def export_xmls_from_db():
"""
export all db jobs from db to db/xml_export directory
"""
now_iso = datetime.now().isoformat()
user = os.environ["USERNAME"]
domain = os.environ["USERDOMAIN"]
all_db_jobs = rms_db.all()
print(now_iso, user, domain)
for job in all_db_jobs:
job_name = job["<project_code>"] + "_" + job["<command>"]
start_time = now_iso.split("T")[0] + "T" + job[">start_time"]
cmd_str = " ".join(serdes(job=job)[1:])
# print(cmd_str)
xml_prms = {r"\{user_id\}": f"{domain}\\\\{user}",
r"\{author\}": f"{domain}\\\\{user}",
r"\{description\}": f"created: {now_iso}",
r"\{creation_date\}": now_iso,
r"\{start_time\}": start_time,
r"\{application\}": sys.executable.replace("\\", "\\\\"),
r"\{args\}": cmd_str.replace("\\", "\\\\"),
}
with open(rms_paths.xml_exp / "rms_xml_daily_template.xml", "r", encoding="utf-16le") as xml_template:
xml_content = xml_template.read()
for param in xml_prms:
re_prm = re.compile(param)
xml_content = re.sub(re_prm, xml_prms[param], xml_content)
with open(rms_paths.xml_exp / f"{job_name}.xml", "w", encoding="utf-16le") as rms_export:
rms_export.write(xml_content)
print(colorful.bold_green(f"exported: {job_name}"))
def test_step_match(sentence, expected_step, expected_arguments, steps):
sys.stdout.write('{0} STEP "{1}" SHOULD MATCH {2} '.format(
colorful.yellow('>>'), colorful.cyan(sentence),
colorful.cyan(expected_step)))
result = match_step(sentence, steps)
if not result:
output_failure(
None, ['Expected sentence didn\'t match any step implementation'])
return False
if expected_step != result.func.__name__:
output_failure(result.func, [
'Expected sentence matched {0} instead of {1}'.format(
result.func.__name__, expected_step)
])
return False
if expected_arguments:
arguments = merge_step_args(result)
expected_arguments = {
k: v
for expected_arguments in expected_arguments
for k, v in expected_arguments.items()
}
argument_errors = check_step_arguments(expected_arguments, arguments)
if argument_errors:
output_failure(result.func, argument_errors)
return False
print(u(colorful.bold_green(u'✔')))
return True
def update_graphs(project_code, html_path):
pd.set_option('display.width', 1800)
html_path = op.join(html_path, "{0}.html".format(project_code))
qc_path = op.dirname(op.abspath(__file__))
commands_dir = op.dirname(qc_path)
root_dir = op.dirname(commands_dir)
log_dir = op.join(root_dir, "logs")
csv_path = op.join(log_dir, project_code + ".csv")
csv = pd.read_csv(csv_path, delimiter=";")
csv.timeStamp = pd.to_datetime(csv.timeStamp)
output_file(html_path, mode="inline")
topics = {
"q_": "QC",
"l_": "LINKS",
"g_": "GROUPS",
"v_": "VIEWS",
"d_": "2D",
"s_": "STYLES",
"e_": "ELEMENTS",
"m_": "PROJECT_SQM",
}
graphs = graph(csv, project_code, topics)
save(column(graphs), validate=False)
print(colorful.bold_green(f" {html_path} updated successfully."))
def check_extensions():
newline()
subheader("Verifying extension programs...")
errors = []
programs = (
(
"jpegtran",
"Thumbor uses jpegtran for optimizing JPEG images. "
"For more information visit https://linux.die.net/man/1/jpegtran.",
),
(
"ffmpeg",
"Thumbor uses ffmpeg for rendering animated images as GIFV. "
"For more information visit https://www.ffmpeg.org/.",
),
(
"gifsicle",
"Thumbor uses gifsicle for better processing of GIF images. "
"For more information visit https://www.lcdf.org/gifsicle/.",
),
)
for program, error_message in programs:
path = which(program)
if path is None:
print(cf.bold_red("❎ %s is not installed." % program))
print(error_message)
newline()
errors.append(error_message)
else:
print(cf.bold_green("✅ %s is installed correctly." % program))
return errors
def check_modules():
newline()
subheader("Verifying libraries support...")
errors = []
modules = (
(
"pycurl",
"Thumbor works much better with PyCurl. For more information visit http://pycurl.io/.",
),
(
"cv2",
"Thumbor requires OpenCV for smart cropping. For more information check https://opencv.org/.",
),
(
"pyexiv2",
"Thumbor uses exiv2 for reading image metadata. For more information check https://python3-exiv2.readthedocs.io/en/latest/.",
),
(
"cairosvg",
"Thumbor uses CairoSVG for reading SVG files. For more information check https://cairosvg.org/.",
),
)
for module, error_message in modules:
try:
import_module(module) # NOQA
print(cf.bold_green("✅ %s is installed correctly." % module))
except ImportError as error:
print(cf.bold_red("❎ %s is not installed." % module))
print(error_message)
newline()
errors.append("%s - %s" % (str(error), error_message))
return errors
def test_step_not_match(sentence, expected_not_matching_step, steps):
step_to_print = (
colorful.cyan(expected_not_matching_step)
if expected_not_matching_step
else "ANY"
)
sys.stdout.write(
'{0} STEP "{1}" SHOULD NOT MATCH {2} '.format(
colorful.yellow(">>"), colorful.cyan(sentence), step_to_print
)
)
result = match_step(sentence, steps)
if result:
if (
not expected_not_matching_step
or result.func.__name__ == expected_not_matching_step
):
output_failure(
None,
[
"Expected sentence did match {0} but it shouldn't".format(
expected_not_matching_step
)
],
)
return False
print(u(colorful.bold_green("✔")))
return True
def check_model_path(job_id):
job_id = int(job_id)
job = rms_db.get(doc_id=job_id)
model_path = pathlib.Path(job["<full_model_path>"])
if model_path.exists():
print(colorful.bold_green(f" model found at: {model_path}"))
return True
else:
print(colorful.bold_red(f" could not find model at: {model_path}"))
def test_step_matches(match_config, steps):
"""
Test if the given match config matches the actual
matched step implementations.
"""
failed = 0
passed = 0
for item in match_config:
validate_config_item(item)
sentence = item['sentence']
expected_step = item['should_match']
sys.stdout.write('{0} STEP "{1}" SHOULD MATCH {2} '.format(
colorful.yellow('>>'), colorful.cyan(sentence),
colorful.cyan(expected_step)))
result = match_step(item['sentence'], steps)
if not result:
output_failure(
None,
['Expected sentence didn\'t match any step implementation'])
failed += 1
continue
if expected_step != result.func.__name__:
output_failure(result.func, [
'Expected sentence matched {0} instead of {1}'.format(
result.func.__name__, expected_step)
])
failed += 1
continue
expected_arguments = item.get('with_arguments')
if expected_arguments:
arguments = merge_step_args(result)
expected_arguments = {
k: v
for expected_arguments in expected_arguments
for k, v in expected_arguments.items()
}
argument_errors = check_step_arguments(expected_arguments,
arguments)
if argument_errors:
output_failure(result.func, argument_errors)
failed += 1
continue
# check if arguments match
print(u(colorful.bold_green(u'✔')))
passed += 1
return failed, passed
def log(msg: str, tp: int = 1, name: str = "NNG One", force: bool = False):
self = Logger
try:
debug = NNGRawSaves().debug
except Exception:
debug = self.level.none
if tp == self.level.success:
print(cf.bold_green(f"[{name}] {msg}"))
elif tp == self.level.warn:
print(cf.bold_blue(f"[{name}] {msg}"))
elif tp == self.level.tagged_success:
print(cf.bold_green(f"[{name}] {Globals.tag} | {msg}"))
elif (tp == self.level.error
and debug >= self.config_logging_level.log_errors) or force:
print(cf.bold_red(f"[{name}] [ERROR] {msg}"))
elif (tp == self.level.debug
and debug >= self.config_logging_level.log_debug) or force:
print(cf.bold_purple(f"[{name}] [DEBUG] {msg}"))
elif tp == self.level.hell and debug >= self.config_logging_level.log_hell:
print(cf.bold_grey(f"{name} [HELL] {msg}"))
def do_snapshot(self, line):
""" Load/Save a snapshot """
l = line.split()
usage = "snapshot load|save ini|file"
if len(l) != 3:
print usage
return
if l[0] == "load":
if l[1] == "ini":
bin_type = armcpu_const.INI_BIN
elif l[1] == "file":
bin_type = armcpu_const.SNAPSHOT_BIN
else:
print usage
return
else:
print usage
return
self.arm_dbg = armcpu.ArmCPU(l[2], bin_type)
print colorful.bold_green("Loaded snapshot: {}".format(l[2]))
def current_lesson(lesson):
print("---")
print(colorful.bold_green("Weekday: ") + days[weekday])
print(colorful.bold_green("Current time: ") + str(now.strftime("%H:%M")))
print("---")
print(colorful.bold_green("Lesson: ") + lesson["name"])
print(colorful.bold_green("Teacher: ") + lesson["teacher"])
print(colorful.bold_green("Room: ") + lesson["room"])
print(
colorful.bold_green("Starttime: ") + str(lesson["starttimehour"]) +
":" + str(lesson["starttimeminute"]))
print(
colorful.bold_green("Endtime: ") + str(lesson["endtimehour"]) + ":" +
str(lesson["endtimeminute"]))
print(colorful.bold_green("Time till break: ") + str(endtime - now))
print("---")
def check_filters():
newline()
subheader("Verifying thumbor filters...")
errors = []
for filter_name in BUILTIN_FILTERS:
try:
import_module(filter_name)
print(cf.bold_green("✅ %s" % filter_name))
except ImportError as error:
print(cf.bold_red("❎ %s" % filter_name))
errors.append(error)
return errors
def check_compiled_extensions():
newline()
subheader("Verifying thumbor compiled extensions...")
errors = []
for extension in BUILTIN_EXTENSIONS:
ext_name = extension.replace("thumbor.ext.filters.", "")
try:
import_module(extension)
print(cf.bold_green("✅ %s" % ext_name))
except ImportError as error:
print(cf.bold_red("❎ %s" % ext_name))
errors.append(error)
return errors
def run(self):
self.initialize_params()
self.save_model(0)
while self.epoch <= self.n_epochs:
self.train_epoch()
self.test()
self.epoch += 1
print(
colorful.bold_green(
f'\n====> Best Epoch: {self.best_epoch}').styled_string)
best_checkpoint_path = os.path.join(self.save_dir,
str(self.best_epoch) + '.pkl')
self.importance_sample(best_checkpoint_path)
self.writer.close()
def captcha_handler(self, captcha):
webbrowser.open_new(captcha.get_url())
key = input(
cf.bold_green(
f"[NNG Framework] [Captcha Handler] Введите код каптчи: {captcha.get_url()}: "
)
)
try:
return captcha.try_again(key)
except vk_api.exceptions.Captcha as capt:
lg.log(
"[Captcha Handler] Неверный код каптчи! Попробуйте снова.",
3,
self.name,
)
self.captcha_handler(capt)
def main():
os.environ['PYTHONUNBUFFERED'] = '1'
process = subprocess.Popen(['python3', 'infiloop.py'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True,
bufsize=1)
while process.poll() is None:
line = process.stdout.readline()
if line:
if '-=>' in line: # Assume sophisticated pattern
print(colorful.bold_green(line.strip()))
else:
print(line.strip())
line = process.stderr.readline()
if line:
print(colorful.red(line.strip()))
def import_xmls_into_db():
"""
import all xml rms tasks from db/xml_import directory into db
"""
found_rms_task_xml = False
for entry in os.scandir(rms_paths.xml_imp):
if not entry.is_file():
continue
if not entry.name.endswith(".xml"):
continue
with open(rms_paths.xml_imp / entry.name, "r",
encoding="utf-16le") as xml_task:
xml_content = xml_task.read()
re_process_model = re.compile("process_model.py")
is_rms_task = re.findall(re_process_model, xml_content)
if is_rms_task:
print(colorful.bold_green(f" processing xml: {entry.name}"))
found_rms_task_xml = True
cmd_tokens = {"args": {}, "opts": []}
re_args = re.compile("Arguments>(.+)</Arguments")
re_opts = re.compile("(--.+)", )
re_start = re.compile("StartBoundary>.+T(.+)</StartBoundary")
arguments = re.findall(re_args, xml_content)
options = re.findall(re_opts, arguments[0])[0]
cmd_args = arguments[0].split("--")[0].split()
cmd_tokens["args"]["<command>"] = cmd_args[1]
cmd_tokens["args"]['<project_code>'] = cmd_args[2]
cmd_tokens["args"]["<full_model_path>"] = cmd_args[3]
cmd_tokens["args"][">start_time"] = re.findall(
re_start, xml_content)[0]
cmd_tokens["opts"] = [
"--" + tok.strip() for tok in options.split("--") if tok
]
# print(f" found {cmd_tokens}")
db_job_dict = serdes(
cmd_tokens=cmd_tokens) # {"args": args, "opts": options})
pprint(db_job_dict)
rms_db.upsert(
db_job_dict, (Query()["<project_code>"]
== cmd_tokens["args"]['<project_code>']) &
(Query()["<command>"] == cmd_tokens["args"]["<command>"]))
print(" added/updated in db.")
if not found_rms_task_xml:
print(
colorful.bold_red(
f" could not find rms task xml in: {rms_paths.db}"))
def test_problem(problem, log=True):
if log: print(f"👷 Testing {problem}...")
lang = language_detector.determine_language(problem)
lang_config = language_detector.get_config(lang)
if log:
print(f"👷 Language = {lang_config.kattis_name} {lang_config.emoji}\n")
inputs = glob.glob(f"./{problem}/*.in")
count = 0
failed = False
for input in inputs:
count += 1
if log:
print(f"🔎 Test number {count}:")
input_file = open(input, "rb")
input_content = input_file.read()
program_path = f"./{problem}/solution.{lang_config.file_extension}"
output_string = lang_config.run_program(program_path, input_content)
answer = input.replace('.in', '.ans')
answer_file = open(answer, "r")
answer_content = answer_file.read()
if output_string.replace("\r\n", "\n") != answer_content.replace(
"\r\n", "\n"):
if log:
print(cf.bold_red("❌ Failed..."))
print("__________INPUT____________")
print(input_content.decode('utf-8'))
print("__________INPUT____________")
print(cf.red("__________OUTPUT___________"))
print(cf.red(output_string))
print(cf.red("__________OUTPUT___________"))
print("__________EXPECTED_________")
print(answer_content)
print("__________EXPECTED_________")
failed = True
elif log:
print(cf.bold_green("✅ Test succesful!\n"))
return not failed
def test_step_not_match(sentence, expected_not_matching_step, steps):
step_to_print = colorful.cyan(
expected_not_matching_step) if expected_not_matching_step else 'ANY'
sys.stdout.write('{0} STEP "{1}" SHOULD NOT MATCH {2} '.format(
colorful.yellow('>>'), colorful.cyan(sentence), step_to_print))
result = match_step(sentence, steps)
if result:
if not expected_not_matching_step or result.func.__name__ == expected_not_matching_step:
output_failure(None, [
'Expected sentence did match {0} but it shouldn\'t'.format(
expected_not_matching_step)
])
return False
print(u(colorful.bold_green(u'✔')))
return True
def main():
"""Converts a given url with the specified arguments."""
options = get_options()
cf.use_style("solarized")
if options["nocolor"]:
cf.disable()
newline()
header("Thumbor v%s (of %s)" % (__version__, __release_date__))
newline()
print(
"Thumbor doctor will analyze your install and verify if everything is working as expected."
)
errors = check_modules()
errors += check_compiled_extensions()
errors += check_filters()
errors += check_extensions()
newline()
if errors:
print(
cf.bold_red(
"😞 Oh no! We found some things that could improve... 😞"))
newline()
print("\n".join(["* %s" % str(err) for err in errors]))
newline()
newline()
print(
cf.cyan(
"If you don't know how to fix them, please open an issue with thumbor."
))
print(
cf.cyan(
"Don't forget to copy this log and add it to the description of your issue."
))
print("Open an issue at https://github.com/thumbor/thumbor/issues/new")
sys.exit(1)
return
print(cf.bold_green("🎉 Congratulations! No errors found! 🎉"))
def table_show_registers(regs_list):
"""Show registers in a table."""
table_data = [[r for r in regs_list[:4]], [r for r in regs_list[4:8]],
[r for r in regs_list[8:12]], [r for r in regs_list[12:16]]]
table_title = colorful.bold_green("ARM Registers")
table_instance = SingleTable(table_data, table_title)
table_instance.inner_heading_row_border = False
table_instance.inner_row_border = True
table_instance.justify_columns = {
0: 'center',
1: 'center',
2: 'center',
3: 'center'
}
print ""
print table_instance.table
print ""
def test_step_not_match(sentence, expected_not_matching_step, steps):
step_to_print = (colorful.cyan(expected_not_matching_step)
if expected_not_matching_step else "ANY")
sys.stdout.write('{0} STEP "{1}" SHOULD NOT MATCH {2} '.format(
colorful.yellow(">>"), colorful.cyan(sentence), step_to_print))
result = match_step(sentence, steps)
if result:
if (not expected_not_matching_step
or result.func.__name__ == expected_not_matching_step):
output_failure(
None,
[
"Expected sentence did match {0} but it shouldn't".format(
expected_not_matching_step)
],
)
return False
print(u(colorful.bold_green("✔")))
return True
def train_epoch(self):
# set to train mode
self.model.train()
train_loss = 0
train_loader = self.dataset.train_loader
epoch_start_time = time.time()
for batch_idx, (data, _) in enumerate(train_loader):
data = data.cuda()
data = self.dataset.preprocess(data)
if batch_idx == 0 and self.epoch == 1:
self.model.write_summary(data, self.writer, 0)
self.optimizer.zero_grad()
loss = self.model(data)
loss.backward()
train_loss += loss.item() * len(data)
assert not np.isnan(loss.item())
self.optimizer.step()
if self.log_interval is not None:
if batch_idx % self.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.
format(self.epoch, batch_idx * len(data),
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item()))
duration = time.time() - epoch_start_time
epoch_loss = train_loss / len(train_loader.dataset)
print(
colorful.bold_green(
'====> Epoch: {} Average loss: {:.4f} Duration(sec): {}'.
format(self.epoch, epoch_loss, duration)).styled_string)
self.writer.add_scalar('train/loss', epoch_loss, self.epoch)
self.model.write_summary(data, self.writer, self.epoch)
def purge_old(directory: Path, extension: str, threshold_age_days=60):
"""
deletes all files with specified extension older than the threshold_age_days
:param directory: path to search
:param extension: file extension to filter by
:param threshold_age_days: max file age date modified
:return:
"""
found = 0
now = time.time()
for node in directory.iterdir():
if node.suffix == f".{extension}":
file_modified = node.stat().st_mtime
if (now - file_modified) // (24 * 3600) >= threshold_age_days:
node.unlink()
found += 1
if found > 0:
print(
col.bold_green(
f" cleaned-up {found} {extension} files older than: {threshold_age_days} in {directory}"
))
def test_step_match(sentence, expected_step, expected_arguments, steps):
sys.stdout.write(
'{0} STEP "{1}" SHOULD MATCH {2} '.format(
colorful.yellow(">>"), colorful.cyan(sentence), colorful.cyan(expected_step)
)
)
result = match_step(sentence, steps)
if not result:
output_failure(None, ["Expected sentence didn't match any step implementation"])
return False
if expected_step != result.func.__name__:
output_failure(
result.func,
[
"Expected sentence matched {0} instead of {1}".format(
result.func.__name__, expected_step
)
],
)
return False
if expected_arguments:
arguments = merge_step_args(result)
expected_arguments = {
k: v
for expected_arguments in expected_arguments
for k, v in expected_arguments.items()
}
argument_errors = check_step_arguments(expected_arguments, arguments)
if argument_errors:
output_failure(result.func, argument_errors)
return False
print(u(colorful.bold_green("✔")))
return True
def importance_sample(self, checkpoint):
self.model.load_state_dict(torch.load(checkpoint))
self.model.eval()
test_loglikelihood = 0
test_loader = self.dataset.test_loader
with torch.no_grad():
for i, (data, _) in enumerate(test_loader):
data = data.cuda()
data = self.dataset.preprocess(data)
if models.n_importance_sample > 1000:
for data_ in data:
test_loglikelihood += self.model.importance_sample(
data_.unsqueeze(0))
else:
test_loglikelihood += self.model.importance_sample(data)
test_loglikelihood /= len(test_loader.dataset)
print(
colorful.bold_green('====> Test set loglikelihood: {:.4f}'.format(
test_loglikelihood)).styled_string)
self.writer.add_scalar('test/loglikelihood', test_loglikelihood,
self.best_epoch)
def get_int_choose(
self,
show_string: str,
maxValue: int,
minValue: int = 1,
custom_error: str = None,
):
value = str(input(cf.bold_green(f"{show_string}: ")))
if not value.isdigit():
lg.log(custom_error or "Введёная строка — не число", lg.level.warn)
return self.get_int_choose(
show_string, maxValue, minValue=minValue, custom_error=custom_error
)
if maxValue < int(value):
lg.log(custom_error or f"Максимум этого числа — {maxValue}", lg.level.warn)
return self.get_int_choose(
show_string, maxValue, minValue=minValue, custom_error=custom_error
)
if int(value) < minValue:
lg.log(custom_error or f"Минимум этого числа — {minValue}", lg.level.warn)
return self.get_int_choose(
show_string, maxValue, minValue=minValue, custom_error=custom_error
)
return int(value)
def test_step_matches_configs(
match_config_files, basedirs, cover_min_percentage=None, cover_show_missing=False
):
"""
Test if the given match config files matches the actual
matched step implementations.
"""
if cover_min_percentage is not None and float(cover_min_percentage) > 100:
sys.stderr.write(
str(
colorful.magenta(
"You are a little cocky to think you can reach a minimum coverage of {0:.2f}%\n".format(
float(cover_min_percentage)
)
)
)
)
return 3
# load user's custom python files
for basedir in basedirs:
load_modules(basedir)
steps = StepRegistry().steps
if not steps:
sys.stderr.write(
str(
colorful.magenta(
"No step implementations found in {0}, thus doesn't make sense to continue".format(
basedirs
)
)
)
)
return 4
failed = 0
passed = 0
covered_steps = set()
for match_config_file in match_config_files:
# load the given match config file
with codecs.open(match_config_file, "r", "utf-8") as f:
match_config = yaml.safe_load(f)
if not match_config:
print(
colorful.magenta(
"No sentences found in {0} to test against".format(
match_config_file
)
)
)
return 5
print(
colorful.yellow(
"Testing sentences from {0}:".format(
colorful.bold_yellow(match_config_file)
)
)
)
failed_sentences, passed_senteces = test_step_matches(match_config, steps)
failed += failed_sentences
passed += passed_senteces
covered_steps = covered_steps.union(
x["should_match"] for x in match_config if "should_match" in x
)
# newline
sys.stdout.write("\n")
report = colorful.bold_white("{0} sentences (".format(failed + passed))
if passed > 0:
report += colorful.bold_green("{0} passed".format(passed))
if passed > 0 and failed > 0:
report += colorful.bold_white(", ")
if failed > 0:
report += colorful.bold_red("{0} failed".format(failed))
report += colorful.bold_white(")")
print(report)
step_coverage = 100.0 / len(steps) * len(covered_steps)
coverage_report = colorful.bold_white(
"Covered {0} of {1} step implementations".format(len(covered_steps), len(steps))
)
ret = 0 if failed == 0 else 1
if cover_min_percentage:
coverage_color = (
colorful.bold_green
if step_coverage >= float(cover_min_percentage)
else colorful.bold_red
)
coverage_report += colorful.bold_white(" (coverage: ")
coverage_report += coverage_color("{0:.2f}%".format(step_coverage))
if float(cover_min_percentage) > step_coverage:
coverage_report += colorful.bold_white(
", expected a minimum of {0}".format(
colorful.bold_green(cover_min_percentage + "%")
)
)
if failed == 0:
ret = 2
# if tests have passed and coverage is too low we fail with exit code 2
coverage_report += colorful.bold_white(")")
print(coverage_report)
if cover_show_missing:
missing_steps = get_missing_steps(steps, covered_steps)
if missing_steps:
missing_step_report = colorful.bold_yellow("Missing steps:\n")
for step in missing_steps:
missing_step_report += "- {0} at ".format(colorful.cyan(step[0]))
missing_step_report += colorful.cyan(step[1]) + "\n"
sys.stdout.write(missing_step_report)
return ret
def test_step_matches_configs(match_config_files,
basedirs,
cover_min_percentage=None,
cover_show_missing=False):
"""
Test if the given match config files matches the actual
matched step implementations.
"""
if cover_min_percentage is not None and float(cover_min_percentage) > 100:
sys.stderr.write(
str(
colorful.magenta(
'You are a little cocky to think you can reach a minimum coverage of {0:.2f}%\n'
.format(float(cover_min_percentage)))))
return 3
# load user's custom python files
for basedir in basedirs:
load_modules(basedir)
steps = StepRegistry().steps
if not steps:
sys.stderr.write(
str(
colorful.magenta(
'No step implementations found in {0}, thus doesn\'t make sense to continue'
.format(basedirs))))
return 4
failed = 0
passed = 0
covered_steps = set()
for match_config_file in match_config_files:
# load the given match config file
with codecs.open(match_config_file, "r", "utf-8") as f:
match_config = yaml.safe_load(f)
if not match_config:
print(
colorful.magenta(
'No sentences found in {0} to test against'.format(
match_config_file)))
return 5
print(
colorful.yellow('Testing sentences from {0}:'.format(
colorful.bold_yellow(match_config_file))))
failed_sentences, passed_senteces = test_step_matches(
match_config, steps)
failed += failed_sentences
passed += passed_senteces
covered_steps = covered_steps.union(x['should_match']
for x in match_config
if 'should_match' in x)
# newline
sys.stdout.write('\n')
report = colorful.bold_white('{0} sentences ('.format(failed + passed))
if passed > 0:
report += colorful.bold_green('{0} passed'.format(passed))
if passed > 0 and failed > 0:
report += colorful.bold_white(', ')
if failed > 0:
report += colorful.bold_red('{0} failed'.format(failed))
report += colorful.bold_white(')')
print(report)
step_coverage = 100.0 / len(steps) * len(covered_steps)
coverage_report = colorful.bold_white(
'Covered {0} of {1} step implementations'.format(
len(covered_steps), len(steps)))
ret = 0 if failed == 0 else 1
if cover_min_percentage:
coverage_color = colorful.bold_green if step_coverage >= float(
cover_min_percentage) else colorful.bold_red
coverage_report += colorful.bold_white(' (coverage: ')
coverage_report += coverage_color('{0:.2f}%'.format(step_coverage))
if float(cover_min_percentage) > step_coverage:
coverage_report += colorful.bold_white(
', expected a minimum of {0}'.format(
colorful.bold_green(cover_min_percentage + '%')))
if failed == 0:
ret = 2
# if tests have passed and coverage is too low we fail with exit code 2
coverage_report += colorful.bold_white(')')
print(coverage_report)
if cover_show_missing:
missing_steps = get_missing_steps(steps, covered_steps)
if missing_steps:
missing_step_report = colorful.bold_yellow('Missing steps:\n')
for step in missing_steps:
missing_step_report += '- {0} at '.format(
colorful.cyan(step[0]))
missing_step_report += colorful.cyan(step[1]) + '\n'
sys.stdout.write(missing_step_report)
return ret
def console_write(self, features, marker):
"""
Writes the endreport for all features
:param list features: all features
"""
stats = {
"features": {
"amount": 0,
"passed": 0,
"failed": 0,
"skipped": 0,
"untested": 0,
"pending": 0,
},
"scenarios": {
"amount": 0,
"passed": 0,
"failed": 0,
"skipped": 0,
"untested": 0,
"pending": 0,
},
"steps": {
"amount": 0,
"passed": 0,
"failed": 0,
"skipped": 0,
"untested": 0,
"pending": 0,
},
}
pending_steps = []
duration = timedelta()
for feature in features:
if not feature.has_to_run(world.config.scenarios):
continue
stats["features"]["amount"] += 1
stats["features"][feature.state] += 1
if feature.state in [Step.State.PASSED, Step.State.FAILED]:
duration += feature.duration
for scenario in feature.all_scenarios:
if not scenario.has_to_run(world.config.scenarios):
continue
if isinstance(scenario, ScenarioOutline): # skip ScenarioOutlines
continue
if isinstance(scenario, ScenarioLoop): # skip ScenarioLoop
continue
stats["scenarios"]["amount"] += 1
stats["scenarios"][scenario.state] += 1
for step in scenario.steps:
stats["steps"]["amount"] += 1
stats["steps"][step.state] += 1
if step.state == Step.State.PENDING:
pending_steps.append(step)
colored_closing_paren = colorful.bold_white(")")
colored_comma = colorful.bold_white(", ")
passed_word = colorful.bold_green("{0} passed")
failed_word = colorful.bold_red("{0} failed")
skipped_word = colorful.cyan("{0} skipped")
pending_word = colorful.bold_yellow("{0} pending")
output = colorful.bold_white(
"{0} features (".format(stats["features"]["amount"])
)
output += passed_word.format(stats["features"]["passed"])
if stats["features"]["failed"]:
output += colored_comma + failed_word.format(stats["features"]["failed"])
if stats["features"]["skipped"]:
output += colored_comma + skipped_word.format(stats["features"]["skipped"])
if stats["features"]["pending"]:
output += colored_comma + pending_word.format(stats["features"]["pending"])
output += colored_closing_paren
output += "\n"
output += colorful.bold_white(
"{} scenarios (".format(stats["scenarios"]["amount"])
)
output += passed_word.format(stats["scenarios"]["passed"])
if stats["scenarios"]["failed"]:
output += colored_comma + failed_word.format(stats["scenarios"]["failed"])
if stats["scenarios"]["skipped"]:
output += colored_comma + skipped_word.format(stats["scenarios"]["skipped"])
if stats["scenarios"]["pending"]:
output += colored_comma + pending_word.format(stats["scenarios"]["pending"])
output += colored_closing_paren
output += "\n"
output += colorful.bold_white("{} steps (".format(stats["steps"]["amount"]))
output += passed_word.format(stats["steps"]["passed"])
if stats["steps"]["failed"]:
output += colored_comma + failed_word.format(stats["steps"]["failed"])
if stats["steps"]["skipped"]:
output += colored_comma + skipped_word.format(stats["steps"]["skipped"])
if stats["steps"]["pending"]:
output += colored_comma + pending_word.format(stats["steps"]["pending"])
output += colored_closing_paren
if pending_steps:
sr = StepRegistry()
pending_step_implementations = make_unique_obj_list(
pending_steps, lambda x: x.definition_func
)
output += colorful.white(
"\nYou have {0} pending step implementation{1} affecting {2} step{3}:\n {4}\n\nNote: this could be the reason for some failing subsequent steps".format(
len(pending_step_implementations),
"s" if len(pending_step_implementations) is not 1 else "",
len(pending_steps),
"s" if len(pending_steps) is not 1 else "",
"\n ".join(
[
"- '{0}' @ {1}".format(
sr.get_pattern(s.definition_func),
get_func_code(s.definition_func).co_filename,
)
for s in pending_step_implementations
]
),
)
)
output += "\n"
if world.config.wip:
if stats["scenarios"]["passed"] > 0:
output += colorful.red(
"\nThe --wip switch was used, so I didn't expect anything to pass. These scenarios passed:\n"
)
has_passed_scenarios = False
for feature in features:
passed_scenarios = list(
filter(
lambda s: s.state == Step.State.PASSED,
feature.all_scenarios,
)
)
for scenario in passed_scenarios:
output += colorful.red(
"\n - {}: {}".format(feature.path, scenario.sentence)
)
has_passed_scenarios = True
if has_passed_scenarios:
output += "\n"
else:
output += colorful.green(
"\nThe --wip switch was used, so the failures were expected. All is good.\n"
)
output += colorful.cyan(
"Run {0} finished within {1}".format(
marker, humanize.naturaldelta(duration)
)
)
write(output)
def main():
# Argument passing/parsing
args, model_args = config_utils.initialize_argparser(
MODELS, _command_args, custom_argparsers.DialogArgumentParser)
hparams, hparams_dict = config_utils.create_or_load_hparams(
args, model_args, args.cfg)
pprint(hparams_dict)
# Set environment variables & gpus
set_logger()
set_gpus(hparams.gpus)
set_tcmalloc()
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus, 'GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# Set random seed
tf.random.set_seed(hparams.random_seed)
np.random.seed(hparams.random_seed)
random.seed(hparams.random_seed)
# For multi-gpu
if hparams.num_gpus > 1:
mirrored_strategy = tf.distribute.MirroredStrategy() # NCCL will be used as default
else:
mirrored_strategy = None
# Download BERT pretrained model
if not os.path.exists(hparams.bert_dir):
os.makedirs(hparams.bert_dir)
fname = 'uncased_L-12_H-768_A-12.zip'
gd_id = '17rfV9CleFBwwfS7m5Yd72vvxdPLWBHl6'
download_from_google_drive(gd_id, os.path.join(hparams.bert_dir, fname))
unzip(hparams.bert_dir, fname)
# Make dataset reader
os.makedirs(hparams.cache_dir, exist_ok=True)
if hparams.data_name == "wizard_of_wikipedia":
reader_cls = WowDatasetReader
elif hparams.data_name == "holle":
reader_cls = HolleDatasetReader
else:
raise ValueError("data_name must be one of 'wizard_of_wikipedia' and 'holle'")
reader = reader_cls(
hparams.batch_size, hparams.num_epochs,
buffer_size=hparams.buffer_size,
bucket_width=hparams.bucket_width,
max_length=hparams.max_length,
max_episode_length=hparams.max_episode_length,
max_knowledge=hparams.max_knowledge,
knowledge_truncate=hparams.knowledge_truncate,
cache_dir=hparams.cache_dir,
bert_dir=hparams.bert_dir,
)
train_dataset, iters_in_train = reader.read('train', mirrored_strategy)
test_dataset, iters_in_test = reader.read('test', mirrored_strategy)
if hparams.data_name == 'wizard_of_wikipedia':
unseen_dataset, iters_in_unseen = reader.read('test_unseen', mirrored_strategy)
vocabulary = reader.vocabulary
# Build model & optimizer & trainer
if mirrored_strategy:
with mirrored_strategy.scope():
model = MODELS[hparams.model](hparams, vocabulary)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.init_lr,
clipnorm=hparams.clipnorm)
else:
model = MODELS[hparams.model](hparams, vocabulary)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.init_lr,
clipnorm=hparams.clipnorm)
trainer = Trainer(model, optimizer, mirrored_strategy,
hparams.enable_function,
WowDatasetReader.remove_pad)
# misc (tensorboard, checkpoints)
file_writer = tf.summary.create_file_writer(hparams.checkpoint_dir)
file_writer.set_as_default()
global_step = tf.compat.v1.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=global_step)
checkpoint_manager = tf.train.CheckpointManager(checkpoint,
directory=hparams.checkpoint_dir,
max_to_keep=hparams.max_to_keep)
checkpoint_tracker = CheckpointTracker(
hparams.checkpoint_dir, max_to_keep=BEST_N_CHECKPOINTS)
# Main loop!
train_dataset_iter = iter(train_dataset)
for epoch in range(hparams.num_epochs):
print(hparams.checkpoint_dir)
base_description = f"(Train) Epoch {epoch}, GPU {hparams.gpus}"
train_tqdm = trange(iters_in_train, ncols=120, desc=base_description)
for current_step in train_tqdm:
example = next(train_dataset_iter)
global_step.assign_add(1)
_global_step = int(global_step)
# Train
output_dict = trainer.train_step(example)
# Print model
if _global_step == 1:
model.print_model()
loss_str = str(output_dict['loss'].numpy())
train_tqdm.set_description(f"{base_description}, Loss {loss_str}")
with file_writer.as_default():
if _global_step % int(hparams.logging_step) == 0:
tf.summary.histogram('train/vocab', output_dict['sample_ids'], step=_global_step)
tf.summary.scalar('train/loss', output_dict['loss'], step=_global_step)
tf.summary.scalar('train/gen_loss', output_dict['gen_loss'], step=_global_step)
tf.summary.scalar('train/knowledge_loss', output_dict['knowledge_loss'], step=_global_step)
tf.summary.scalar('train/kl_loss', output_dict['kl_loss'], step=_global_step)
# Test
if _global_step % int(iters_in_train * hparams.evaluation_epoch) == 0:
checkpoint_manager.save(global_step)
test_loop_outputs = trainer.test_loop(test_dataset, iters_in_test, epoch, 'seen')
if hparams.data_name == 'wizard_of_wikipedia':
unseen_loop_outputs = trainer.test_loop(unseen_dataset, iters_in_unseen, epoch, 'unseen')
test_summaries, log_dict = run_wow_evaluation(
test_loop_outputs, hparams.checkpoint_dir, 'seen')
if hparams.data_name == 'wizard_of_wikipedia':
unseen_summaries, unseen_log_dict = run_wow_evaluation(
unseen_loop_outputs, hparams.checkpoint_dir, 'unseen')
# Logging
tqdm.write(colorful.bold_green("seen").styled_string)
tqdm.write(colorful.bold_red(pformat(log_dict)).styled_string)
if hparams.data_name == 'wizard_of_wikipedia':
tqdm.write(colorful.bold_green("unseen").styled_string)
tqdm.write(colorful.bold_red(pformat(unseen_log_dict)).styled_string)
with file_writer.as_default():
for family, test_summary in test_summaries.items():
for key, value in test_summary.items():
tf.summary.scalar(f'{family}/{key}', value, step=_global_step)
if hparams.data_name == 'wizard_of_wikipedia':
for family, unseen_summary in unseen_summaries.items():
for key, value in unseen_summary.items():
tf.summary.scalar(f'{family}/{key}', value, step=_global_step)
if hparams.keep_best_checkpoint:
current_score = log_dict["rouge1"]
checkpoint_tracker.update(current_score, _global_step)
class ArmjitsuCmd(Cmd):
"""Command dispatch loop"""
prompt = colorful.bold_green("(armjitsu) ")
ruler = "-"
debug = True
def __init__(self):
Cmd.__init__(self)
self.bin_loaded = False
self.bin_running = False
self.arm_dbg = None
if DEBUG_MODE:
import ipdb; ipdb.set_trace()
@options([make_option('-l', '--list', action="store_true", help="Show supported binary formats."),
make_option('-r', '--raw', action="store_true", help="Load ARM RAW/Shellcode from file."),
make_option('-e', '--elf', action="store_true", help="Load ARM ELF binary from file.")
])
def do_file(self, args, opts=None):
"""
Load an ARM binary file for emulation and debugging.
To list ARMjitsu supported binary formats invoke:
(armjitsu) file --list
"""
BIN_TYPE = armcpu_const.RAW_BIN
if opts.raw:
BIN_TYPE = armcpu_const.RAW_BIN
elif opts.elf:
BIN_TYPE = armcpu_const.ELF_BIN
line = args
if not line:
print colorful.yellow("Supply the filename of the binary you wish to load please.")
return None
file_name = line if is_file(line) else None
if not file_name or not BIN_TYPE:
print colorful.yellow("Error with supplied filename.")
return False
self.arm_dbg = armcpu.ArmCPU(file_name, BIN_TYPE)
self.bin_loaded = True
print colorful.base1("Loaded binary file: {}".format(file_name))
# Synonyms for do_file
do_load = do_file
# REMOVE AFTER DEV
def do_testing(self, line):
self.arm_dbg = armcpu.ArmCPU("armraw.bin", armcpu_const.RAW_BIN)
self.bin_loaded = True
print colorful.bold_red("Developer testing mode! armraw.bin loaded!")
print colorful.base1("Loaded binary file: {}".format(file_name))
# Synonyms for do_file
do_load = do_file
# REMOVE AFTER DEV
def do_testing(self, line):
self.arm_dbg = armcpu.ArmCPU("armraw.bin", armcpu_const.RAW_BIN)
self.bin_loaded = True
print colorful.bold_red("Developer testing mode! armraw.bin loaded!")
do_t = do_testing
def do_run(self, line):
"""Begins execution of ARM binary."""
if not self.bin_running:
self.bin_running = True
else:
print colorful.yellow("Process is already running.")
return None
self.arm_dbg.start_execution()
do_start = do_run
do_r = do_run
def do_continue(self, line):
"""Continue execution from a paused state."""
self.arm_dbg.continue_execution()
do_c = do_continue
do_con = do_continue
def do_registers(self, line):
"""Display registers."""
self.arm_dbg.context_registers()
do_regs = do_registers
def do_step(self, line):
self.arm_dbg.stop_next_instruction = False
self.arm_dbg.use_step_mode = True
self.arm_dbg.step_execution()
# TODO: RF - check for error conditions
def do_x(self, line):
"""Examine memory similar to GDB x/? command"""
l = line.split()
byte_count = l[0]
address = int(l[1], 16)
# Read memory as byte, half-word, word
if byte_count == "b":
size = 1
elif byte_count == "h":
size = 2
elif byte_count == "w":
size = 4
# Print our data
data = self.arm_dbg.read_mem(address, size)
data_list = []
for i in data:
data_list.append("0x{:02x} ".format(i))
print " ".join(data_list)
@options([make_option('-l', '--list', action="store_false", help="List all set breakpoints.")])
def do_break(self, line):
pass
def do_snapshot(self, line):
""" Load/Save a snapshot """
l = line.split()
usage = "snapshot load|save ini|file"
if len(l) != 3:
print usage
return
if l[0] == "load":
if l[1] == "ini":
bin_type = armcpu_const.INI_BIN
elif l[1] == "file":
bin_type = armcpu_const.SNAPSHOT_BIN
else:
print usage
return
else:
print usage
return
self.arm_dbg = armcpu.ArmCPU(l[2], bin_type)
print colorful.bold_green("Loaded snapshot: {}".format(l[2]))
def do_info(self, line):
pass
def do_exit(self, line):
print "Exiting..."
return True
rps_addin = rvt_journal_writer.write_addin(
addin_file_path,
rvt_journal_writer.rps_addin_template,
rvt_model_version,
)
run_proc = rvt_journal_run(rvt_install_path, journal_file_path,
paths["root_dir"])
run_proc_id = run_proc.pid
run_proc_name = run_proc.name()
# let's wait a second for rvt process to fire up
time.sleep(0.5)
if run_proc.name() == "Revit.exe":
proc_name_colored = colorful.bold_green(run_proc_name)
else:
proc_name_colored = colorful.bold_red(run_proc_name)
print(f" process info: {run_proc_id} - {proc_name_colored}")
print(colorful.bold_orange(f"-detected revit: {rvt_model_version}"))
print(f" version:{rvt_model_version} at path: {rvt_install_path}")
print(colorful.bold_orange("-process countdown:"))
print(
f" timeout until termination of process: {run_proc_id} - {proc_name_colored}:"
)
log_journal = get_rvt_proc_journal(run_proc, paths["journals_dir"])
return_code = None
def train_model(config, model: MLabReservoir, scheduler: DataScheduler,
writer: SummaryWriter):
saved_model_path = os.path.join(config['log_dir'], 'ckpts')
os.makedirs(saved_model_path, exist_ok=True)
prev_t = config['data_schedule'][0]['subsets'][0][1]
done_t_num = 0
results_dict = dict()
for step, (x, y, t) in enumerate(scheduler):
summarize = step % config['summary_step'] == 0
# if we want to evaluate based on steps.
evaluate = (step % config['eval_step'] == config['eval_step'] - 1)
# find current task t's id in data_schedule to obtain the data name.
for data_dict in config['data_schedule']:
for subset in data_dict['subsets']:
if subset[1] == t:
cur_subset = subset[0]
# Evaluate the model when task changes
if t != prev_t:
done_t_num += 1
results_dict = scheduler.eval(model,
writer,
step + 1,
prev_t,
eval_title='eval',
results_dict=results_dict)
# Save the model
torch.save(
model.state_dict(),
os.path.join(saved_model_path,
'ckpt-{}'.format(str(step + 1).zfill(6))))
print(
colorful.bold_green('\nProgressing to Task %d' %
t).styled_string)
if step == 0:
print(
colorful.bold_green('\nProgressing to Task %d' %
t).styled_string)
if done_t_num >= len(scheduler.schedule):
writer.flush()
return
# learn the model
for i in range(config['batch_iter']):
if 'slab' in config['model_name']:
model.learn(x,
y,
t,
step * config['batch_iter'] + i,
scheduler.datasets[cur_subset].category_map,
scheduler.datasets[cur_subset].split_cats_dict,
data_obj=scheduler.datasets[cur_subset])
else:
model.learn(x,
y,
t,
step * config['batch_iter'] + i,
scheduler.datasets[cur_subset].category_map,
scheduler.datasets[cur_subset].split_cats_dict,
data_obj=scheduler.datasets[cur_subset].subsets[t])
prev_t = t
