhs = str(v).lower() if str(v) == "True" or str(v) == "False" else str(v)
rhs = set(map(lambda v: v.lower() if v=="True" or v=="False" else v,
set(map(str, vrs))))
dependencies_formatted[lhs] = rhs
D.dependencies = dependencies_formatted
# Add lambdas to dependencies based on sub-parts
dependencies_lambdas = {}
for v, vrs in D.dependencies.items():
dependencies_lambdas[v] = vrs
for lam in lambdas:
lam_vrs = re.findall(r"[\w']+", lam[lam.index(":")+1:])
if any(map(lambda lv: lv in vrs, lam_vrs)):
dependencies_lambdas[v].add(lam)
D.dependencies = dependencies_lambdas
# # Transitive Closure of Dependencies
# D.dependencies = transitive_close(D.dependencies)
return D, R
def find_dependencies(root):
# sca files
xml_file_extensions = [
'wsdl', 'xsd', 'xml', 'xsl', 'bpel', 'componentType', 'decs', 'dvm',
'jpr', 'edl', 'jca', 'jws', 'config', 'monitor', 'mwp', 'rules', 'sch',
'schema', 'table', 'offlinedb'
]
# elements and attributes
element_filters = [
'reference', 'component', 'service', 'import', 'schemaImport',
'schema-import', 'schema'
]
attribute_filters = [
'location', 'wsdlLocation', 'schemaLocation', 'localPart', 'src'
]
# generate XPaths
element_xpath = './/*[' + ' or '.join([
"contains(name(.),'" + element_filter + "')"
for element_filter in element_filters
]) + ']'
attribute_xpath = './/@*[' + ' or '.join([
"contains(local-name(.),'" + attribute_filter + "')"
for attribute_filter in attribute_filters
]) + ']'
# loop over directories
dependencies = []
for directory_path, directory_names, file_names in os.walk(root):
# loop over files in the same directory
for file_name in file_names:
# get file extension
file_extension = ''
if len(file_name.split('.')) > 1:
file_extension = file_name.split('.')[-1]
# file extension filter
if file_extension in xml_file_extensions:
# parse xml file
file_path = os.path.join(directory_path, file_name)
relative_file_path = os.path.relpath(file_path, root)
xml_tree = xml_parser.parse(file_path)
# get elements
elements = xml_tree.xpath(element_xpath)
for element in elements:
# get element name
element_name = element.xpath('name(.)')
# remove namespace from element name if exists
if len(element_name.split(':')) > 1:
element_name = element_name.split(':')[-1]
# get attributes
paths = element.xpath(attribute_xpath)
for path in paths:
# get attribute name
attribute_name = element.xpath(
"local-name(.//@*[. = '" + path + "'])")
# remove namespace from attribute name if exists
if len(attribute_name.split(':')) > 1:
attribute_name = attribute_name.split(':')[-1]
# ignore wsdl service location
if not ((file_extension == 'wsdl') and
(element_name == 'service') and
(attribute_name == 'location')):
# create dependency
dependency = Dependency(file=relative_file_path,
element=element_name,
attribute=attribute_name,
path=path)
dependencies.append(dependency)
# return
return dependencies
projectName, projectId, projectVersion, projectUrl, projectTimePeriod, projectDependencies = projectEntry # check the project list for a project with a similar id # assume that we did not find it, yet found = False for sfproject in projectList: if projectId == sfproject.getProjectId(): found = True break if not found: # create a new project list entry sfproject = SfProject(projectName, projectId) # set up dependency object dependency = Dependency() dependency.setTimePeriodStart(projectTimePeriod[0]) dependency.setTimePeriodEnd(projectTimePeriod[1]) dependency.setVersion(projectVersion) dependency.setUrl(projectUrl) for dep in projectDependencies: dependency.addDependency(dep) sfproject.addProjectDependencyEntry(dependency) if not found: projectList.append(sfproject) testEnde1 = time.clock() #print "number of projects: ", len(projectList) #for sfproject in projectList:
def union(self, fd1: Dependency, fd2: Dependency):
if fd1.alpha == fd2.alpha:
# Dependencia (α, β ∪ γ)
return Dependency(fd1.alpha, fd1.beta.union(fd2.beta))
# from analysis import is_dependent, pprint_dependency, compute_dependency_contract
from dependency import Dependency
from refinement import Refinement
slither = Slither('data_dependency_simple_example.sol')
myContract = slither.get_contract_from_name('MyContract')
funcA = myContract.get_function_from_signature('foo()')
a = myContract.get_state_variable_from_name('a')
b = myContract.get_state_variable_from_name('b')
c = myContract.get_state_variable_from_name('c')
d = myContract.get_state_variable_from_name('d')
D = Dependency()
D.compute_contract(myContract, slither)
D.dependencies = funcA.context[D.KEY_NON_SSA]
R = Refinement()
R.compute_contract(myContract, slither)
guards = []
for var in R.types[R.Typ.GUARD]:
if var in D.dependencies:
guards += D.dependencies[var]
R.types[R.Typ.GUARD] += guards
for typ in R.types:
def decomposition(self, fd: Dependency, beta: set, gamma: set):
if fd.beta == beta.union(gamma):
# Dependencia (α, β) e (α, γ)
return [Dependency(fd.alpha, beta), Dependency(fd.alpha, gamma)]
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("conll", type=str, help="Conll file for head selection.")
ap.add_argument("-j", "--json", type=str, default=None, help="Json with the head ensembles")
ap.add_argument("-e", "--evaluate-only", action="store_true",
help="Whether to only evaluate (preomputed Json with head ensembles needed)")
# other arguments
ap.add_argument("--report-result", type=str, default=None, help="File where to save the results.")
ap.add_argument("-s", "--sentences", nargs='*', type=int, default=None,
help="Only use the specified sentences; 0-based")
args = ap.parse_args()
dependency_tree = Dependency(args.conll)
offset_modes = None
if args.evaluate_only:
if not args.json:
raise ValueError("JSON with offset modes required in evaluate only mode!")
with open(args.json, 'r') as inj:
offset_modes = json.load(inj)
else:
offset_modes = dependency_tree.calc_offset_modes()
results = defaultdict(dict)
clausal_relations = ('adj-modifier', 'adv-modifier', 'auxiliary', 'compound', 'conjunct', 'determiner',
from dependency import Dependency
from rules import Rules
from fplus import calc_fplus
# relação R (A, B, C, D)
r = [{"A"}, {"B"}, {"C"}, {"D"}]
# F dependencias
F = [
Dependency({"A"}, {"C"}),
Dependency({"D"}, {"B"}),
]
fplus = calc_fplus(r, F)
for dep in fplus:
print(dep)
print("\n{} dependencias encontradas para F+".format(len(fplus)))