max_stars_repo_path
stringlengths 4
245
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stringlengths 7
115
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int64 101
368k
| id
stringlengths 2
8
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stringlengths 6
1.03M
|
|---|---|---|---|---|
src/radish/step_testing/__init__.py | radish-bdd/radish2 | 182 | 12611088 | """
radish
~~~~~~
The root from red to green. BDD tooling for Python.
:copyright: (c) 2019 by <NAME> <<EMAIL>>
:license: MIT, see LICENSE for more details.
"""
|
core/migrations/0089_projects_to_project__alter_created_by.py | simpsonw/atmosphere | 197 | 12611098 | # -*- coding: utf-8 -*-
# Generated by Django 1.10.6 on 2017-05-17 16:34
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
def move_projects_to_project(apps, schema_editor):
Project = apps.get_model('core', 'Project')
for project in Project.objects.all():
for instance in project.instances.all():
instance.project = project
instance.save()
for volume in project.volumes.all():
volume.project = project
volume.save()
pass
class Migration(migrations.Migration):
dependencies = [
('core', '0088_set_project_leaders_and_authors'),
]
operations = [
migrations.AlterField(
model_name='project',
name='created_by',
field=models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name='projects',
to=settings.AUTH_USER_MODEL
),
),
migrations.RunPython(
move_projects_to_project, reverse_code=migrations.RunPython.noop
),
migrations.RemoveField(
model_name='project',
name='instances',
),
migrations.RemoveField(
model_name='project',
name='volumes',
),
]
|
src/oscar/management/commands/oscar_find_duplicate_emails.py | QueoLda/django-oscar | 4,639 | 12611099 | <filename>src/oscar/management/commands/oscar_find_duplicate_emails.py
from collections import Counter
from django.core.management.base import BaseCommand
from oscar.core.compat import get_user_model
User = get_user_model()
class Command(BaseCommand):
help = ('Finds email addresses that are used by more than one user. '
'Casing is ignored.')
def handle(self, *args, **options):
emails = User.objects.values_list('email', flat=True)
emails = map(lambda x: x.lower(), emails)
duplicates = sorted([
(email, count) for email, count in Counter(emails).most_common()
if count > 1])
if duplicates:
for email, __ in duplicates:
users = User.objects.filter(email__iexact=email)
user_strings = [
"{} (#{})".format(user.get_username(), user.pk)
for user in users]
print("{email} is assigned to: {pk_list}".format(
email=email, pk_list=", ".join(user_strings)))
else:
print("No duplicate email addresses found!")
|
wagtailmenus/tests/models/pages.py | cazgp/wagtailmenus | 329 | 12611107 | from datetime import date
from django.db import models
from django.http import Http404
from django.template.response import TemplateResponse
from wagtail.admin.edit_handlers import (
FieldPanel, MultiFieldPanel, PublishingPanel
)
from wagtail.contrib.routable_page.models import RoutablePageMixin, route
from wagtail.core.models import Page
from wagtailmenus.models import MenuPage, AbstractLinkPage
from .utils import TranslatedField
class MultilingualMenuPage(MenuPage):
title_de = models.CharField(
verbose_name='title (de)',
blank=True,
max_length=255,
)
title_fr = models.CharField(
verbose_name='title (fr)',
blank=True,
max_length=255,
)
repeated_item_text_de = models.CharField(
verbose_name='repeated item link text (de)',
blank=True,
max_length=255,
)
repeated_item_text_fr = models.CharField(
verbose_name='repeated item link text (fr)',
blank=True,
max_length=255,
)
translated_title = TranslatedField(
'title', 'title_de', 'title_fr'
)
translated_repeated_item_text = TranslatedField(
'repeated_item_text', 'repeated_item_text_de', 'repeated_item_text_fr',
)
class Meta:
abstract = True
def modify_submenu_items(
self, menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance, request, use_absolute_page_urls
):
return super().modify_submenu_items(
menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance, request, use_absolute_page_urls)
def has_submenu_items(
self, current_page, allow_repeating_parents, original_menu_tag,
menu_instance, request
):
return super().has_submenu_items(
current_page, allow_repeating_parents, original_menu_tag,
menu_instance, request)
def get_repeated_menu_item(
self, current_page, current_site, apply_active_classes,
original_menu_tag, request, use_absolute_page_urls
):
item = super().get_repeated_menu_item(
current_page, current_site, apply_active_classes,
original_menu_tag, request, use_absolute_page_urls)
item.text = self.translated_repeated_item_text or self.translated_title
return item
settings_panels = [
PublishingPanel(),
MultiFieldPanel(
heading="Advanced menu behaviour",
classname="collapsible collapsed",
children=(
FieldPanel('repeat_in_subnav'),
FieldPanel('repeated_item_text'),
FieldPanel('repeated_item_text_de'),
FieldPanel('repeated_item_text_fr'),
)
)
]
class HomePage(MenuPage):
template = 'homepage.html'
parent_page_types = [Page]
class TopLevelPage(MultilingualMenuPage):
template = 'page.html'
parent_page_types = [HomePage]
class LowLevelPage(Page):
template = 'page.html'
class TypicalPage(Page):
template = 'typical-page.html'
class ContactPage(MenuPage):
template = 'page.html'
parent_page_types = [HomePage]
subpage_types = []
def modify_submenu_items(
self, menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance=None, request=None, use_absolute_page_urls=False
):
menu_items = super().modify_submenu_items(
menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance, use_absolute_page_urls=use_absolute_page_urls)
"""
If rendering a 'main_menu', add some additional menu items to the end
of the list that link to various anchored sections on the same page
"""
if original_menu_tag == 'main_menu':
base_url = self.relative_url(current_site)
menu_items.extend((
{
'text': 'Get support',
'href': base_url + '#support',
'active_class': 'support',
},
{
'text': 'Speak to someone',
'href': base_url + '#call',
'active_class': 'call',
},
{
'text': 'Map & directions',
'href': base_url + '#map',
'active_class': 'map',
},
))
return menu_items
def has_submenu_items(
self, current_page, allow_repeating_parents,
original_menu_tag, menu_instance=None, request=None
):
"""
Because `modify_submenu_items` is being used to add additional menu
items, we need to indicate in menu templates that `ContactPage` objects
do have submenu items in main menus, even if they don't have children
pages.
"""
if original_menu_tag == 'main_menu':
return True
return super().has_submenu_items(
current_page, allow_repeating_parents, original_menu_tag,
menu_instance, request)
class NoAbsoluteUrlsPage(MenuPage):
"""
Ensure that we can handle pages that do not specify the `use_absolute_page_urls` kwarg.
"""
template = 'page.html'
parent_page_types = [HomePage]
def modify_submenu_items(
self, menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance=None, request=None
):
return super().modify_submenu_items(
menu_items, current_page, current_ancestor_ids,
current_site, allow_repeating_parents, apply_active_classes,
original_menu_tag, menu_instance, request)
def get_repeated_menu_item(
self, current_page, current_site, apply_active_classes,
original_menu_tag, request
):
return super().get_repeated_menu_item(
current_page, current_site, apply_active_classes,
original_menu_tag, request
)
class LinkPage(AbstractLinkPage):
pass
class ArticleListPage(RoutablePageMixin, Page):
template = 'page.html'
subpage_types = ['tests.ArticlePage']
def render(self, request, **kwargs):
return TemplateResponse(
request,
self.get_template(request, **kwargs),
self.get_context(request, **kwargs)
)
@route(r'^$')
def default_view(self, request):
"""
List published aticles
"""
return self.render(request)
@route(r'^([0-9]{4})/$')
def year_view(self, request, year):
"""
List articles published within a specific year
"""
return self.render(request, year=year)
@route(r'^([0-9]{4})/([0-9]{2})/$')
def month_view(self, request, year, month):
"""
List articles published within a specific month
"""
return self.render(request, year=year, month=month)
@route(r'^([0-9]{4})/([0-9]{2})/([0-9]{2})/$')
def day_view(self, request, year, month, day):
"""
List articles published on a specific day
"""
return self.render(request, year=year, month=month, day=day)
def route(self, request, path_components):
"""
Overrides RoutablePageMixin.route() to allow articles to have
publish date components in URLs."""
if len(path_components) >= 4:
# Attempt to route to an article using date/slug components
# NOTE: The page must have been published in order for
# `first_published_at` to be set
try:
year = path_components[0] # year
month = path_components[1] # month
day = path_components[2] # day
slug = path_components[3] # slug
publish_date = date(int(year), int(month), int(day))
except ValueError:
# Date components invalid
raise Http404
try:
# Find an article matching the above date and slug
article = ArticlePage.objects.child_of(self).get(
publish_date=publish_date, slug=slug)
except ArticlePage.DoesNotExist:
raise Http404
# Delegate further routing to the arcicle, excluding the
# date and slug components used above
return article.route(request, path_components[4:])
return super().route(request, path_components)
class ArticlePage(Page):
template = 'page.html'
parent_page_types = ['tests.ArticleListPage']
publish_date = models.DateField()
content_panels = Page.content_panels + [
FieldPanel('publish_date')
]
def get_url_parts(self, request=None):
"""
Overrides Page.get_url_parts() to replace the 'page path'
part, so that article urls include segments for the
article's `publish_date` as well as the `slug`
"""
site_id, root_url, page_path = super().get_url_parts(request)
page_path_bits = page_path.rstrip('/').split('/')
slug = page_path_bits.pop()
page_path_bits.extend(
self.publish_date.strftime('%Y|%m|%d').split('|')
)
# Add the slug to the end and re-combine
page_path_bits.append(slug)
page_path = '/'.join(page_path_bits) + '/'
return site_id, root_url, page_path
|
Algo and DSA/LeetCode-Solutions-master/Python/all-elements-in-two-binary-search-trees.py | Sourav692/FAANG-Interview-Preparation | 3,269 | 12611148 | # Time: O(n)
# Space: O(h)
# Definition for a binary tree node.
class TreeNode(object):
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution(object):
def getAllElements(self, root1, root2):
"""
:type root1: TreeNode
:type root2: TreeNode
:rtype: List[int]
"""
def inorder_gen(root):
result, stack = [], [(root, False)]
while stack:
root, is_visited = stack.pop()
if root is None:
continue
if is_visited:
yield root.val
else:
stack.append((root.right, False))
stack.append((root, True))
stack.append((root.left, False))
yield None
result = []
left_gen, right_gen = inorder_gen(root1), inorder_gen(root2)
left, right = next(left_gen), next(right_gen)
while left is not None or right is not None:
if right is None or (left is not None and left < right):
result.append(left)
left = next(left_gen)
else:
result.append(right)
right = next(right_gen)
return result
|
golem/report/execution_report.py | Sunil-Rathore/golem | 171 | 12611172 | import base64
import errno
import json
import os
from golem.core import utils
from golem.core.project import Project
from golem.report import test_report
from golem.test_runner.conf import ResultsEnum
def execution_report_default():
params = utils.ImmutableKeysDict(browsers=[],
processes=None,
environments=[],
tags=[],
remote_url='')
execution_report = utils.ImmutableKeysDict(tests=[],
params=params,
total_tests=0,
totals_by_result={},
net_elapsed_time=0,
has_finished=False)
return execution_report
def _parse_execution_data(execution_directory=None, project=None, execution=None,
timestamp=None, finalize=False):
execution_data = execution_report_default()
if not execution_directory:
execution_directory = execution_report_path(project, execution, timestamp)
tests = []
if os.path.isdir(execution_directory):
tests = next(os.walk(execution_directory))[1]
for test in tests: # test_file + test set
test_path = os.path.join(execution_directory, test)
test_file_json_report = os.path.join(test_path, 'report.json')
report_log_path = os.path.join(test_path, 'execution_info.log')
if os.path.isfile(test_file_json_report):
with open(test_file_json_report, encoding='utf-8') as f:
# This contains one dict per test_function inside this test_file
test_file_report = json.load(f)
else:
test_file_report = []
for test_function in test_file_report:
execution_data['total_tests'] += 1
if finalize:
if test_function['result'] == ResultsEnum.PENDING:
test_function['result'] = ResultsEnum.NOT_RUN
_status_total = execution_data['totals_by_result'].get(test_function['result'], 0) + 1
execution_data['totals_by_result'][test_function['result']] = _status_total
execution_data['tests'].append(test_function)
return execution_data
def get_execution_data(execution_directory=None, project=None, execution=None,
timestamp=None):
"""Retrieve the data of all the tests of an execution.
From the report.json if it exists, otherwise it parses
the tests one by one.
The `report.json` should have been generated when the execution ended.
"""
has_finished = False
if execution_directory is None:
execution_directory = execution_report_path(project, execution, timestamp)
report_path = os.path.join(execution_directory, 'report.json')
if os.path.isfile(report_path):
with open(report_path, encoding='utf-8') as f:
data = json.load(f)
# if execution report file exists, the execution has finished
has_finished = True
else:
data = _parse_execution_data(execution_directory, project, execution, timestamp)
data['has_finished'] = has_finished
return data
def generate_execution_report(execution_directory, elapsed_time, browsers, processes,
environments, tags, remote_url):
"""Generate execution json report.
This is called at the end of the execution
"""
data = _parse_execution_data(execution_directory=execution_directory, finalize=True)
data['net_elapsed_time'] = elapsed_time
data['params']['browsers'] = browsers
remote_browser = any([b['capabilities'] for b in browsers])
contains_remote = any('remote' in b['name'] for b in browsers)
if remote_browser or contains_remote:
data['params']['remote_url'] = remote_url
data['params']['processes'] = processes
data['params']['environments'] = environments
data['params']['tags'] = tags
data['has_finished'] = True
report_path = os.path.join(execution_directory, 'report.json')
with open(report_path, 'w', encoding='utf-8') as json_file:
json.dump(data, json_file, indent=4, ensure_ascii=False)
return data
def save_execution_json_report(report_data, reportdir, report_name='report'):
"""Save execution report data to the specified reportdir and report_name"""
report_path = os.path.join(reportdir, f'{report_name}.json')
if not os.path.exists(os.path.dirname(report_path)):
os.makedirs(os.path.dirname(report_path), exist_ok=True)
try:
with open(report_path, 'w', encoding='utf-8') as f:
json.dump(report_data, f, indent=4, ensure_ascii=False)
except IOError as e:
if e.errno == errno.EACCES:
print(f'ERROR: cannot write to {report_path}, PermissionError (Errno 13)')
else:
print(f'ERROR: There was an error writing to {report_path}')
def execution_report_path(project, execution, timestamp):
return os.path.join(Project(project).report_directory_path, execution, timestamp)
def create_execution_directory(project, execution, timestamp):
"""Create the report directory for an execution.
Directory should have the following path:
<testdir>/projects/<project>/reports/<execution>/<timestamp>/
"""
path = execution_report_path(project, execution, timestamp)
os.makedirs(path, exist_ok=True)
return path
def has_execution_finished(path):
"""Is a execution finished."""
json_report_path = os.path.join(path, 'report.json')
return os.path.isfile(json_report_path)
def test_file_execution_result_all_sets(project, execution, timestamp, test_file):
""""""
status = {
'sets': {},
'has_finished': False
}
path = execution_report_path(project, execution, timestamp)
if os.path.isdir(path):
set_dirs = [x for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]
set_dirs = [x for x in set_dirs if x.startswith(test_file)]
for set_dir in set_dirs:
set_name = set_dir.replace(test_file, '')
if set_name and set_name.startswith('.'):
set_name = set_name[1:]
test_file_report = test_report.get_test_file_report_json(project, execution, timestamp,
test_file, set_name=set_name)
log_info = test_report.get_test_info_log(project, execution, timestamp, test_file, set_name=set_name)
log_debug = test_report.get_test_debug_log(project, execution, timestamp, test_file, set_name=set_name)
if set_name == '':
set_name = 'default'
status['sets'][set_name] = {
'report': test_file_report,
'log_info': log_info,
'log_debug': log_debug
}
status['has_finished'] = has_execution_finished(path)
return status
def test_file_execution_result(project, execution, timestamp, test_file, set_name):
all_sets = test_file_execution_result_all_sets(project, execution, timestamp, test_file)
if set_name == '':
set_name = 'default'
return {
'set': all_sets['sets'][set_name],
'has_finished': all_sets['has_finished']
}
def function_test_execution_result(project, execution, timestamp, test_file, test, set_name='',
no_screenshots=False, encode_screenshots=False):
"""
:Args:
- encode_screenshots: return screenshot files encoded as a base64 string or
the screenshot filename (rel to its folder).
- no_screenshots: convert screenshot values to None
"""
path = execution_report_path(project, execution, timestamp)
test_json = {
'has_finished': False
}
if has_execution_finished(path):
json_report = get_execution_data(path)
for t in json_report['tests']:
if t['test_file'] == test_file and t['test'] == test and t['set_name'] == set_name:
test_json = t
test_json['has_finished'] = True
break
else:
test_json = test_report.get_test_function_report_json(project, execution, timestamp,
test_file, test, set_name)
test_json['has_finished'] = False
test_json['debug_log'] = test_report.get_test_debug_log(project, execution, timestamp,
test_file, set_name)
test_json['info_log'] = test_report.get_test_info_log(project, execution, timestamp,
test_file, set_name)
if no_screenshots:
for step in test_json['steps']:
step['screenshot'] = None
elif encode_screenshots:
for step in test_json['steps']:
if step['screenshot'] is not None:
image_filename = test_report.screenshot_path(project, execution, timestamp,
test_file, test, set_name,
step['screenshot'])
b64 = base64.b64encode(open(image_filename, "rb").read()).decode('utf-8')
step['screenshot'] = b64
return test_json
|
python/paddle/fluid/tests/unittests/ir/inference/test_trt_matmul_quant_dequant.py | wwqgtxx/Paddle | 17,085 | 12611180 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
from quant_dequant_test import QuantDequantTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import PassVersionChecker
from paddle.fluid.core import AnalysisConfig
class TensorRTMatMulQuantDequantDims3Test(QuantDequantTest):
def setUp(self):
self.set_params()
def network():
self.data = fluid.data(
name='data', shape=[1, 28, 28], dtype='float32')
self.label = fluid.data(name='label', shape=[1, 1], dtype='int64')
matmul_out = fluid.layers.matmul(
x=self.data,
y=self.data,
transpose_x=self.transpose_x,
transpose_y=self.transpose_y,
alpha=self.alpha)
fc_out = fluid.layers.fc(input=matmul_out,
size=10,
num_flatten_dims=1,
bias_attr=False,
act=None)
result = fluid.layers.relu(fc_out)
loss = fluid.layers.cross_entropy(input=result, label=self.label)
avg_loss = fluid.layers.mean(loss)
return avg_loss, result
self.main_program.random_seed = 2
self.startup_program.random_seed = 2
self.test_main_program.random_seed = 2
#self.test_startup_program.random_seed = 2
with fluid.unique_name.guard():
with fluid.program_guard(self.main_program, self.startup_program):
self.loss, result = network()
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(self.loss)
with fluid.unique_name.guard():
with fluid.program_guard(self.test_main_program,
self.startup_program):
network()
self.feeds = {"data": np.random.random([1, 28, 28]).astype("float32")}
self.fetch_list = [result]
self.enable_trt = True
self.trt_parameters = TensorRTMatMulQuantDequantDims3Test.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Int8, False, False)
self.activation_quantize_type = 'moving_average_abs_max'
self.weight_quantize_type = 'channel_wise_abs_max'
def set_params(self):
self.transpose_x = False
self.transpose_y = False
self.alpha = 1.0
def test_check_output(self):
#self.quant_dequant()
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(
use_gpu, atol=1e-1, flatten=False, rtol=1e-1)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
class TensorRTMatMulQuantDequantDims3TransposeXTest(
TensorRTMatMulQuantDequantDims3Test):
def set_params(self):
self.transpose_x = True
self.transpose_y = False
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims3TransposeYTest(
TensorRTMatMulQuantDequantDims3Test):
def set_params(self):
self.transpose_x = False
self.transpose_y = True
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims3TransposeXYTest(
TensorRTMatMulQuantDequantDims3Test):
def set_params(self):
self.transpose_x = True
self.transpose_y = True
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims4Test(QuantDequantTest):
def setUp(self):
self.set_params()
def network():
self.data = fluid.data(
name='data', shape=[1, 28, 28], dtype='float32')
self.label = fluid.data(name='label', shape=[1, 1], dtype='int64')
reshape_out = fluid.layers.reshape(self.data, shape=[1, 4, 14, 14])
matmul_out = fluid.layers.matmul(
x=reshape_out,
y=reshape_out,
transpose_x=self.transpose_x,
transpose_y=self.transpose_y,
alpha=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
fc_out = fluid.layers.fc(input=matmul_out,
size=10,
num_flatten_dims=1,
bias_attr=False,
act=None)
result = fluid.layers.relu(fc_out)
loss = fluid.layers.cross_entropy(input=result, label=self.label)
avg_loss = fluid.layers.mean(loss)
return avg_loss, result
self.main_program.random_seed = 2
self.startup_program.random_seed = 2
self.test_main_program.random_seed = 2
#self.test_startup_program.random_seed = 2
with fluid.unique_name.guard():
with fluid.program_guard(self.main_program, self.startup_program):
self.loss, result = network()
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(self.loss)
with fluid.unique_name.guard():
with fluid.program_guard(self.test_main_program,
self.startup_program):
network()
self.feeds = {"data": np.random.random([1, 28, 28]).astype("float32")}
self.fetch_list = [result]
self.enable_trt = True
self.trt_parameters = TensorRTMatMulQuantDequantDims4Test.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Int8, False, False)
self.activation_quantize_type = 'moving_average_abs_max'
self.weight_quantize_type = 'channel_wise_abs_max'
def set_params(self):
self.transpose_x = False
self.transpose_y = False
self.alpha = 1.0
def test_check_output(self):
#self.quant_dequant()
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(
use_gpu, atol=1e-1, flatten=False, rtol=1e-1)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
class TensorRTMatMulQuantDequantDims4TransposeXTest(
TensorRTMatMulQuantDequantDims4Test):
def set_params(self):
self.transpose_x = True
self.transpose_y = False
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims4TransposeYTest(
TensorRTMatMulQuantDequantDims4Test):
def set_params(self):
self.transpose_x = False
self.transpose_y = True
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims4TransposeXYTest(
TensorRTMatMulQuantDequantDims4Test):
def set_params(self):
self.transpose_x = True
self.transpose_y = True
self.alpha = 1.0
class TensorRTMatMulQuantDequantDims4ScaleTest(
TensorRTMatMulQuantDequantDims4Test):
def set_params(self):
self.transpose_x = False
self.transpose_y = False
self.alpha = 2.0
if __name__ == "__main__":
unittest.main()
|
test-data/unit/plugins/type_anal_hook.py | Phlogistique/mypy | 12,496 | 12611185 | from typing import Optional, Callable
from mypy.plugin import Plugin, AnalyzeTypeContext
from mypy.types import Type, TypeList, AnyType, CallableType, TypeOfAny
# The official name changed to NoneType but we have an alias for plugin compat reasons
# so we'll keep testing that here.
from mypy.types import NoneTyp
class TypeAnalyzePlugin(Plugin):
def get_type_analyze_hook(self, fullname: str
) -> Optional[Callable[[AnalyzeTypeContext], Type]]:
if fullname == 'm.Signal':
return signal_type_analyze_callback
return None
def signal_type_analyze_callback(ctx: AnalyzeTypeContext) -> Type:
if (len(ctx.type.args) != 1
or not isinstance(ctx.type.args[0], TypeList)):
ctx.api.fail('Invalid "Signal" type (expected "Signal[[t, ...]]")', ctx.context)
return AnyType(TypeOfAny.from_error)
args = ctx.type.args[0]
assert isinstance(args, TypeList)
analyzed = ctx.api.analyze_callable_args(args)
if analyzed is None:
return AnyType(TypeOfAny.from_error) # Error generated elsewhere
arg_types, arg_kinds, arg_names = analyzed
arg_types = [ctx.api.analyze_type(arg) for arg in arg_types]
type_arg = CallableType(arg_types,
arg_kinds,
arg_names,
NoneTyp(),
ctx.api.named_type('builtins.function', []))
return ctx.api.named_type('m.Signal', [type_arg])
def plugin(version):
return TypeAnalyzePlugin
|
rebuild_classifier_table.py | RomeroLaura/Axelrod | 596 | 12611188 | <gh_stars>100-1000
import os
from axelrod import all_strategies
from axelrod.classifier import all_classifiers, rebuild_classifier_table
if __name__ == "__main__":
# Change to relative path inside axelrod folder
rebuild_classifier_table(all_classifiers, all_strategies)
|
napari/components/experimental/monitor/__init__.py | MaksHess/napari | 1,345 | 12611197 | <reponame>MaksHess/napari
"""Monitor service."""
from ._monitor import monitor
from ._utils import numpy_dumps
|
test/cctest/testcfg.py | hgl888/v8-3.17.16.2 | 140 | 12611199 | <reponame>hgl888/v8-3.17.16.2
# Copyright 2008 the V8 project authors. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import shutil
from testrunner.local import commands
from testrunner.local import testsuite
from testrunner.local import utils
from testrunner.objects import testcase
class CcTestSuite(testsuite.TestSuite):
def __init__(self, name, root):
super(CcTestSuite, self).__init__(name, root)
self.serdes_dir = os.path.normpath(
os.path.join(root, "..", "..", "out", ".serdes"))
if os.path.exists(self.serdes_dir):
shutil.rmtree(self.serdes_dir, True)
os.makedirs(self.serdes_dir)
def ListTests(self, context):
shell = os.path.abspath(os.path.join(context.shell_dir, self.shell()))
if utils.IsWindows():
shell += ".exe"
output = commands.Execute(context.command_prefix +
[shell, "--list"] +
context.extra_flags)
if output.exit_code != 0:
print output.stdout
print output.stderr
return []
tests = []
for test_desc in output.stdout.strip().split():
raw_test, dependency = test_desc.split('<')
if dependency != '':
dependency = raw_test.split('/')[0] + '/' + dependency
else:
dependency = None
test = testcase.TestCase(self, raw_test, dependency=dependency)
tests.append(test)
tests.sort()
return tests
def GetFlagsForTestCase(self, testcase, context):
testname = testcase.path.split(os.path.sep)[-1]
serialization_file = os.path.join(self.serdes_dir, "serdes_" + testname)
serialization_file += ''.join(testcase.flags).replace('-', '_')
return (testcase.flags + [testcase.path] + context.mode_flags +
["--testing_serialization_file=" + serialization_file])
def shell(self):
return "cctest"
def GetSuite(name, root):
return CcTestSuite(name, root)
# Deprecated definitions below.
# TODO(jkummerow): Remove when SCons is no longer supported.
from os.path import exists, join, normpath
import test
class CcTestCase(test.TestCase):
def __init__(self, path, executable, mode, raw_name, dependency, context, variant_flags):
super(CcTestCase, self).__init__(context, path, mode)
self.executable = executable
self.raw_name = raw_name
self.dependency = dependency
self.variant_flags = variant_flags
def GetLabel(self):
return "%s %s %s" % (self.mode, self.path[-2], self.path[-1])
def GetName(self):
return self.path[-1]
def BuildCommand(self, name):
serialization_file = ''
if exists(join(self.context.buildspace, 'obj', 'test', self.mode)):
serialization_file = join('obj', 'test', self.mode, 'serdes')
else:
serialization_file = join('obj', 'serdes')
if not exists(join(self.context.buildspace, 'obj')):
os.makedirs(join(self.context.buildspace, 'obj'))
serialization_file += '_' + self.GetName()
serialization_file = join(self.context.buildspace, serialization_file)
serialization_file += ''.join(self.variant_flags).replace('-', '_')
serialization_option = '--testing_serialization_file=' + serialization_file
result = [ self.executable, name, serialization_option ]
result += self.context.GetVmFlags(self, self.mode)
return result
def GetCommand(self):
return self.BuildCommand(self.raw_name)
def Run(self):
if self.dependency != '':
dependent_command = self.BuildCommand(self.dependency)
output = self.RunCommand(dependent_command)
if output.HasFailed():
return output
return test.TestCase.Run(self)
class CcTestConfiguration(test.TestConfiguration):
def __init__(self, context, root):
super(CcTestConfiguration, self).__init__(context, root)
def GetBuildRequirements(self):
return ['cctests']
def ListTests(self, current_path, path, mode, variant_flags):
executable = 'cctest'
if utils.IsWindows():
executable += '.exe'
executable = join(self.context.buildspace, executable)
if not exists(executable):
executable = join('obj', 'test', mode, 'cctest')
if utils.IsWindows():
executable += '.exe'
executable = join(self.context.buildspace, executable)
full_command = self.context.processor([executable, '--list'])
output = test.Execute(full_command, self.context)
if output.exit_code != 0:
print output.stdout
print output.stderr
return []
result = []
for test_desc in output.stdout.strip().split():
raw_test, dependency = test_desc.split('<')
relative_path = raw_test.split('/')
full_path = current_path + relative_path
if dependency != '':
dependency = relative_path[0] + '/' + dependency
if self.Contains(path, full_path):
result.append(CcTestCase(full_path, executable, mode, raw_test, dependency, self.context, variant_flags))
result.sort()
return result
def GetTestStatus(self, sections, defs):
status_file = join(self.root, 'cctest.status')
if exists(status_file):
test.ReadConfigurationInto(status_file, sections, defs)
def GetConfiguration(context, root):
return CcTestConfiguration(context, root)
|
lib/bindings/samples/server/utils/loop.py | tlalexander/stitchEm | 182 | 12611249 | <reponame>tlalexander/stitchEm
import threading
import logging
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
class Loop(threading.Thread):
"""Calls a function every interval seconds:
t = Loop(30.0, f, args=[], kwargs={})
t.start()
t.cancel() # stop the loop's action if it's still waiting
"""
STOP = True
CONTINUE = False
def __init__(self, interval, function, name=None, restart=False, args=[], kwargs={}):
super(Loop, self).__init__(name=name)
self.current_interval = 0
self.interval = interval
self.function = function
self.args = args
self.kwargs = kwargs
self.finished = threading.Event()
self.running = threading.Event()
self.running.set()
self.daemon = True
self.restart = restart
def start(self, paused=False):
if paused:
self.running.clear()
super(Loop, self).start()
def set_interval(self, interval):
self.interval = interval
def is_finished(self):
return self.finished.is_set()
def pause(self):
self.running.clear()
def unpause(self):
self.running.set()
def is_paused(self):
return not self.running.is_set()
def finish(self):
self.unpause()
self.finished.set()
def cancel(self):
self.finish()
self.join()
def run(self):
logging.info("Starting loop {}".format(self.name))
while True:
try:
while not self.finished.is_set():
self.finished.wait(self.current_interval)
self.running.wait()
if not self.finished.is_set():
stop = self.function(*self.args, **self.kwargs)
if stop:
break
else:
break
self.current_interval = self.interval
break
except Exception as e:
logging.error("Exception in loop {}: {}".format(self.name, str(e)))
if not self.restart:
break
logging.info("Restarting loop {}".format(self.name))
logging.info("Stopped loop {}".format(self.name))
|
pythainlp/corpus/__init__.py | Gorlph/pythainlp | 569 | 12611265 | # -*- coding: utf-8 -*-
"""
Corpus related functions.
Access to dictionaries, word lists, and language models.
Including download manager.
"""
__all__ = [
"corpus_path",
"corpus_db_path",
"corpus_db_url",
"countries",
"download",
"get_corpus",
"get_corpus_db",
"get_corpus_db_detail",
"get_corpus_default_db",
"get_corpus_path",
"provinces",
"remove",
"thai_family_names",
"thai_female_names",
"thai_male_names",
"thai_negations",
"thai_stopwords",
"thai_syllables",
"thai_words",
"path_pythainlp_corpus",
]
import os
from pythainlp.tools import get_full_data_path, get_pythainlp_path
from tinydb import TinyDB
# Remote and local corpus databases
_CORPUS_DIRNAME = "corpus"
_CORPUS_PATH = os.path.join(get_pythainlp_path(), _CORPUS_DIRNAME)
# remote corpus catalog URL
_CORPUS_DB_URL = (
"https://pythainlp.github.io/pythainlp-corpus/db.json"
)
# local corpus catalog filename
_CORPUS_DB_FILENAME = "db.json"
# local corpus catalog full path
_CORPUS_DB_PATH = get_full_data_path(_CORPUS_DB_FILENAME)
# create a local corpus database if it does not already exist
if not os.path.exists(_CORPUS_DB_PATH):
TinyDB(_CORPUS_DB_PATH).close()
def corpus_path() -> str:
"""
Get path where corpus files are kept locally.
"""
return _CORPUS_PATH
def corpus_db_url() -> str:
"""
Get remote URL of corpus catalog.
"""
return _CORPUS_DB_URL
def corpus_db_path() -> str:
"""
Get local path of corpus catalog.
"""
return _CORPUS_DB_PATH
from pythainlp.corpus.core import (
download,
get_corpus,
get_corpus_db,
get_corpus_db_detail,
get_corpus_default_db,
get_corpus_path,
remove,
path_pythainlp_corpus,
) # these imports must come before other pythainlp.corpus.* imports
from pythainlp.corpus.common import (
countries,
provinces,
thai_family_names,
thai_female_names,
thai_male_names,
thai_negations,
thai_stopwords,
thai_syllables,
thai_words,
)
|
cyvcf2/__main__.py | grahamgower/cyvcf2 | 307 | 12611270 | <reponame>grahamgower/cyvcf2
import sys
from .cli import cyvcf2 as cli
"""
cyvcf2.__main__
~~~~~~~~~~~~~~~~~~~~~
The main entry point for the command line interface.
Invoke as ``cyvcf2`` (if installed)
or ``python -m cyvcf2`` (no install required).
"""
if __name__ == "__main__":
# exit using whatever exit code the CLI returned
sys.exit(cli()) |
CalibTracker/SiPixelTools/python/SiPixelErrorsCalibDigis_cfi.py | ckamtsikis/cmssw | 852 | 12611294 | import FWCore.ParameterSet.Config as cms
siPixelErrorsDigisToCalibDigis = cms.EDAnalyzer("SiPixelErrorsDigisToCalibDigis",
saveFile = cms.untracked.bool(True),
outputFilename = cms.string('myResults.root'),
SiPixelProducerLabelTag = cms.InputTag("siPixelCalibDigis")
)
|
leetcode/282.expression-add-operators.py | geemaple/algorithm | 177 | 12611308 | class Solution(object):
def __init__(self, *args, **kwargs):
self.num = None
self.target = None
def addOperators(self, num, target):
"""
:type num: str
:type target: int
:rtype: List[str]
"""
if num is None or len(num) == 0:
return []
self.num = num
self.target = target
res = []
self.dfs(0, 0, 0, '', res)
return res
def dfs(self, pos, last, sum, path, res):
if pos == len(self.num):
if sum == self.target:
res.append(path)
return
cur_val = 0
cur = ''
for i in range(pos, len(self.num)):
cur_val = cur_val * 10 + int(self.num[i])
cur += self.num[i]
if pos == 0:
self.dfs(i + 1, cur_val, cur_val, path + cur, res)
else:
self.dfs(i + 1, cur_val, sum + cur_val, path + '+' + cur, res)
self.dfs(i + 1, -cur_val, sum - cur_val, path + '-' + cur, res)
self.dfs(i + 1, last * cur_val, sum - last + last * cur_val, path + '*' + cur, res)
if self.num[pos] == '0':
break
|
heat/tests/convergence/framework/scenario.py | noironetworks/heat | 265 | 12611333 | #
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os
from oslo_log import log as logging
LOG = logging.getLogger(__name__)
def list_all():
scenario_dir = os.path.join(os.path.dirname(__file__), '../scenarios')
if not os.path.isdir(scenario_dir):
LOG.error('Scenario directory "%s" not found', scenario_dir)
return
for root, dirs, files in os.walk(scenario_dir):
for filename in files:
name, ext = os.path.splitext(filename)
if ext == '.py':
LOG.debug('Found scenario "%s"', name)
yield name, os.path.join(root, filename)
class Scenario(object):
def __init__(self, name, path):
self.name = name
with open(path) as f:
source = f.read()
self.code = compile(source, path, 'exec')
LOG.debug('Loaded scenario %s', self.name)
def __call__(self, _event_loop, **global_env):
LOG.info('*** Beginning scenario "%s"', self.name)
exec(self.code, global_env, {})
_event_loop()
|
tests/test_model/test_backbone/test_repvgg_backbone.py | ZJCV/PyCls | 110 | 12611338 | # -*- coding: utf-8 -*-
"""
@date: 2021/2/2 下午5:02
@file: test_repvgg_backbone.py
@author: zj
@description:
"""
import torch
from zcls.model.backbones.vgg.repvgg_backbone import RepVGGBackbone
def test_repvgg_backbone():
model = RepVGGBackbone()
print(model)
data = torch.randn(1, 3, 224, 224)
outputs = model(data)
print(outputs.shape)
assert outputs.shape == (1, 512, 7, 7)
if __name__ == '__main__':
test_repvgg_backbone()
|
setup.py | hafixo/afctl | 131 | 12611339 | <reponame>hafixo/afctl
from setuptools import setup, find_packages
from os import path
import versioneer
with open('requirements.txt') as f:
required = f.read().splitlines()
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name="afctl",
version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(),
url="https://github.com/qubole/afctl",
author="Qubole",
author_email="<EMAIL>",
description="Python commandline tool to make deployment of Airflow projects easier.",
long_description=long_description,
long_description_content_type='text/markdown',
keywords="airflow cli deployment",
packages=find_packages(),
include_package_data=True,
entry_points={
'console_scripts': ['afctl=afctl.command_line:main'],
},
install_requires=required,
)
|
python/oneflow/support/async_util.py | wangyuyue/oneflow | 3,285 | 12611344 | """
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import threading
def Await(counter, func):
assert counter > 0
cond_var = threading.Condition()
counter_box = [counter]
result_list = []
def Yield(result=None):
result_list.append(result)
cond_var.acquire()
assert counter_box[0] > 0
counter_box[0] -= 1
cond_var.notify()
cond_var.release()
func(Yield)
cond_var.acquire()
while counter_box[0] > 0:
cond_var.wait()
cond_var.release()
return result_list
|
var/spack/repos/builtin/packages/py-yahmm/package.py | kkauder/spack | 2,360 | 12611374 | <reponame>kkauder/spack
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyYahmm(PythonPackage):
"""YAHMM is a HMM package for Python, implemented in Cython for speed."""
pypi = "yahmm/yahmm-1.1.3.zip"
version('1.1.3', sha256='fe3614ef96da9410468976756fb93dc8235485242c05df01d8e5ed356a7dfb43')
depends_on('[email protected]:', type=('build', 'run'))
depends_on('[email protected]:', type=('build', 'run'))
depends_on('[email protected]:', type=('build', 'run'))
depends_on('[email protected]:', type=('build', 'run'))
depends_on('[email protected]:', type=('build', 'run'))
|
setup.py | xiazhaokang/vnpy | 323 | 12611406 | <filename>setup.py
"""
vn.py - By Traders, For Traders.
The vn.py project is an open-source quantitative trading framework
that is developed by traders, for traders.
The project is mainly written in Python and uses C++ for low-layer
and performance sensitive infrastructure.
Using the vn.py project, institutional investors and professional
traders, such as hedge funds, prop trading firms and investment banks,
can easily develop complex trading strategies with the Event Engine
Strategy Module, and automatically route their orders to the most
desired destinations, including equity, commodity, forex and many
other financial markets.
"""
import ast
import os
import platform
import re
import sys
from setuptools import Extension, find_packages, setup
def gather_autocxxpy_generated_files(root: str):
fs = [os.path.join(root, "module.cpp")]
for root, dirs, filenames in os.walk(root):
for filename in filenames:
filebase, ext = os.path.splitext(filename)
if ext == ".cpp" and filebase.startswith("generated_functions_"):
path = os.path.join(root, filename)
fs.append(path)
return fs
def check_extension_build_flag(ext_modules, key: str, module: Extension):
value = os.environ.get(key, None)
if value is not None:
if value == '1':
ext_modules = list(set(ext_modules) | {module})
elif value == '0':
ext_modules = list(set(ext_modules) - {module})
else:
raise ValueError(
f"Flag {key} should be '0' or '1', but {repr(value)} got.")
return ext_modules
def is_psycopg2_exists():
try:
import psycopg2 # noqa
return True
except ImportError:
return False
def get_install_requires():
install_requires = [
"PyQt5",
"qdarkstyle",
"requests",
"websocket-client",
"peewee",
"pymysql",
"mongoengine",
"numpy",
"pandas",
"matplotlib",
"seaborn",
"futu-api",
"tigeropen",
"rqdatac",
"ta-lib",
"ibapi",
"deap",
"pyzmq",
"QScintilla"
]
if not is_psycopg2_exists():
install_requires.append("psycopg2-binary")
if sys.version_info.minor < 7:
install_requires.append("dataclasses")
return install_requires
def get_version_string():
global version
with open("vnpy/__init__.py", "rb") as f:
version_line = re.search(
r"__version__\s+=\s+(.*)", f.read().decode("utf-8")
).group(1)
return str(ast.literal_eval(version_line))
def get_ext_modules():
if platform.uname().system == "Windows":
compiler_flags = [
"/MP", "/std:c++17", # standard
"/O2", "/Ob2", "/Oi", "/Ot", "/Oy", "/GL", # Optimization
"/bigobj", # Better compatibility
"/wd4819", # 936 code page
"/D_CRT_SECURE_NO_WARNINGS",
# suppress warning of unsafe functions like fopen, strcpy, etc
]
extra_link_args = []
runtime_library_dirs = None
else:
compiler_flags = [
"-std=c++17", # standard
"-O3", # Optimization
"-Wno-delete-incomplete", "-Wno-sign-compare",
]
extra_link_args = ["-lstdc++"]
runtime_library_dirs = ["$ORIGIN"]
vnctpmd = Extension(
"vnpy.api.ctp.vnctpmd",
[
"vnpy/api/ctp/vnctp/vnctpmd/vnctpmd.cpp",
],
include_dirs=["vnpy/api/ctp/include",
"vnpy/api/ctp/vnctp", ],
define_macros=[],
undef_macros=[],
library_dirs=["vnpy/api/ctp/libs", "vnpy/api/ctp"],
libraries=["thostmduserapi_se", "thosttraderapi_se", ],
extra_compile_args=compiler_flags,
extra_link_args=extra_link_args,
runtime_library_dirs=runtime_library_dirs,
depends=[],
language="cpp",
)
vnctptd = Extension(
"vnpy.api.ctp.vnctptd",
[
"vnpy/api/ctp/vnctp/vnctptd/vnctptd.cpp",
],
include_dirs=["vnpy/api/ctp/include",
"vnpy/api/ctp/vnctp", ],
define_macros=[],
undef_macros=[],
library_dirs=["vnpy/api/ctp/libs", "vnpy/api/ctp"],
libraries=["thostmduserapi_se", "thosttraderapi_se", ],
extra_compile_args=compiler_flags,
extra_link_args=extra_link_args,
runtime_library_dirs=runtime_library_dirs,
depends=[],
language="cpp",
)
vnoes = Extension(
name="vnpy.api.oes.vnoes",
sources=gather_autocxxpy_generated_files(
"vnpy/api/oes/vnoes/generated_files/",
),
include_dirs=["vnpy/api/oes/vnoes/include",
"vnpy/api/oes/vnoes/include/oes", ],
define_macros=[("BRIGAND_NO_BOOST_SUPPORT", "1")],
undef_macros=[],
library_dirs=["vnpy/api/oes/vnoes/libs"],
libraries=["oes_api"],
extra_compile_args=compiler_flags,
extra_link_args=extra_link_args,
runtime_library_dirs=runtime_library_dirs,
depends=[],
language="cpp",
)
if platform.system() == "Windows":
# use pre-built pyd for windows ( support python 3.7 only )
ext_modules = []
elif platform.system() == "Darwin":
ext_modules = []
else:
ext_modules = [vnctptd, vnctpmd, vnoes]
ext_modules = check_extension_build_flag(
ext_modules, "VNPY_BUILD_OES", vnoes)
ext_modules = check_extension_build_flag(
ext_modules, "VNPY_BUILD_CTP", vnctptd)
ext_modules = check_extension_build_flag(
ext_modules, "VNPY_BUILD_CTP", vnctpmd)
return ext_modules
parallel = os.environ.get('VNPY_BUILD_PARALLEL', None)
if parallel:
if parallel == 'auto':
parallel = os.cpu_count()
if parallel != 'no':
from ci.parallel_build_distutils import patch_distutils
patch_distutils(int(parallel))
setup(
name="vnpy",
version=get_version_string(),
author="vn.py team",
author_email="<EMAIL>",
license="MIT",
url="https://www.vnpy.com",
description="A framework for developing quant trading systems.",
long_description=__doc__,
keywords='quant quantitative investment trading algotrading',
include_package_data=True,
packages=find_packages(exclude=["tests", "ci", "tests.*"]),
package_data={"": [
"*.ico",
"*.ini",
"*.dll",
"*.so",
"*.pyd",
]},
install_requires=get_install_requires(),
classifiers=[
"Development Status :: 5 - Production/Stable",
"Operating System :: Microsoft :: Windows :: Windows 7",
"Operating System :: Microsoft :: Windows :: Windows 8",
"Operating System :: Microsoft :: Windows :: Windows 10",
"Operating System :: Microsoft :: Windows :: Windows Server 2008",
"Operating System :: Microsoft :: Windows :: Windows Server 2012",
"Operating System :: Microsoft :: Windows :: Windows Server 2012",
"Operating System :: POSIX :: Linux"
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Topic :: Office/Business :: Financial :: Investment",
"Programming Language :: Python :: Implementation :: CPython",
"License :: OSI Approved :: MIT License",
"Natural Language :: Chinese (Simplified)",
"Natural Language :: Chinese (Simplified)"
],
ext_modules=get_ext_modules(),
)
|
src/oci/certificates_management/models/update_certificate_authority_config_details.py | ezequielramos/oci-python-sdk | 249 | 12611421 | <reponame>ezequielramos/oci-python-sdk<filename>src/oci/certificates_management/models/update_certificate_authority_config_details.py
# coding: utf-8
# Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class UpdateCertificateAuthorityConfigDetails(object):
"""
The configuration details for updating a certificate authority (CA).
"""
#: A constant which can be used with the config_type property of a UpdateCertificateAuthorityConfigDetails.
#: This constant has a value of "ROOT_CA_GENERATED_INTERNALLY"
CONFIG_TYPE_ROOT_CA_GENERATED_INTERNALLY = "ROOT_CA_GENERATED_INTERNALLY"
#: A constant which can be used with the config_type property of a UpdateCertificateAuthorityConfigDetails.
#: This constant has a value of "SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA"
CONFIG_TYPE_SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA = "SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA"
#: A constant which can be used with the stage property of a UpdateCertificateAuthorityConfigDetails.
#: This constant has a value of "CURRENT"
STAGE_CURRENT = "CURRENT"
#: A constant which can be used with the stage property of a UpdateCertificateAuthorityConfigDetails.
#: This constant has a value of "PENDING"
STAGE_PENDING = "PENDING"
def __init__(self, **kwargs):
"""
Initializes a new UpdateCertificateAuthorityConfigDetails object with values from keyword arguments. This class has the following subclasses and if you are using this class as input
to a service operations then you should favor using a subclass over the base class:
* :class:`~oci.certificates_management.models.UpdateSubordinateCaIssuedByInternalCaConfigDetails`
* :class:`~oci.certificates_management.models.UpdateRootCaByGeneratingInternallyConfigDetails`
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param config_type:
The value to assign to the config_type property of this UpdateCertificateAuthorityConfigDetails.
Allowed values for this property are: "ROOT_CA_GENERATED_INTERNALLY", "SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA"
:type config_type: str
:param version_name:
The value to assign to the version_name property of this UpdateCertificateAuthorityConfigDetails.
:type version_name: str
:param stage:
The value to assign to the stage property of this UpdateCertificateAuthorityConfigDetails.
Allowed values for this property are: "CURRENT", "PENDING"
:type stage: str
"""
self.swagger_types = {
'config_type': 'str',
'version_name': 'str',
'stage': 'str'
}
self.attribute_map = {
'config_type': 'configType',
'version_name': 'versionName',
'stage': 'stage'
}
self._config_type = None
self._version_name = None
self._stage = None
@staticmethod
def get_subtype(object_dictionary):
"""
Given the hash representation of a subtype of this class,
use the info in the hash to return the class of the subtype.
"""
type = object_dictionary['configType']
if type == 'SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA':
return 'UpdateSubordinateCaIssuedByInternalCaConfigDetails'
if type == 'ROOT_CA_GENERATED_INTERNALLY':
return 'UpdateRootCaByGeneratingInternallyConfigDetails'
else:
return 'UpdateCertificateAuthorityConfigDetails'
@property
def config_type(self):
"""
**[Required]** Gets the config_type of this UpdateCertificateAuthorityConfigDetails.
The origin of the CA.
Allowed values for this property are: "ROOT_CA_GENERATED_INTERNALLY", "SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA"
:return: The config_type of this UpdateCertificateAuthorityConfigDetails.
:rtype: str
"""
return self._config_type
@config_type.setter
def config_type(self, config_type):
"""
Sets the config_type of this UpdateCertificateAuthorityConfigDetails.
The origin of the CA.
:param config_type: The config_type of this UpdateCertificateAuthorityConfigDetails.
:type: str
"""
allowed_values = ["ROOT_CA_GENERATED_INTERNALLY", "SUBORDINATE_CA_ISSUED_BY_INTERNAL_CA"]
if not value_allowed_none_or_none_sentinel(config_type, allowed_values):
raise ValueError(
"Invalid value for `config_type`, must be None or one of {0}"
.format(allowed_values)
)
self._config_type = config_type
@property
def version_name(self):
"""
Gets the version_name of this UpdateCertificateAuthorityConfigDetails.
The name of the CA version. When the value is not null, a name is unique across versions of a given CA.
:return: The version_name of this UpdateCertificateAuthorityConfigDetails.
:rtype: str
"""
return self._version_name
@version_name.setter
def version_name(self, version_name):
"""
Sets the version_name of this UpdateCertificateAuthorityConfigDetails.
The name of the CA version. When the value is not null, a name is unique across versions of a given CA.
:param version_name: The version_name of this UpdateCertificateAuthorityConfigDetails.
:type: str
"""
self._version_name = version_name
@property
def stage(self):
"""
Gets the stage of this UpdateCertificateAuthorityConfigDetails.
The rotation state of the CA. The default is `PENDING`, meaning that the CA is staged and available for use. A CA version
that you mark as `CURRENT` is currently in use, but you don't yet want to rotate it into current, active use. For example,
you might create or update a CA and mark its rotation state as `PENDING` if you haven't yet updated the certificate on the target system.
Allowed values for this property are: "CURRENT", "PENDING"
:return: The stage of this UpdateCertificateAuthorityConfigDetails.
:rtype: str
"""
return self._stage
@stage.setter
def stage(self, stage):
"""
Sets the stage of this UpdateCertificateAuthorityConfigDetails.
The rotation state of the CA. The default is `PENDING`, meaning that the CA is staged and available for use. A CA version
that you mark as `CURRENT` is currently in use, but you don't yet want to rotate it into current, active use. For example,
you might create or update a CA and mark its rotation state as `PENDING` if you haven't yet updated the certificate on the target system.
:param stage: The stage of this UpdateCertificateAuthorityConfigDetails.
:type: str
"""
allowed_values = ["CURRENT", "PENDING"]
if not value_allowed_none_or_none_sentinel(stage, allowed_values):
raise ValueError(
"Invalid value for `stage`, must be None or one of {0}"
.format(allowed_values)
)
self._stage = stage
def __repr__(self):
return formatted_flat_dict(self)
def __eq__(self, other):
if other is None:
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
|
metadata-ingestion/tests/unit/test_mce_builder.py | pramodbiligiri/datahub | 3,586 | 12611436 | import datahub.emitter.mce_builder as builder
from datahub.metadata.schema_classes import (
DataFlowInfoClass,
DatasetPropertiesClass,
DatasetSnapshotClass,
MetadataChangeEventClass,
OwnershipClass,
)
def test_can_add_aspect():
dataset_mce: MetadataChangeEventClass = builder.make_lineage_mce(
[
builder.make_dataset_urn("bigquery", "upstream1"),
builder.make_dataset_urn("bigquery", "upstream2"),
],
builder.make_dataset_urn("bigquery", "downstream"),
)
assert isinstance(dataset_mce.proposedSnapshot, DatasetSnapshotClass)
assert builder.can_add_aspect(dataset_mce, DatasetPropertiesClass)
assert builder.can_add_aspect(dataset_mce, OwnershipClass)
assert not builder.can_add_aspect(dataset_mce, DataFlowInfoClass)
|
recipes/Python/577339_Random_Passwords/recipe-577339.py | tdiprima/code | 2,023 | 12611442 | <filename>recipes/Python/577339_Random_Passwords/recipe-577339.py
import random
import string
import time
def mkpass(size=16):
chars = []
chars.extend([i for i in string.ascii_letters])
chars.extend([i for i in string.digits])
chars.extend([i for i in '\'"!@#$%&*()-_=+[{}]~^,<.>;:/?'])
passwd = ''
for i in range(size):
passwd += chars[random.randint(0, len(chars) - 1)]
random.seed = int(time.time())
random.shuffle(chars)
return passwd
|
scipy/fftpack/_basic.py | jake-is-ESD-protected/scipy | 9,095 | 12611451 | <gh_stars>1000+
"""
Discrete Fourier Transforms - _basic.py
"""
# Created by <NAME>, August,September 2002
__all__ = ['fft','ifft','fftn','ifftn','rfft','irfft',
'fft2','ifft2']
from scipy.fft import _pocketfft
from ._helper import _good_shape
def fft(x, n=None, axis=-1, overwrite_x=False):
"""
Return discrete Fourier transform of real or complex sequence.
The returned complex array contains ``y(0), y(1),..., y(n-1)``, where
``y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum()``.
Parameters
----------
x : array_like
Array to Fourier transform.
n : int, optional
Length of the Fourier transform. If ``n < x.shape[axis]``, `x` is
truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
default results in ``n = x.shape[axis]``.
axis : int, optional
Axis along which the fft's are computed; the default is over the
last axis (i.e., ``axis=-1``).
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed; the default is False.
Returns
-------
z : complex ndarray
with the elements::
[y(0),y(1),..,y(n/2),y(1-n/2),...,y(-1)] if n is even
[y(0),y(1),..,y((n-1)/2),y(-(n-1)/2),...,y(-1)] if n is odd
where::
y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k* 2*pi/n), j = 0..n-1
See Also
--------
ifft : Inverse FFT
rfft : FFT of a real sequence
Notes
-----
The packing of the result is "standard": If ``A = fft(a, n)``, then
``A[0]`` contains the zero-frequency term, ``A[1:n/2]`` contains the
positive-frequency terms, and ``A[n/2:]`` contains the negative-frequency
terms, in order of decreasingly negative frequency. So ,for an 8-point
transform, the frequencies of the result are [0, 1, 2, 3, -4, -3, -2, -1].
To rearrange the fft output so that the zero-frequency component is
centered, like [-4, -3, -2, -1, 0, 1, 2, 3], use `fftshift`.
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non-floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
This function is most efficient when `n` is a power of two, and least
efficient when `n` is prime.
Note that if ``x`` is real-valued, then ``A[j] == A[n-j].conjugate()``.
If ``x`` is real-valued and ``n`` is even, then ``A[n/2]`` is real.
If the data type of `x` is real, a "real FFT" algorithm is automatically
used, which roughly halves the computation time. To increase efficiency
a little further, use `rfft`, which does the same calculation, but only
outputs half of the symmetrical spectrum. If the data is both real and
symmetrical, the `dct` can again double the efficiency by generating
half of the spectrum from half of the signal.
Examples
--------
>>> from scipy.fftpack import fft, ifft
>>> x = np.arange(5)
>>> np.allclose(fft(ifft(x)), x, atol=1e-15) # within numerical accuracy.
True
"""
return _pocketfft.fft(x, n, axis, None, overwrite_x)
def ifft(x, n=None, axis=-1, overwrite_x=False):
"""
Return discrete inverse Fourier transform of real or complex sequence.
The returned complex array contains ``y(0), y(1),..., y(n-1)``, where
``y(j) = (x * exp(2*pi*sqrt(-1)*j*np.arange(n)/n)).mean()``.
Parameters
----------
x : array_like
Transformed data to invert.
n : int, optional
Length of the inverse Fourier transform. If ``n < x.shape[axis]``,
`x` is truncated. If ``n > x.shape[axis]``, `x` is zero-padded.
The default results in ``n = x.shape[axis]``.
axis : int, optional
Axis along which the ifft's are computed; the default is over the
last axis (i.e., ``axis=-1``).
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed; the default is False.
Returns
-------
ifft : ndarray of floats
The inverse discrete Fourier transform.
See Also
--------
fft : Forward FFT
Notes
-----
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non-floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
This function is most efficient when `n` is a power of two, and least
efficient when `n` is prime.
If the data type of `x` is real, a "real IFFT" algorithm is automatically
used, which roughly halves the computation time.
Examples
--------
>>> from scipy.fftpack import fft, ifft
>>> import numpy as np
>>> x = np.arange(5)
>>> np.allclose(ifft(fft(x)), x, atol=1e-15) # within numerical accuracy.
True
"""
return _pocketfft.ifft(x, n, axis, None, overwrite_x)
def rfft(x, n=None, axis=-1, overwrite_x=False):
"""
Discrete Fourier transform of a real sequence.
Parameters
----------
x : array_like, real-valued
The data to transform.
n : int, optional
Defines the length of the Fourier transform. If `n` is not specified
(the default) then ``n = x.shape[axis]``. If ``n < x.shape[axis]``,
`x` is truncated, if ``n > x.shape[axis]``, `x` is zero-padded.
axis : int, optional
The axis along which the transform is applied. The default is the
last axis.
overwrite_x : bool, optional
If set to true, the contents of `x` can be overwritten. Default is
False.
Returns
-------
z : real ndarray
The returned real array contains::
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2))] if n is even
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2))] if n is odd
where::
y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
j = 0..n-1
See Also
--------
fft, irfft, scipy.fft.rfft
Notes
-----
Within numerical accuracy, ``y == rfft(irfft(y))``.
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non-floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
To get an output with a complex datatype, consider using the newer
function `scipy.fft.rfft`.
Examples
--------
>>> from scipy.fftpack import fft, rfft
>>> a = [9, -9, 1, 3]
>>> fft(a)
array([ 4. +0.j, 8.+12.j, 16. +0.j, 8.-12.j])
>>> rfft(a)
array([ 4., 8., 12., 16.])
"""
return _pocketfft.rfft_fftpack(x, n, axis, None, overwrite_x)
def irfft(x, n=None, axis=-1, overwrite_x=False):
"""
Return inverse discrete Fourier transform of real sequence x.
The contents of `x` are interpreted as the output of the `rfft`
function.
Parameters
----------
x : array_like
Transformed data to invert.
n : int, optional
Length of the inverse Fourier transform.
If n < x.shape[axis], x is truncated.
If n > x.shape[axis], x is zero-padded.
The default results in n = x.shape[axis].
axis : int, optional
Axis along which the ifft's are computed; the default is over
the last axis (i.e., axis=-1).
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed; the default is False.
Returns
-------
irfft : ndarray of floats
The inverse discrete Fourier transform.
See Also
--------
rfft, ifft, scipy.fft.irfft
Notes
-----
The returned real array contains::
[y(0),y(1),...,y(n-1)]
where for n is even::
y(j) = 1/n (sum[k=1..n/2-1] (x[2*k-1]+sqrt(-1)*x[2*k])
* exp(sqrt(-1)*j*k* 2*pi/n)
+ c.c. + x[0] + (-1)**(j) x[n-1])
and for n is odd::
y(j) = 1/n (sum[k=1..(n-1)/2] (x[2*k-1]+sqrt(-1)*x[2*k])
* exp(sqrt(-1)*j*k* 2*pi/n)
+ c.c. + x[0])
c.c. denotes complex conjugate of preceding expression.
For details on input parameters, see `rfft`.
To process (conjugate-symmetric) frequency-domain data with a complex
datatype, consider using the newer function `scipy.fft.irfft`.
Examples
--------
>>> from scipy.fftpack import rfft, irfft
>>> a = [1.0, 2.0, 3.0, 4.0, 5.0]
>>> irfft(a)
array([ 2.6 , -3.16405192, 1.24398433, -1.14955713, 1.46962473])
>>> irfft(rfft(a))
array([1., 2., 3., 4., 5.])
"""
return _pocketfft.irfft_fftpack(x, n, axis, None, overwrite_x)
def fftn(x, shape=None, axes=None, overwrite_x=False):
"""
Return multidimensional discrete Fourier transform.
The returned array contains::
y[j_1,..,j_d] = sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
x[k_1,..,k_d] * prod[i=1..d] exp(-sqrt(-1)*2*pi/n_i * j_i * k_i)
where d = len(x.shape) and n = x.shape.
Parameters
----------
x : array_like
The (N-D) array to transform.
shape : int or array_like of ints or None, optional
The shape of the result. If both `shape` and `axes` (see below) are
None, `shape` is ``x.shape``; if `shape` is None but `axes` is
not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
length ``shape[i]``.
If any element of `shape` is -1, the size of the corresponding
dimension of `x` is used.
axes : int or array_like of ints or None, optional
The axes of `x` (`y` if `shape` is not None) along which the
transform is applied.
The default is over all axes.
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed. Default is False.
Returns
-------
y : complex-valued N-D NumPy array
The (N-D) DFT of the input array.
See Also
--------
ifftn
Notes
-----
If ``x`` is real-valued, then
``y[..., j_i, ...] == y[..., n_i-j_i, ...].conjugate()``.
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non-floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
Examples
--------
>>> from scipy.fftpack import fftn, ifftn
>>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
>>> np.allclose(y, fftn(ifftn(y)))
True
"""
shape = _good_shape(x, shape, axes)
return _pocketfft.fftn(x, shape, axes, None, overwrite_x)
def ifftn(x, shape=None, axes=None, overwrite_x=False):
"""
Return inverse multidimensional discrete Fourier transform.
The sequence can be of an arbitrary type.
The returned array contains::
y[j_1,..,j_d] = 1/p * sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
x[k_1,..,k_d] * prod[i=1..d] exp(sqrt(-1)*2*pi/n_i * j_i * k_i)
where ``d = len(x.shape)``, ``n = x.shape``, and ``p = prod[i=1..d] n_i``.
For description of parameters see `fftn`.
See Also
--------
fftn : for detailed information.
Examples
--------
>>> from scipy.fftpack import fftn, ifftn
>>> import numpy as np
>>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
>>> np.allclose(y, ifftn(fftn(y)))
True
"""
shape = _good_shape(x, shape, axes)
return _pocketfft.ifftn(x, shape, axes, None, overwrite_x)
def fft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
"""
2-D discrete Fourier transform.
Return the 2-D discrete Fourier transform of the 2-D argument
`x`.
See Also
--------
fftn : for detailed information.
Examples
--------
>>> from scipy.fftpack import fft2, ifft2
>>> y = np.mgrid[:5, :5][0]
>>> y
array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]])
>>> np.allclose(y, ifft2(fft2(y)))
True
"""
return fftn(x,shape,axes,overwrite_x)
def ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
"""
2-D discrete inverse Fourier transform of real or complex sequence.
Return inverse 2-D discrete Fourier transform of
arbitrary type sequence x.
See `ifft` for more information.
See Also
--------
fft2, ifft
Examples
--------
>>> from scipy.fftpack import fft2, ifft2
>>> y = np.mgrid[:5, :5][0]
>>> y
array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]])
>>> np.allclose(y, fft2(ifft2(y)))
True
"""
return ifftn(x,shape,axes,overwrite_x)
|
koku/masu/test/external/downloader/aws/__init__.py | rubik-ai/koku | 157 | 12611454 | """AWS downloader tests."""
import random
import string
import faker
def fake_aws_account_id():
"""Generate a dummy AWS AwsAccount ID for testing purposes."""
return "".join(random.choice(string.digits) for _ in range(12))
def fake_arn(account_id="", service="fakeservice", region="", resource_separator=":", generate_account_id=False):
"""
Generate a dummy AWS ARN for testing purposes.
account_id argument is optional, and will be randomly generated if None.
Args:
account_id (str): Optional account ID.
service (str): Optional service name
region (str): Optional region
resource_separator (str): A colon ':' or a forward-slash '/'
generate_account_id (bool): Whether to generate a random account_id,
This will override any account_id that is passed in
Returns:
str: A well-formed, randomized ARN.
"""
if generate_account_id:
account_id = fake_aws_account_id()
resource = faker.Faker().name()
resource_type = faker.Faker().name().replace(" ", "_")
arn = ("arn:aws:{0}:{1}:{2}:{3}{4}{5}").format(
service, region, account_id, resource_type, resource_separator, resource
)
return arn
|
tests/chainer_tests/testing_tests/test_function_link.py | zjzh/chainer | 3,705 | 12611457 | import unittest
import numpy
import pytest
import six
import chainer
from chainer import initializers
from chainer import testing
from chainer import utils
import chainerx
# Utilities for contiguousness tests.
#
# These tests checks incoming array contiguousness.
# As it's not possible to assume contiguousness of incoming arrays consistently
# (because gradient_check passes contiguous arrays in numerical_grad),
# we instead simulate the test failure. The function implementation raises an
# error if an incoming array matches the expected contiguousness and we expect
# the failure.
class _ContiguousnessMatched(Exception):
pass
def _is_f_contiguous(shape, strides, itemsize):
if numpy.prod(shape) <= 1:
return True
for sh, st in zip(shape, reversed(strides)):
if sh == 1:
continue
if st != itemsize:
return False
itemsize *= sh
return True
def _get_contiguousness(arr):
if isinstance(arr, chainerx.ndarray):
c_contig = arr.is_contiguous
f_contig = _is_f_contiguous(
arr.shape, arr.strides, arr.itemsize)
return (c_contig, f_contig)
return (arr.flags.c_contiguous, arr.flags.f_contiguous)
def _check_contiguousness(arr, expected_contiguous):
if isinstance(arr, chainer.Variable):
_check_contiguousness(arr.array, expected_contiguous)
return
c_contig, f_contig = _get_contiguousness(arr)
if numpy.prod(arr.shape) <= 1:
return # not applicable for this shape
if expected_contiguous is None:
# expected to be non-contiguous
if not c_contig and not f_contig:
raise _ContiguousnessMatched()
elif expected_contiguous == 'C':
# expected to be C-contiguous
if c_contig:
raise _ContiguousnessMatched()
else:
assert False
def _check_grad(grad, expect_grad_none, class_or_tuple):
if expect_grad_none:
assert grad is None
else:
isinstance(grad, class_or_tuple)
def _check_grads(grads, expect_grads_none, class_or_tuple):
for grad, expect_grad_none in six.moves.zip(grads, expect_grads_none):
_check_grad(grad, expect_grad_none, class_or_tuple)
_inject_backend_tests = testing.inject_backend_tests(
None,
[
# CPU tests
{},
{'use_ideep': 'always'},
# GPU tests
{'use_cuda': True},
{'use_cuda': True, 'cuda_device': 1},
# ChainerX tests
{'use_chainerx': True, 'chainerx_device': 'native:0'},
{'use_chainerx': True, 'chainerx_device': 'cuda:0'},
{'use_chainerx': True, 'chainerx_device': 'cuda:1'},
])
def _forward_correct(x1, x2):
dt = x1.dtype.type
y1 = (x1 + x2) ** dt(2)
y2 = (x1 ** dt(2)) * (x2 ** dt(2))
return utils.force_array(y1), utils.force_array(y2)
def _backward_correct(x1, x2, gy1, gy2):
dt = x1.dtype.type
ggx1 = (
+ gy1 * dt(2) * (x1 + x2)
+ gy2 * dt(2) * x1 * x2 ** dt(2))
ggx2 = (
+ gy1 * dt(2) * (x1 + x2)
+ gy2 * dt(2) * x1 ** dt(2) * x2)
return ggx1, ggx2
def _double_backward_correct(x1, x2, gy1, gy2, ggx1, ggx2):
dt = x1.dtype.type
ggy1 = (ggx1 + ggx2) * dt(2) * (x1 + x2)
ggy2 = (ggx1 * x2 + ggx2 * x1) * dt(2) * x1 * x2
gx1 = (
+ ggx1 * (dt(2) * gy1 + dt(2) * x2 ** dt(2) * gy2)
+ ggx2 * (dt(2) * gy1 + dt(4) * x1 * x2 * gy2))
gx2 = (
+ ggx1 * (dt(2) * gy1 + dt(4) * x1 * x2 * gy2)
+ ggx2 * (dt(2) * gy1 + dt(2) * x1 ** dt(2) * gy2))
return gx1, gx2, ggy1, ggy2
# TestFunctionTestSuccessful
#
# This test checks for successful case.
# Incoming array types are also checked.
class FuncCorrectlyImplemented(chainer.FunctionNode):
def __init__(
self, device,
expect_grad_outputs_none=(False, False),
expect_grad_grad_inputs_none=(False, False)):
self.device = device
self.expect_grad_outputs_none = expect_grad_outputs_none
self.expect_grad_grad_inputs_none = expect_grad_grad_inputs_none
def forward(self, inputs):
device = self.device
x1, x2 = inputs
if device.xp is chainerx:
fallback_device = device.fallback_device
assert isinstance(x1, fallback_device.supported_array_types)
assert isinstance(x2, fallback_device.supported_array_types)
self.retain_inputs((0, 1))
y1, y2 = _forward_correct(x1, x2)
return utils.force_array(y1), utils.force_array(y2)
def backward(self, indexes, grad_outputs):
device = self.device
_check_grads(
grad_outputs, self.expect_grad_outputs_none,
device.supported_array_types)
x1, x2 = self.get_retained_inputs()
gy1, gy2 = grad_outputs
assert isinstance(x1.array, device.supported_array_types)
assert isinstance(x2.array, device.supported_array_types)
grad_func = FuncGradCorrectlyImplemented(
device,
self.expect_grad_outputs_none,
self.expect_grad_grad_inputs_none)
return grad_func.apply((x1, x2, gy1, gy2))
class FuncGradCorrectlyImplemented(chainer.FunctionNode):
def __init__(
self, device,
expect_grad_outputs_none,
expect_grad_grad_inputs_none):
self.device = device
self.expect_grad_outputs_none = expect_grad_outputs_none
self.expect_grad_grad_inputs_none = expect_grad_grad_inputs_none
def forward(self, inputs_and_grad_outputs):
device = self.device
x1, x2, gy1, gy2 = inputs_and_grad_outputs
if device.xp is chainerx:
fallback_device = device.fallback_device
_check_grads(
(gy1, gy2), self.expect_grad_outputs_none,
fallback_device.supported_array_types)
self.retain_inputs((0, 1, 2, 3))
ggx1, ggx2 = _backward_correct(
x1, x2,
0 if self.expect_grad_outputs_none[0] else gy1,
0 if self.expect_grad_outputs_none[1] else gy2)
return utils.force_array(ggx1), utils.force_array(ggx2)
def backward(self, indexes, grad_grad_inputs):
device = self.device
_check_grads(
grad_grad_inputs, self.expect_grad_grad_inputs_none,
chainer.Variable)
ggx1, ggx2 = grad_grad_inputs
x1, x2, gy1, gy2 = self.get_retained_inputs()
assert isinstance(x1, chainer.Variable)
assert isinstance(x2, chainer.Variable)
assert isinstance(x1.array, device.supported_array_types)
assert isinstance(x2.array, device.supported_array_types)
_check_grads(
(gy1, gy2), self.expect_grad_outputs_none, chainer.Variable)
if not self.expect_grad_outputs_none[0]:
isinstance(gy1.array, device.supported_array_types)
if not self.expect_grad_outputs_none[1]:
isinstance(gy2.array, device.supported_array_types)
gx1, gx2, ggy1, ggy2 = _double_backward_correct(
x1, x2,
0 if self.expect_grad_outputs_none[0] else gy1,
0 if self.expect_grad_outputs_none[1] else gy2,
0 if self.expect_grad_grad_inputs_none[0] else ggx1,
0 if self.expect_grad_grad_inputs_none[1] else ggx2)
return gx1, gx2, ggy1, ggy2
@testing.parameterize(*testing.product({
'shape': [(3, 2), (2,), (1,), (), (2, 0, 3)],
}))
@_inject_backend_tests
class TestFunctionTestSuccessful(testing.FunctionTestCase):
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
return x1, x2
def forward(self, inputs, device):
func = FuncCorrectlyImplemented(device)
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
@_inject_backend_tests
class TestFunctionTestSuccessfulNoneGrads(testing.FunctionTestCase):
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
return x1, x2
def generate_grad_outputs(self, output_templates):
grad_outputs = (
None,
(numpy.random.uniform(-1, 1, output_templates[1].shape)
.astype(output_templates[1].dtype)))
return grad_outputs
def generate_grad_grad_inputs(self, input_templates):
grad_inputs = (
(numpy.random.uniform(-1, 1, input_templates[0].shape)
.astype(input_templates[0].dtype)),
None)
return grad_inputs
def forward(self, inputs, device):
func = FuncCorrectlyImplemented(
device,
expect_grad_outputs_none=(True, False),
expect_grad_grad_inputs_none=(False, True))
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
# TestFunctionTestIncorrectForward
#
# This test checks if it can detect incorrect forward implementation.
class FuncWithIncorrectForward(chainer.FunctionNode):
def forward(self, inputs):
x1, x2 = inputs
y1, y2 = _forward_correct(x1, x2)
y1, y2 = utils.force_array(y1), utils.force_array(y2)
y2[...] += 1 # ! make incorrect
return y1, y2
def backward(self, *args, **kwargs):
assert False # should never be called
@testing.parameterize(*testing.product({
'shape': [(3, 2), (2,), (1,), ()],
}))
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.FunctionTestError)
class TestFunctionTestIncorrectForward(testing.FunctionTestCase):
skip_backward_test = True
skip_double_backward_test = True
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
return x1, x2
def forward(self, inputs, device):
func = FuncWithIncorrectForward()
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
# TestFunctionTestIncorrectBackward
#
# This test checks if it can detect incorrect backward implementation.
class FuncWithIncorrectBackward(chainer.FunctionNode):
def __init__(self, expect_grad_outputs_none=(False, False)):
self.expect_grad_outputs_none = expect_grad_outputs_none
def forward(self, inputs):
x1, x2 = inputs
y1, y2 = _forward_correct(x1, x2)
self.retain_inputs((0, 1))
return utils.force_array(y1), utils.force_array(y2)
def backward(self, indexes, grad_outputs):
gy1, gy2 = grad_outputs
x1, x2 = self.get_retained_inputs()
ggx1, ggx2 = _backward_correct(
x1, x2,
0 if self.expect_grad_outputs_none[0] else gy1,
0 if self.expect_grad_outputs_none[1] else gy2)
ggx1 = ggx1 + 100000
ggx2 = ggx2 + 10000 # ! make incorrect
return utils.force_array(ggx1), utils.force_array(ggx2)
@testing.parameterize(*testing.product({
'shape': [(3, 2), (2,), (1,), ()],
}))
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.FunctionTestError)
class TestFunctionTestIncorrectBackward(testing.FunctionTestCase):
skip_forward_test = True
skip_double_backward_test = True
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
return x1, x2
def forward(self, inputs, device):
func = FuncWithIncorrectBackward()
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.FunctionTestError)
class TestFunctionTestIncorrectBackwardNoneGrads(testing.FunctionTestCase):
skip_forward_test = True
skip_double_backward_test = True
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
return x1, x2
def generate_grad_outputs(self, output_templates):
grad_outputs = (
None,
(numpy.random.uniform(-1, 1, output_templates[1].shape)
.astype(output_templates[1].dtype)))
return grad_outputs
def forward(self, inputs, device):
func = FuncWithIncorrectBackward(
expect_grad_outputs_none=(True, False))
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
# TestFunctionTestIncorrectDoubleBackward
#
# This test checks if it can detect incorrect double backward implementation.
class FuncWithIncorrectDoubleBackward(chainer.FunctionNode):
def __init__(
self,
expect_grad_outputs_none=(False, False),
expect_grad_grad_inputs_none=(False, False)):
self.expect_grad_outputs_none = expect_grad_outputs_none
self.expect_grad_grad_inputs_none = expect_grad_grad_inputs_none
def forward(self, inputs):
x1, x2 = inputs
y1, y2 = _forward_correct(x1, x2)
self.retain_inputs((0, 1))
return utils.force_array(y1), utils.force_array(y2)
def backward(self, indexes, grad_outputs):
x1, x2 = self.get_retained_inputs()
gy1, gy2 = grad_outputs
grad_func = FuncGradWithIncorrectDoubleBackward(
expect_grad_outputs_none=self.expect_grad_outputs_none,
expect_grad_grad_inputs_none=self.expect_grad_grad_inputs_none)
return grad_func.apply((x1, x2, gy1, gy2))
class FuncGradWithIncorrectDoubleBackward(chainer.FunctionNode):
def __init__(
self,
expect_grad_outputs_none=(False, False),
expect_grad_grad_inputs_none=(False, False)):
self.expect_grad_outputs_none = expect_grad_outputs_none
self.expect_grad_grad_inputs_none = expect_grad_grad_inputs_none
def forward(self, inputs_and_grad_outputs):
x1, x2, gy1, gy2 = inputs_and_grad_outputs
self.retain_inputs((0, 1, 2, 3))
ggx1, ggx2 = _backward_correct(
x1, x2,
0 if self.expect_grad_outputs_none[0] else gy1,
0 if self.expect_grad_outputs_none[1] else gy2)
return utils.force_array(ggx1), utils.force_array(ggx2)
def backward(self, indexes, grad_grad_inputs):
ggx1, ggx2 = grad_grad_inputs
x1, x2, gy1, gy2 = self.get_retained_inputs()
gx1, gx2, ggy1, ggy2 = _double_backward_correct(
x1, x2,
0 if self.expect_grad_outputs_none[0] else gy1,
0 if self.expect_grad_outputs_none[1] else gy2,
0 if self.expect_grad_grad_inputs_none[0] else ggx1,
0 if self.expect_grad_grad_inputs_none[1] else ggx2)
ggy2 = ggy2 + 10000 # ! make incorrect
return gx1, gx2, ggy1, ggy2
@testing.parameterize(*testing.product({
'shape': [(3, 2), (2,), (1,), ()],
}))
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.FunctionTestError)
class TestFunctionTestIncorrectDoubleBackward(testing.FunctionTestCase):
skip_forward_test = True
skip_backward_test = True
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
return x1, x2
def forward(self, inputs, device):
func = FuncWithIncorrectDoubleBackward()
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.FunctionTestError)
class TestFunctionTestIncorrectDoubleBackwardNoneGrads(
testing.FunctionTestCase):
skip_forward_test = True
skip_backward_test = True
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
return x1, x2
def generate_grad_outputs(self, output_templates):
grad_outputs = (
None,
(numpy.random.uniform(-1, 1, output_templates[1].shape)
.astype(output_templates[1].dtype)))
return grad_outputs
def generate_grad_grad_inputs(self, input_templates):
grad_inputs = (
(numpy.random.uniform(-1, 1, input_templates[0].shape)
.astype(input_templates[0].dtype)),
None)
return grad_inputs
def forward(self, inputs, device):
func = FuncWithIncorrectDoubleBackward(
expect_grad_outputs_none=(True, False),
expect_grad_grad_inputs_none=(False, True))
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
# FunctionTestCaseArrayContiguousnessTest
class FuncWithContiguousnessCheck(chainer.FunctionNode):
def __init__(self, contiguous, check_on):
self.contiguous = contiguous
self.check_on = check_on
def _check_contiguousness(self, arr):
assert isinstance(arr, chainer.get_array_types())
_check_contiguousness(arr, self.contiguous)
def forward(self, inputs):
x1, x2 = inputs
if self.check_on == 'forward_input':
self._check_contiguousness(x1)
self._check_contiguousness(x2)
self.retain_inputs((0, 1))
y1, y2 = _forward_correct(x1, x2)
return utils.force_array(y1), utils.force_array(y2)
def backward(self, indexes, grad_outputs):
x1, x2 = self.get_retained_inputs()
gy1, gy2 = grad_outputs
if self.check_on == 'backward_retained_input':
self._check_contiguousness(x1.array)
self._check_contiguousness(x2.array)
elif self.check_on == 'backward_grad_output':
self._check_contiguousness(gy1.array)
self._check_contiguousness(gy2.array)
grad_func = FuncGradWithContiguousnessCheck(
self.contiguous, self.check_on)
return grad_func.apply((x1, x2, gy1, gy2))
class FuncGradWithContiguousnessCheck(chainer.FunctionNode):
def __init__(self, contiguous, check_on):
self.contiguous = contiguous
self.check_on = check_on
def _check_contiguousness(self, arr):
testing.function_link._check_contiguousness(arr, self.contiguous)
def forward(self, inputs_and_grad_outputs):
x1, x2, gy1, gy2 = inputs_and_grad_outputs
self.retain_inputs((0, 1, 2, 3))
ggx1, ggx2 = _backward_correct(x1, x2, gy1, gy2)
return utils.force_array(ggx1), utils.force_array(ggx2)
def backward(self, indexes, grad_grad_inputs):
ggx1, ggx2 = grad_grad_inputs
if self.check_on == 'double_backward_grad_grad_input':
self._check_contiguousness(ggx1)
self._check_contiguousness(ggx2)
x1, x2, gy1, gy2 = self.get_retained_inputs()
gx1, gx2, ggy1, ggy2 = _double_backward_correct(
x1, x2, gy1, gy2, ggx1, ggx2)
return gx1, gx2, ggy1, ggy2
@testing.parameterize(*testing.product({
'shape': [(3, 2), (2,), (1, 2)],
'contiguous': [None, 'C'],
'check_on': [ # Check points in which contiguousness is probed.
'forward_input',
# TODO(niboshi): As gradient_check.check_backward currently copies the
# grads without preserving strides, they cannot be non-contiguous.
# Enable this check after check_backward will be fixed.
# 'backward_grad_output',
'backward_retained_input',
# TODO(niboshi): Enable this check after check_backward will be fixed.
# 'double_backward_grad_grad_input',
]}))
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=_ContiguousnessMatched)
class FunctionTestCaseArrayContiguousnessTest(testing.FunctionTestCase):
def generate_inputs(self):
x1 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
x2 = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
return x1, x2
def forward(self, inputs, device):
func = FuncWithContiguousnessCheck(self.contiguous, self.check_on)
return func.apply(inputs)
def forward_expected(self, inputs):
return _forward_correct(*inputs)
def before_test(self, test_name):
# Some combinations of test methods and check points are irrelevant.
# Skip such combinations.
# For example, `test_forward` method does not generate grad_outputs.
if test_name == 'test_forward':
if self.check_on != 'forward_input':
raise unittest.SkipTest()
if test_name == 'test_backward':
if self.check_on == 'double_backward_grad_grad_input':
raise unittest.SkipTest()
class Dot(chainer.FunctionNode):
def __init__(
self, incorrect_forward=False, incorrect_backward_gx=False,
incorrect_backward_gp=False, contiguous=None,
check_on=None):
self.incorrect_forward = incorrect_forward
self.incorrect_backward_gx = incorrect_backward_gx
self.incorrect_backward_gp = incorrect_backward_gp
self.contiguous = contiguous
self.check_on = check_on
def forward(self, inputs):
self.retain_inputs((0, 1))
xp = chainer.backend.get_array_module(*inputs)
x, p = inputs
if self.check_on == 'forward_input':
self._check_contiguousness(x)
self._check_contiguousness(p)
y = xp.dot(x, p)
if self.incorrect_forward:
y *= 9999
return y,
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
x, p = self.get_retained_inputs()
if self.check_on == 'backward_retained_input':
self._check_contiguousness(x.array)
self._check_contiguousness(p.array)
elif self.check_on == 'backward_grad_output':
self._check_contiguousness(gy.array)
gx = chainer.functions.matmul(gy, p.T)
gp = chainer.functions.matmul(x.T, gy)
if self.incorrect_backward_gx:
gx /= 2
if self.incorrect_backward_gp:
gp += 1000
return gx, gp
def _check_contiguousness(self, arr):
assert isinstance(arr, chainer.get_array_types())
_check_contiguousness(arr, self.contiguous)
class DotLink(chainer.Link):
"""correctly implemented dot."""
def __init__(
self, in_size, out_size, initial_p=None, contiguous=None,
check_on=None):
super(DotLink, self).__init__()
with self.init_scope():
if initial_p is None:
initial_p = initializers.Constant(1)
self.p = chainer.Parameter(initial_p, shape=(in_size, out_size))
self.contiguous = contiguous
self.check_on = check_on
def forward(self, inputs):
x = inputs
p = self.p
contiguous = self.contiguous
check_on = self.check_on
y, = Dot(contiguous=contiguous, check_on=check_on).apply((x, p))
return y
class DotLinkIncorrectForward(DotLink):
"""Incorrectly implemented dot (forward)."""
def __init__(self, *args, **kwargs):
super(DotLinkIncorrectForward, self).__init__(*args, **kwargs)
def forward(self, inputs):
x = inputs
p = self.p
y, = Dot(incorrect_forward=True).apply((x, p))
return y
class DotLinkIncorrectBackward(DotLink):
"""Incorrect implementation of dot (backward)."""
def __init__(self, incorrect_gx, incorrect_gp, *args, **kwargs):
super(DotLinkIncorrectBackward, self).__init__(*args, **kwargs)
self.incorrect_gx = incorrect_gx
self.incorrect_gp = incorrect_gp
def forward(self, inputs):
x = inputs
p = self.p
y, = Dot(
incorrect_backward_gx=self.incorrect_gx,
incorrect_backward_gp=self.incorrect_gp).apply((x, p))
return y
class DotLinkIncorrectInitialization(DotLink):
"""Incorrect implementation of dot (parameter initialization)."""
def __init__(self, in_size, out_size, initial_p=None):
# Ignores given initializer here.
super(DotLinkIncorrectInitialization, self).__init__(
in_size, out_size, initializers.Constant(0))
class DotLinkTestBase(object):
param_names = ('p',)
def setUp(self):
self.n = 1
self.in_size = 2
self.out_size = 3
self.dtype = numpy.float32
def generate_params(self):
in_size = self.in_size
out_size = self.out_size
return numpy.random.uniform(
-1, 1, (in_size, out_size)).astype(self.dtype),
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
return DotLink(in_size, out_size, initial_p)
def generate_inputs(self):
return numpy.random.rand(self.n, self.in_size).astype(self.dtype),
# Required for forward backward tests.
def forward_expected(self, link, inputs):
p = link.p.array
x, = inputs
return numpy.dot(x, p),
# Requires for initializers test.
def get_initializers(self):
return [
initializers.Constant(0), 2,
testing.InitializerArgument(None, initializers.Constant(1))],
@_inject_backend_tests
class TestLinkCorrect(DotLinkTestBase, testing.LinkTestCase):
pass
@_inject_backend_tests
class TestLinkInitializersCorrect(
DotLinkTestBase, testing.LinkInitializersTestCase):
pass
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.LinkTestError)
class TestLinkIncorrectForward(DotLinkTestBase, testing.LinkTestCase):
skip_backward_test = True
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
link = DotLinkIncorrectForward(in_size, out_size, initial_p)
return link
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.LinkTestError)
class TestLinkIncorrectBackwardInput(DotLinkTestBase, testing.LinkTestCase):
skip_forward_test = True
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
link = DotLinkIncorrectBackward(
True, False, in_size, out_size, initial_p)
return link
@testing.fix_random()
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.LinkTestError)
class TestLinkIncorrectBackwardParam(DotLinkTestBase, testing.LinkTestCase):
skip_forward_test = True
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
link = DotLinkIncorrectBackward(
False, True, in_size, out_size, initial_p)
return link
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=TypeError)
class TestLinkIncorrectCreateLink(DotLinkTestBase, testing.LinkTestCase):
def create_link(self, initializers):
# Invalid return type (that is not an instance of chainer.Link).
return numpy.array([1])
@testing.parameterize(*testing.product({
'invalid_forward_backward_initializer': [
chainer.Variable(numpy.array([1])),
chainer.Parameter(numpy.array([1])),
]}))
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=TypeError)
class TestLinkIncorrectForwardBackwardInitializers(
DotLinkTestBase, testing.LinkTestCase):
def generate_params(self):
return self.invalid_forward_backward_initializer,
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=testing.LinkTestError)
class TestLinkIncorrectBackwardInitializers(
DotLinkTestBase, testing.LinkInitializersTestCase):
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
link = DotLinkIncorrectInitialization(in_size, out_size, initial_p)
return link
@testing.parameterize(*testing.product({
'invalid_initializer': [
chainer.Variable(numpy.array([1])),
chainer.Parameter(numpy.array([1])),
]}))
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=TypeError)
class TestLinkIncorrectInitializers(
DotLinkTestBase, testing.LinkInitializersTestCase):
def get_initializers(self):
return [self.invalid_initializer],
@testing.parameterize(*testing.product({
'contiguous': [None, 'C'],
'check_on': [ # Check points in which contiguousness is probed.
'forward_input',
# TODO(hvy): As gradient_check.check_backward currently copies the
# grads without preserving strides, they cannot be non-contiguous.
# Enable this check after check_backward will be fixed.
# 'backward_grad_output',
'backward_retained_input',
# TODO(hvy): Enable this check after check_backward will be fixed.
# 'double_backward_grad_grad_input',
]}))
@_inject_backend_tests
@pytest.mark.xfail(strict=True, raises=_ContiguousnessMatched)
class TestLinkContiguousness(DotLinkTestBase, testing.LinkTestCase):
def before_test(self, test_name):
# Some combinations of test methods and check points are irrelevant.
# Skip such combinations.
# For example, `test_forward` method does not generate grad_outputs.
if test_name == 'test_forward':
if self.check_on != 'forward_input':
raise unittest.SkipTest()
def create_link(self, initializers):
initial_p, = initializers
in_size = self.in_size
out_size = self.out_size
contiguous = self.contiguous
check_on = self.check_on
link = DotLink(
in_size, out_size, initial_p, contiguous=contiguous,
check_on=check_on)
return link
testing.run_module(__name__, __file__)
|
ctools/utils/log_helper.py | XinyuJing/DI-star | 267 | 12611487 | <reponame>XinyuJing/DI-star<filename>ctools/utils/log_helper.py
'''
Copyright 2020 Sensetime X-lab. All Rights Reserved
Main Function:
1. log helper, used to help to save logger on terminal, tensorboard or save file.
2. CountVar, to help counting number.
'''
import json
import logging
import numbers
import os
import sys
import cv2
import numpy as np
import yaml
from tabulate import tabulate
from tensorboardX import SummaryWriter
import torch
def build_logger(cfg, name=None, rank=0):
r'''
Overview:
use config to build checkpoint helper. Only rank == 0 can build.
Arguments:
- name (:obj:`str`): the logger file name
- rank (:obj:`int`): only rank == 0 can build, else return TextLogger that only save terminal output
Returns:
- logger (:obj:`TextLogger`): logger that save terminal output
- tb_logger (:obj:`TensorBoardLogger` or :obj:`None`): logger that save output to tensorboard,
if rank != 0 then None
- variable_record (:obj:`VariableRecord` or :obj:`None`): logger that record variable for further process,
if rank != 0 then None
'''
path = cfg.common.save_path
# Note: Only support rank0 tb_logger, variable_record
if rank == 0:
logger = TextLogger(path, name=name)
tb_logger = TensorBoardLogger(path, name=name)
var_record_type = cfg.learner.get("var_record_type", None)
if var_record_type is None:
variable_record = VariableRecord(cfg.learner.log_freq)
else:
raise NotImplementedError("not support var_record_type: {}".format(var_record_type))
return logger, tb_logger, variable_record
else:
logger = TextLogger(path, name=name)
return logger, None, None
def build_logger_naive(path, name, level=logging.INFO, print_freq=1):
r'''
Overview:
use config to build Textlogger and VariableRecord
Arguments:
- path (:obj:`str`): logger's save dir, please reference log_helper.TextLogger
- name (:obj:`str`): the logger file name
- level (:obj:`int` or :obj:`str`): Set the logging level of logger
- rank (:obj:`int`): only rank == 0 can build, else return TextLogger that only save terminal output
Returns:
- logger (:obj:`TextLogger`): logger that save terminal output
- variable_record (:obj:`VariableRecord`): logger that record variable for further process
'''
logger = TextLogger(path, name, level)
variable_record = VariableRecord(print_freq)
return logger, variable_record
def get_default_logger(name=None):
r"""
Overview:
get the logger using logging.getLogger
Arguments:
- name (:obj:`str`): the name of logger, if None then get 'default_logger'
Notes:
you can reference Logger.manager.getLogger(name) in the python3 /logging/__init__.py
"""
if name is None:
name = 'default_logger'
return logging.getLogger(name)
class TextLogger(object):
r"""
Overview:
Logger that save terminal output to file
Interface: __init__, info
"""
def __init__(self, path, name=None, level=logging.INFO):
r"""
Overview:
initialization method, create logger.
Arguments:
- path (:obj:`str`): logger's save dir
- name (:obj:`str`): logger's name
- level (:obj:`int` or :obj:`str`): Set the logging level of logger, reference Logger class setLevel method.
"""
if name is None:
name = 'default_logger'
# ensure the path exists
try:
os.makedirs(path)
except FileExistsError:
pass
self.logger = self._create_logger(name, os.path.join(path, name + '.txt'), level=level)
def _create_logger(self, name, path, level=logging.INFO):
r"""
Overview:
create logger using logging
Arguments:
- name (:obj:`str`): logger's name
- path (:obj:`str`): logger's save dir
Returns:
- (:obj`logger`): new logger
"""
logger = logging.getLogger(name)
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='[%(asctime)s][%(filename)15s][line:%(lineno)4d][%(levelname)8s] %(message)s')
if not logger.handlers:
formatter = logging.Formatter('[%(asctime)s][%(filename)15s][line:%(lineno)4d][%(levelname)8s] %(message)s')
fh = logging.FileHandler(path, 'a')
fh.setFormatter(formatter)
logger.setLevel(level)
logger.addHandler(fh)
#handler = logging.StreamHandler()
#handler.setFormatter(formatter)
#logger.addHandler(handler)
#logger.propagate = False
return logger
def info(self, s):
r"""
Overview:
add message to logger
Arguments:
- s (:obj:`str`): message to add to logger
Notes:
you can reference Logger class in the python3 /logging/__init__.py
"""
self.logger.info(s)
def bug(self, s):
r"""
Overview:
call logger.debug
Arguments:
- s (:obj:`str`): message to add to logger
Notes:
you can reference Logger class in the python3 /logging/__init__.py
"""
self.logger.debug(s)
def error(self, s):
self.logger.error(s)
class TensorBoardLogger(object):
r"""
Overview:
logger that save message to tensorboard
Interface:
__init__, add_scalar, add_text, add_scalars, add_histogram, add_figure, add_image, add_scalar_list,
register_var, scalar_var_names
"""
def __init__(self, path, name=None):
r"""
Overview:
initialization method, create logger and set var names.
Arguments:
- path (:obj:`str`): logger save dir
- name (:obj:`str`): logger name
"""
if name is None:
name = 'default_tb_logger'
self.logger = SummaryWriter(os.path.join(path, name)) # get summary writer
self._var_names = {
'scalar': [],
'text': [],
'scalars': [],
'histogram': [],
'figure': [],
'image': [],
}
def add_scalar(self, name, *args, **kwargs):
r"""
Overview:
add message to scalar
Arguments:
- name (:obj:`str`): name to add which in self._var_names['scalar']
"""
assert (name in self._var_names['scalar'])
self.logger.add_scalar(name, *args, **kwargs)
def add_text(self, name, *args, **kwargs):
r"""
Overview:
add message to text
Arguments:
- name (:obj:`str`): name to add which in self._var_names['text']
"""
assert (name in self._var_names['text'])
self.logger.add_text(name, *args, **kwargs)
def add_scalars(self, name, *args, **kwargs):
r"""
Overview:
add message to scalars
Arguments:
- name (:obj:`str`): name to add which in self._var_names['scalars']
"""
assert (name in self._var_names['scalars'])
self.logger.add_scalars(name, *args, **kwargs)
def add_histogram(self, name, *args, **kwargs):
r"""
Overview:
add message to histogram
Arguments:
- name (:obj:`str`): name to add which in self._var_names['histogram']
"""
assert (name in self._var_names['histogram'])
self.logger.add_histogram(name, *args, **kwargs)
def add_figure(self, name, *args, **kwargs):
r"""
Overview:
add message to figure
Arguments:
- name (:obj:`str`): name to add which in self._var_names['figure']
"""
assert (name in self._var_names['figure'])
self.logger.add_figure(name, *args, **kwargs)
def add_image(self, name, *args, **kwargs):
r"""
Overview:
add message to image
Arguments:
- name (:obj:`str`): name to add which in self._var_names['image']
"""
assert (name in self._var_names['image'])
self.logger.add_image(name, *args, **kwargs)
def add_val_list(self, val_list, viz_type):
r"""
Overview:
add val_list info to tb
Arguments:
- val_list (:obj:`list`): include element(name, value, step) to be added
- viz_type (:obs:`str`): must be in ['scalar', 'scalars', 'histogram']
"""
assert (viz_type in ['scalar', 'scalars', 'histogram'])
func_dict = {
'scalar': self.add_scalar,
'scalars': self.add_scalars,
'histogram': self.add_histogram,
}
for n, v, s in val_list:
func_dict[viz_type](n, v, s)
def _no_contain_name(self, name):
for k, v in self._var_names.items():
if name in v:
return False
return True
def register_var(self, name, var_type='scalar'):
r"""
Overview:
add var to self_var._names
Arguments:
- name (:obj:`str`): name to add
- var_type (:obj:`str`): the type of var to add to, defalut set to 'scalar',
support [scalar', 'text', 'scalars', 'histogram', 'figure', 'image']
"""
assert (var_type in self._var_names.keys())
# assert (self._no_contain_name(name))
self._var_names[var_type].append(name)
@property
def scalar_var_names(self):
r"""
Overview:
return scalar_var_names
Returns:
- names(:obj:`list` of :obj:`str`): self._var_names['scalar']
"""
return self._var_names['scalar']
class VariableRecord(object):
r"""
Overview:
logger that record variable for further process
Interface:
__init__, register_var, update_var, get_var_names, get_var_text, get_vars_tb_format, get_vars_text
"""
def __init__(self, length):
r"""
Overview:
init the VariableRecord
Arguments:
- length (:obj:`int`): the length to average across, if less than 10 then will be set to 10
"""
self.var_dict = {'scalar': {}}
self.length = max(length, 10) # at least average across 10 iteration
def register_var(self, name, length=None, var_type='scalar'):
r"""
Overview:
add var to self_var._names, calculate it's average value
Arguments:
- name (:obj:`str`): name to add
- length (:obj:`int` or :obj:`None`): length of iters to average, default set to self.length
- var_type (:obj:`str`): the type of var to add to, defalut set to 'scalar'
"""
assert (var_type in ['scalar'])
lens = self.length if length is None else length
self.var_dict[var_type][name] = AverageMeter(lens)
def update_var(self, info):
r"""
Overview:
update vars
Arguments:
- info (:obj:`dict`): key is var type and value is the corresponding variable name
"""
assert isinstance(info, dict)
for k, v in info.items():
var_type = self._get_var_type(k)
self.var_dict[var_type][k].update(v)
def _get_var_type(self, k):
for var_type, var_type_dict in self.var_dict.items():
if k in var_type_dict.keys():
return var_type
raise KeyError("invalid key({}) in variable record".format(k))
def get_var_names(self, var_type='scalar'):
r"""
Overview:
get the corresponding variable names of a certain var_type
Arguments:
- var_type (:obj:`str`): defalut set to 'scalar', support [scalar']
Returns:
- keys (:obj:`list` of :obj:`str`): the var names of a certain var_type
"""
return self.var_dict[var_type].keys()
def get_var_text(self, name, var_type='scalar'):
r"""
Overview:
get the text discroption of var
Arguments:
- name (:obj:`str`): name of the var to query
- var_type(:obj:`str`): default set to scalar, support ['scalar']
Returns:
- text(:obj:`str`): the corresponding text description
"""
assert (var_type in ['scalar'])
if var_type == 'scalar':
handle_var = self.var_dict[var_type][name]
return '{}: val({:.6f})|avg({:.6f})'.format(name, handle_var.val, handle_var.avg)
else:
raise NotImplementedError
def get_vars_tb_format(self, keys, cur_step, var_type='scalar', **kwargs):
r"""
Overview:
get the tb_format description of var
Arguments:
- keys (:obj:`list` of :obj:`str`): keys(names) of the var to query
- cur_step (:obj:`int`): the current step
- var_type(:obj:`str`): default set to scalar, support support ['scalar']
Returns:
- ret (:obj:`list` of :obj:`list` of :obj:`str`): the list of tb_format info of vars queried
"""
assert (var_type in ['scalar'])
if var_type == 'scalar':
ret = []
var_keys = self.get_var_names(var_type)
for k in keys:
if k in var_keys:
v = self.var_dict[var_type][k]
if k == 'grad':
ret.append([k, v.val, cur_step])
else:
ret.append([k, v.avg, cur_step])
return ret
else:
raise NotImplementedError
def get_vars_text(self):
r"""
Overview:
get the string description of var
Returns:
- ret (:obj:`list` of :obj:`str`): the list of text description of vars queried
"""
headers = ["Name", "Value", "Avg"]
data = []
for k in self.get_var_names('scalar'):
handle_var = self.var_dict['scalar'][k]
data.append([k, "{:.6f}".format(handle_var.val), "{:.6f}".format(handle_var.avg)])
s = "\n" + tabulate(data, headers=headers, tablefmt='grid')
return s
def get_star_text(self):
headers = ["name", "val", "name", "reward", "value", "td_loss", "pg_loss", "at", "delay", "queued", "su", "tu",
"tl"]
data = []
k0 = ['cur_lr', 'data_time', 'train_time', 'forward_time', 'backward_time', 'total_loss', 'grad']
k1 = ['winloss', 'bo', 'bu', 'effect', 'upgrade', 'battle', 'upgo', 'kl', 'entropy']
k2 = ['reward', 'value', 'td', 'total', 'at', 'delay', 'queued', 'su', 'tu', 'tl', ]
all_vars = self.get_var_names('scalar')
for i in range(max(len(k1), len(k0))):
d = []
if i < len(k0):
if k0[i] == 'grad':
d += [k0[i], "{:.6f}".format(self.var_dict['scalar'][k0[i]].val)]
else:
d += [k0[i], "{:.6f}".format(self.var_dict['scalar'][k0[i]].avg)]
else:
d += ['', '']
if i < len(k1):
d += [k1[i]]
vals = []
for k in k2:
var_key = k1[i] + '_' + k
if var_key in all_vars:
vals.append("{:.6f}".format(self.var_dict['scalar'][var_key].avg))
else:
vals.append('')
d += vals
data.append(d)
s = "\n" + tabulate(data, headers=headers, tablefmt='grid')
return s
class AverageMeter(object):
r"""
Overview:
Computes and stores the average and current value, scalar and 1D-array
Interface:
__init__, reset, update
"""
def __init__(self, length=0):
r"""
Overview:
init AverageMeter class
Arguments:
- length (:obj:`int`) : set the default length of iters to average
"""
assert (length > 0)
self.length = length
self.reset()
def reset(self):
r"""
Overview:
reset AverageMeter class
"""
self.history = []
self.val = 0.0
self.avg = 0.0
def update(self, val):
r"""
Overview:
update AverageMeter class, append the val to the history and calculate the average
Arguments:
- val (:obj:`numbers.Integral` or :obj:`list` or :obj:`numbers.Real` ) : the latest value
"""
if isinstance(val, torch.Tensor):
val = val.item()
assert (isinstance(val, list) or isinstance(val, numbers.Integral) or isinstance(val, numbers.Real))
self.history.append(val)
if len(self.history) > self.length:
del self.history[0]
self.val = self.history[-1]
self.avg = np.mean(self.history, axis=0)
class DistributionTimeImage(object):
r"""
Overview:
DistributionTimeImage can be used to store images accorrding to time_steps,
for data with 3 dims(time, category, value)
Interface:
__init__, add_one_time_step, get_image
"""
def __init__(self, maxlen=600, val_range=None):
r"""
Overview:
init the DistributionTimeImage class
Arguments:
- maxlen (:obj:`int`): the max length of data inputs
- val_range (:obj:`dict` or :obj:`None`): contain :obj:`int` type val_range['min'] and val_range['max'],
default set to None
"""
self.maxlen = maxlen
self.val_range = val_range
self.img = np.ones((maxlen, maxlen))
self.time_step = 0
self.one_img = np.ones((maxlen, maxlen))
def add_one_time_step(self, data):
r"""
Overview:
step one timestep in DistributionTimeImage and add the data to distribution image
Arguments:
- data (:obj:`np.array`):the data input
"""
assert (isinstance(data, np.ndarray))
data = np.expand_dims(data, 1)
data = cv2.resize(data, (1, self.maxlen), interpolation=cv2.INTER_LINEAR)
if self.time_step >= self.maxlen:
self.img = np.concatenate([self.img[:, 1:], data])
else:
self.img[:, self.time_step:self.time_step + 1] = data
self.time_step += 1
def get_image(self):
r"""
Overview:
return the distribution image
Returns:
img (:obj:`bp.ndarray`): the calculated distribution image
"""
norm_img = np.copy(self.img)
valid = norm_img[:, :self.time_step]
if self.val_range is None:
valid = (valid - valid.min()) / (valid.max() - valid.min())
else:
valid = np.clip(valid, self.val_range['min'], self.val_range['max'])
valid = (valid - self.val_range['min']) / (self.val_range['max'] - self.val_range['min'])
norm_img[:, :self.time_step] = valid
return np.stack([self.one_img, norm_img, norm_img], axis=0)
def pretty_print(result, direct_print=True):
r"""
Overview:
print the result in a pretty way
Arguments:
- result (:obj:`dict`): the result to print
- direct_print (:obj:`bool`): whether to print directly
Returns:
- string (:obj:`str`): the printed result in str format
"""
result = result.copy()
out = {}
for k, v in result.items():
if v is not None:
out[k] = v
cleaned = json.dumps(out)
string = yaml.safe_dump(json.loads(cleaned), default_flow_style=False)
if direct_print:
print(string)
return string
|
scripts/sptk/compute_dpcl_label.py | unanan/setk | 280 | 12611488 | #!/usr/bin/env python
# wujian@2019
"""
Compute labels for DC (Deep Clustering) training:
-1 means silence
0...N for each speaker
"""
import argparse
import numpy as np
from libs.opts import StftParser
from libs.data_handler import SpectrogramReader, NumpyWriter
from libs.utils import get_logger, EPSILON
logger = get_logger(__name__)
def run(args):
# shape: T x F
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"round_power_of_two": args.round_power_of_two,
"window": args.window,
"center": args.center,
"apply_abs": True,
}
spk_scps = args.spks.split(",")
if len(spk_scps) < 2:
raise RuntimeError("Please give at least 2 speakers")
mix_reader = SpectrogramReader(args.mix, **stft_kwargs)
spk_reader = [SpectrogramReader(spk, **stft_kwargs) for spk in spk_scps]
with NumpyWriter(args.dir) as writer:
for key, mix in mix_reader:
T, F = mix.shape
masks = np.zeros_like(mix, dtype=np.float32)
# sil: -1
mix_2db = 20 * np.log10(np.maximum(mix, EPSILON))
sil_idx = mix_2db < (np.max(mix_2db) - args.beta)
masks[sil_idx] = -1
logger.info("For {}, silence covered {:.2f}%".format(
key,
np.sum(sil_idx) * 100 / (T * F)))
# for each speaker
act_idx = ~sil_idx
labels = np.argmax(np.stack([reader[key]
for reader in spk_reader]),
axis=0)
masks[act_idx] = labels[act_idx]
writer.write(key, masks)
logger.info("Processed {:d} utterances done".format(len(mix_reader)))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Command to compute labels for DC (Deep Clustering) "
"training, -1 means silence, 0..N for each speaker",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[StftParser.parser])
parser.add_argument("mix", type=str, help="Rspecifier for mixture")
parser.add_argument("spks",
type=str,
help="Rspecifier for multiple speakers, "
"separated by \',\', egs: spk1.scp,spk2.scp")
parser.add_argument("dir",
type=str,
help="Directory to store computed labels")
parser.add_argument("--beta",
type=float,
default=40,
help="Threshold to discriminate silence bins (in dB)")
args = parser.parse_args()
run(args) |
OmniDB/OmniDB_app/include/Session.py | lejmr/OmniDB | 2,982 | 12611519 | import OmniDB_app.include.Spartacus as Spartacus
import OmniDB_app.include.Spartacus.Database as Database
import OmniDB_app.include.Spartacus.Utils as Utils
import OmniDB_app.include.OmniDatabase as OmniDatabase
import uuid
from datetime import datetime,timedelta
from django.contrib.sessions.backends.db import SessionStore
from OmniDB import settings
from OmniDB import custom_settings
import sys
sys.path.append('OmniDB_app/include')
import paramiko
from sshtunnel import SSHTunnelForwarder
import socket
import time
import os
from collections import OrderedDict
from django.contrib.auth.models import User
from OmniDB_app.models.main import *
import logging
logger = logging.getLogger('OmniDB_app.Session')
from django.db.models import Q
import threading
tunnels = dict([])
tunnel_locks = dict([])
'''
------------------------------------------------------------------------
Session
------------------------------------------------------------------------
'''
class Session(object):
def __init__(self,
p_user_id,
p_user_name,
p_theme,
p_font_size,
p_super_user,
p_user_key,
p_csv_encoding,
p_csv_delimiter):
self.v_user_id = p_user_id
self.v_user_name = p_user_name
self.v_theme = p_theme
self.v_font_size = p_font_size
self.v_super_user = p_super_user
self.v_database_index = -1
self.v_databases = OrderedDict()
self.v_user_key = p_user_key
self.v_csv_encoding = p_csv_encoding
self.v_csv_delimiter = p_csv_delimiter
self.v_tabs_databases = dict([])
self.RefreshDatabaseList()
def AddDatabase(self,
p_conn_id = None,
p_technology = None,
p_database = None,
p_prompt_password = True,
p_tunnel_information = None,
p_alias = None,
p_public = None):
if len(self.v_databases)==0:
self.v_database_index = 0
self.v_databases[p_conn_id] = {
'database': p_database,
'prompt_password': <PASSWORD>,
'prompt_timeout': None,
'tunnel': p_tunnel_information,
'tunnel_object': None,
'alias': p_alias,
'technology': p_technology,
'public': p_public
}
def RemoveDatabase(self,
p_conn_id = None):
del self.v_databases[p_conn_id]
def DatabaseReachPasswordTimeout(self,p_database_index):
v_return = { 'timeout': False, 'message': ''}
# This region of the code cannot be accessed by multiple threads, so locking is required
try:
lock_object = tunnel_locks[self.v_databases[p_database_index]['database'].v_conn_id]
except:
lock_object = threading.Lock()
tunnel_locks[self.v_databases[p_database_index]['database'].v_conn_id] = lock_object
try:
lock_object.acquire()
except:
None
try:
#Create tunnel if enabled
if self.v_databases[p_database_index]['tunnel']['enabled']:
v_create_tunnel = False
if self.v_databases[p_database_index]['tunnel_object'] != None:
try:
result = 0
v_tunnel_object = tunnels[self.v_databases[p_database_index]['database'].v_conn_id]
if not v_tunnel_object.is_active:
v_tunnel_object.stop()
v_create_tunnel = True
except Exception as exc:
v_create_tunnel = True
None
if self.v_databases[p_database_index]['tunnel_object'] == None or v_create_tunnel:
try:
if self.v_databases[p_database_index]['tunnel']['key'].strip() != '':
v_file_name = '{0}'.format(str(time.time())).replace('.','_')
v_full_file_name = os.path.join(settings.TEMP_DIR, v_file_name)
with open(v_full_file_name,'w') as f:
f.write(self.v_databases[p_database_index]['tunnel']['key'])
server = SSHTunnelForwarder(
(self.v_databases[p_database_index]['tunnel']['server'], int(self.v_databases[p_database_index]['tunnel']['port'])),
ssh_username=self.v_databases[p_database_index]['tunnel']['user'],
ssh_private_key_password=self.v_databases[p_database_index]['tunnel']['password'],
ssh_pkey = v_full_file_name,
remote_bind_address=(self.v_databases[p_database_index]['database'].v_active_server, int(self.v_databases[p_database_index]['database'].v_active_port)),
logger=logger
)
else:
server = SSHTunnelForwarder(
(self.v_databases[p_database_index]['tunnel']['server'], int(self.v_databases[p_database_index]['tunnel']['port'])),
ssh_username=self.v_databases[p_database_index]['tunnel']['user'],
ssh_password=self.v_databases[p_database_index]['tunnel']['password'],
remote_bind_address=(self.v_databases[p_database_index]['database'].v_active_server, int(self.v_databases[p_database_index]['database'].v_active_port)),
logger=logger
)
server.set_keepalive = 120
server.start()
s = SessionStore(session_key=self.v_user_key)
tunnels[self.v_databases[p_database_index]['database'].v_conn_id] = server
self.v_databases[p_database_index]['tunnel_object'] = str(server.local_bind_port)
self.v_databases[p_database_index]['database'].v_connection.v_host = '127.0.0.1'
self.v_databases[p_database_index]['database'].v_connection.v_port = server.local_bind_port
s['omnidb_session'] = self
s.save()
except Exception as exc:
return { 'timeout': True, 'message': str(exc)}
if self.v_databases[p_database_index]['prompt_password']:
#Reached timeout, must request password
if not self.v_databases[p_database_index]['prompt_timeout'] or datetime.now() > self.v_databases[p_database_index]['prompt_timeout'] + timedelta(0,settings.PWD_TIMEOUT_TOTAL):
#Try passwordless connection
self.v_databases[p_database_index]['database'].v_connection.v_password = ''
v_test = self.v_databases[p_database_index]['database'].TestConnection()
if v_test=='Connection successful.':
s = SessionStore(session_key=self.v_user_key)
s['omnidb_session'].v_databases[p_database_index]['prompt_timeout'] = datetime.now()
s['omnidb_session'].v_databases[p_database_index]['database'].v_connection.v_password = ''
s.save()
v_return = { 'timeout': False, 'message': ''}
else:
v_return = { 'timeout': True, 'message': v_test}
#Reached half way to timeout, update prompt_timeout
if datetime.now() > self.v_databases[p_database_index]['prompt_timeout'] + timedelta(0,settings.PWD_TIMEOUT_REFRESH):
s = SessionStore(session_key=self.v_user_key)
s['omnidb_session'].v_databases[p_database_index]['prompt_timeout'] = datetime.now()
s.save()
except:
None
try:
lock_object.release()
except:
None
return v_return
def GetSelectedDatabase(self):
return self.v_databases(self.v_database_index)
def RefreshDatabaseList(self):
self.v_databases = {}
try:
for conn in Connection.objects.filter(Q(user=User.objects.get(id=self.v_user_id)) | Q(public=True)):
tunnel_information = {
'enabled': conn.use_tunnel,
'server': conn.ssh_server,
'port': conn.ssh_port,
'user': conn.ssh_user,
'password': <PASSWORD>,
'key': conn.ssh_key
}
database = OmniDatabase.Generic.InstantiateDatabase(
conn.technology.name,
conn.server,
conn.port,
conn.database,
conn.username,
conn.password,
conn.id,
conn.alias,
p_conn_string = conn.conn_string,
p_parse_conn_string = True
)
prompt_password = conn.password == ''
self.AddDatabase(conn.id,conn.technology.name,database,prompt_password,tunnel_information,conn.alias,conn.public)
# No connections
except Exception as exc:
None
def Execute(self,
p_database,
p_sql,
p_loghistory):
v_table = p_database.v_connection.Execute(p_sql)
return v_table
def Query(self,
p_database,
p_sql,
p_loghistory):
v_table = p_database.v_connection.Query(p_sql,True)
return v_table
def QueryDataLimited(self,
p_database,
p_sql,
p_count,
p_loghistory):
v_table = p_database.QueryDataLimited(p_sql, p_count)
return v_table
|
setup.py | Trowsing/tapioca-wrapper | 362 | 12611526 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import re
import os
import sys
description = """
Tapioca provides an easy way to make explorable Python API wrappers.
APIs wrapped by Tapioca follow a simple interaction pattern that works uniformly so developers don't need to learn how to use a new coding interface/style for each service API.
Source code hosted on Github: https://github.com/vintasoftware/tapioca-wrapper
Documentation hosted by Readthedocs: http://tapioca-wrapper.readthedocs.io/en/stable/
"""
package = 'tapioca'
requirements = [
'requests[security]>=2.6',
'arrow>=0.6.0,<1',
'six>=1',
'xmltodict>=0.9.2'
]
test_requirements = [
'responses>=0.5',
'mock>=1.3,<1.4'
]
def get_version(package):
"""
Return package version as listed in `__version__` in `init.py`.
"""
init_py = open(os.path.join(package, '__init__.py')).read()
return re.search("^__version__ = ['\"]([^'\"]+)['\"]", init_py, re.MULTILINE).group(1)
# python setup.py register
if sys.argv[-1] == 'publish':
os.system("python setup.py sdist upload")
args = {'version': get_version(package)}
print("You probably want to also tag the version now:")
print(" git tag -a %(version)s -m 'version %(version)s'" % args)
print(" git push --tags")
sys.exit()
setup(
name='tapioca-wrapper',
version=get_version(package),
description='Python API client generator',
long_description=description,
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/vintasoftware/tapioca-wrapper',
packages=[
'tapioca',
],
package_dir={'tapioca': 'tapioca'},
include_package_data=True,
install_requires=requirements,
license="MIT",
zip_safe=False,
keywords='tapioca,wrapper,api',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Programming Language :: Python :: 3',
],
test_suite='tests',
tests_require=test_requirements
)
|
lldb/test/API/commands/statistics/basic/TestStats.py | rarutyun/llvm | 2,338 | 12611533 | import lldb
from lldbsuite.test.decorators import *
from lldbsuite.test.lldbtest import *
from lldbsuite.test import lldbutil
class TestCase(TestBase):
mydir = TestBase.compute_mydir(__file__)
def test(self):
self.build()
lldbutil.run_to_source_breakpoint(self, "// break here", lldb.SBFileSpec("main.c"))
self.expect("statistics disable", substrs=['need to enable statistics before disabling'], error=True)
# 'expression' should change the statistics.
self.expect("statistics enable")
self.expect("statistics enable", substrs=['already enabled'], error=True)
self.expect("expr patatino", substrs=['27'])
self.expect("statistics disable")
self.expect("statistics dump", substrs=['expr evaluation successes : 1\n',
'expr evaluation failures : 0\n'])
self.expect("statistics enable")
# Doesn't parse.
self.expect("expr doesnt_exist", error=True,
substrs=["undeclared identifier 'doesnt_exist'"])
# Doesn't successfully execute.
self.expect("expr int *i = nullptr; *i", error=True)
# Interpret an integer as an array with 3 elements is also a failure.
self.expect("expr -Z 3 -- 1", error=True,
substrs=["expression cannot be used with --element-count"])
self.expect("statistics disable")
# We should have gotten 3 new failures and the previous success.
self.expect("statistics dump", substrs=['expr evaluation successes : 1\n',
'expr evaluation failures : 3\n'])
# 'frame var' with disabled statistics shouldn't change stats.
self.expect("frame var", substrs=['27'])
self.expect("statistics enable")
# 'frame var' with enabled statistics will change stats.
self.expect("frame var", substrs=['27'])
self.expect("statistics disable")
self.expect("statistics dump", substrs=['frame var successes : 1\n',
'frame var failures : 0\n'])
|
tools/c7n_azure/c7n_azure/resources/mysql.py | vkubyshko/cloud-custodian | 2,415 | 12611537 | # Copyright The Cloud Custodian Authors.
# SPDX-License-Identifier: Apache-2.0
from c7n_azure.provider import resources
from c7n_azure.resources.arm import ArmResourceManager
@resources.register('mysql')
class MySQL(ArmResourceManager):
"""Azure MySQL Server Resource
:example:
Returns all MySQL servers
.. code-block:: yaml
policies:
- name: basic-logic-app
resource: azure.mysql
filters:
- type: value
key: sku.name
op: equal
value: Basic
"""
class resource_type(ArmResourceManager.resource_type):
doc_groups = ['Databases']
service = 'azure.mgmt.rdbms.mysql'
client = 'MySQLManagementClient'
enum_spec = ('servers', 'list', None)
default_report_fields = (
'name',
'location',
'resourceGroup'
)
resource_type = 'Microsoft.DBForMySQL/servers'
|
test/fixture/python_scanner/nested1/nested2/nested3/imports_parent_module.py | Valkatraz/scons | 1,403 | 12611548 | <gh_stars>1000+
from .. import module
|
CMSIS/DSP/SDFTools/examples/example5/main.py | DavidLesnjak/CMSIS_5 | 2,293 | 12611551 | <filename>CMSIS/DSP/SDFTools/examples/example5/main.py
import sched as s
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.animation as animation
import cmsisdsp as dsp
import numpy as np
import cmsisdsp.fixedpoint as f
import cmsisdsp.mfcc as mfcc
import scipy.signal as sig
from cmsisdsp.datatype import F32,Q31,Q15
from sharedconfig import *
import cmsisdsp.datatype as dt
mfccq15=dsp.arm_mfcc_instance_q15()
windowQ15 = dt.convert(sig.hamming(FFTSize, sym=False),Q15)
filtLen,filtPos,packedFiltersQ15 = mfcc.melFilterMatrix(Q15,freq_min, freq_high, numOfMelFilters,sample_rate,FFTSize)
dctMatrixFiltersQ15 = mfcc.dctMatrix(Q15,numOfDctOutputs, numOfMelFilters)
status=dsp.arm_mfcc_init_q15(mfccq15,FFTSize,numOfMelFilters,numOfDctOutputs,
dctMatrixFiltersQ15,
filtPos,filtLen,packedFiltersQ15,windowQ15)
#DISPBUF = np.zeros(nbMFCCOutputs*numOfDctOutputs)
DISPBUF=[]
print("Start")
nb,error = s.scheduler(mfccq15,DISPBUF)
print("Nb sched = %d" % nb)
fig, ax = plt.subplots()
# The test signal is 5 second long
# MFCC are slided by 0.5 second by the last window
# Each sink entry is a one second of MFCC
ims = []
for i in range(10):
mfccdata = (1<<8)*f.Q15toF32(DISPBUF[i])
mfccdata=mfccdata.reshape((nbMFCCOutputs,numOfDctOutputs))
mfccdata= np.swapaxes(mfccdata, 0 ,1)
if i==0:
ax.imshow(mfccdata, vmin=-10, vmax=10,interpolation='nearest',cmap=cm.coolwarm ,origin='lower')
im=ax.imshow(mfccdata, vmin=-10, vmax=10,interpolation='nearest', animated=True,cmap=cm.coolwarm ,origin='lower')
ims.append([im])
ani = animation.ArtistAnimation(fig, ims, interval=500, blit=True,
repeat_delay=500)
#ani.save("mfcc.mp4")
plt.show()
|
aredis_om/checks.py | gowthamparuchuru/redis-om-python | 195 | 12611571 | from functools import lru_cache
from typing import List
from aredis_om.connections import get_redis_connection
@lru_cache(maxsize=None)
async def check_for_command(conn, cmd):
cmd_info = await conn.execute_command("command", "info", cmd)
return None not in cmd_info
@lru_cache(maxsize=None)
async def has_redis_json(conn=None):
if conn is None:
conn = get_redis_connection()
command_exists = await check_for_command(conn, "json.set")
return command_exists
@lru_cache(maxsize=None)
async def has_redisearch(conn=None):
if conn is None:
conn = get_redis_connection()
if has_redis_json(conn):
return True
command_exists = await check_for_command(conn, "ft.search")
return command_exists
|
python_code/order.py | compile-run-Ali/SIMS | 254 | 12611615 | from __future__ import division
import os,cv2,helper,time,scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.layers.python.layers import initializers
from spatial_transformer import transformer
import numpy as np
import numpy.matlib
training_phase = False
if(training_phase):
label_path = "../traindata/label_refine/"
mat_path = "../traindata/order/"
save_model_path = "../trainedmodels/order"
else:
label_path = "../testdata/label_refine/"
mat_path = '../testdata/order/'
save_result_path ="../result/order/data/"
save_model_path = "../trainedmodels/order"
os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp')
os.environ['CUDA_VISIBLE_DEVICES']=str(np.argmax([int(x.split()[2]) for x in open('tmp','r').readlines()]))
os.system('rm tmp')
batch_size = 10
crop_size_h = 256
crop_size_w = 512
input_dim = 6
sp = 256
topk = 10
sess=tf.Session()
def lrelu(x):
return tf.maximum(0.2*x,x)
MEAN_VALUES = np.array([123.6800, 116.7790, 103.9390]).reshape((1,1,1,3))
def build_net(ntype,nin,nwb=None,name=None):
if ntype=='conv':
return tf.nn.relu(tf.nn.conv2d(nin,nwb[0],strides=[1,1,1,1],padding='SAME',name=name)+nwb[1])
elif ntype=='pool':
return tf.nn.avg_pool(nin,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def get_weight_bias(vgg_layers,i):
weights=vgg_layers[i][0][0][2][0][0]
weights=tf.constant(weights)
bias=vgg_layers[i][0][0][2][0][1]
bias=tf.constant(np.reshape(bias,(bias.size)))
return weights,bias
def one_hot(label):
#print(label.shape)
output=np.zeros((1,100,label.shape[0],label.shape[1]),dtype=np.float32);
for i in range(label.shape[0]):
for j in range(label.shape[1]):
if label[i,j]>0:
output[0,label[i,j]-1,i,j]=1
return output
def proposal_classifier(data):
conv1_selection = slim.repeat(data, 2, slim.conv2d, 64, [3, 3], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu, scope='g_selection_conv1')
pool1_selection = slim.avg_pool2d(conv1_selection, [3, 3], stride=2, padding='SAME', scope='g_selection_pool1')
conv2_selection = slim.repeat(pool1_selection, 2, slim.conv2d, 128, [3, 3], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu, scope='g_selection_conv2')
pool2_selection = slim.max_pool2d(conv2_selection, [3, 3], stride=2, padding='SAME', scope='g_selection_pool2')
conv3_selection = slim.repeat(pool2_selection, 3, slim.conv2d, 256, [3, 3], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu, scope='g_selection_conv3')
pool3_selection = slim.max_pool2d(conv3_selection, [3, 3], stride=2, padding='SAME', scope='g_selection_pool3')
conv4_selection = slim.repeat(pool3_selection, 3, slim.conv2d, 512, [3, 3], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu,
scope='g_selection_conv4')
pool4_selection = slim.max_pool2d(conv4_selection, [3, 3], stride=2, padding='SAME', scope='g_selection_pool4')
conv5_selection = slim.repeat(pool4_selection, 3, slim.conv2d, 512, [3, 3], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu,
scope='g_selection_conv5')
pool5_selection = slim.max_pool2d(conv5_selection, [3, 3], stride=2, padding='SAME', scope='g_selection_pool5')
pool6_selection = slim.avg_pool2d(pool5_selection, [8, 12], stride=[8, 12], padding='VALID',
scope='g_selection_pool6')
fc_selection = slim.conv2d(pool6_selection, 1024, [1, 1], rate=1, normalizer_fn=slim.layer_norm,
activation_fn=lrelu, scope='g_selection_conv6')
fc_selection = slim.dropout(fc_selection, 0.5, is_training= training_phase, scope='drop7_selection_norm')
score = slim.conv2d(fc_selection, 20, [1, 1], rate=1,
activation_fn=None, scope='g_selection_classify')
score = tf.squeeze(score)
return score
with tf.variable_scope(tf.get_variable_scope()) as scope:
label=tf.placeholder(tf.int32,[batch_size,1])
data=tf.placeholder(tf.float32,[batch_size,None,None,input_dim])
sp=256
proposal_score = proposal_classifier(data)
weight=tf.placeholder(tf.float32)
label = tf.squeeze(label)
G_loss=tf.nn.sparse_softmax_cross_entropy_with_logits(labels = label, logits= proposal_score)
G_loss = tf.reduce_mean(G_loss)
lr=tf.placeholder(tf.float32)
t_vars = tf.trainable_variables()
for var in t_vars:
if var.name.startswith('g_'):
print var.name
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(loss = G_loss,var_list=[var for var in t_vars if var.name.startswith('g_')])
saver=tf.train.Saver(max_to_keep=1000)
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state(save_model_path+ "/0100")
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
best=np.inf
g_loss=np.zeros(3000,dtype=float)
tmp_label = np.zeros((batch_size,1),dtype = np.int32)
tmp_data = np.zeros((batch_size, crop_size_h,crop_size_w, input_dim), dtype = float)
tmp_mask = np.zeros((batch_size,1), dtype =float)
total_number = 2975
base_lr = 1e-6
if(training_phase):
for epoch in range(1,101):
tmp_label = np.zeros((batch_size,1),dtype = np.int32)
tmp_data = np.zeros((batch_size, crop_size_h,crop_size_w, input_dim), dtype = float)
tmp_mask = np.zeros((batch_size,1), dtype =float)
if os.path.isdir(save_model_path+"/%04d"%epoch):
continue
tmp_list = np.random.permutation(total_number) + 1
global_ind = 0
while(global_ind<total_number):
st=time.time()
try:
dic = scipy.io.loadmat(mat_path + "%08d.mat" % tmp_list[global_ind])
except:
print("cannot load"+ "%08d.mat"%tmp_list[global_ind])
global_ind=global_ind + 1
continue
topk_proposal = dic['semantic_segment_mask'].astype(np.float32)
if (topk_proposal.ndim == 0):
global_ind = global_ind + 1
continue
corr_label = dic['semantic_segment_label'].astype(np.int32)
topk_proposal = topk_proposal/255.0
semantic = cv2.imread(label_path+"/%08d.png"%tmp_list[global_ind]).astype(np.float32)
semantic = semantic/255.0
tmp_semantic = semantic.copy()
tmp_semantic[:,:,0] = semantic[:,:,2].copy()
tmp_semantic[:,:,1] = semantic[:,:,1].copy()
tmp_semantic[:,:,2] = semantic[:,:,0].copy()
if(topk_proposal.ndim==3):
tmp_data[0,:,:,0:3] = topk_proposal
tmp_data[0,:,:,3:6] = tmp_semantic
tmp_label[0,:] = corr_label
if(topk_proposal.ndim == 4):
num_proposal = topk_proposal.shape[3]
if(num_proposal>=batch_size):
tmp_data[0:batch_size,:,:,0:3] = np.transpose(topk_proposal[:,:,:,0:batch_size],[3,0,1,2])
tmp_data[0:batch_size,:,:,3:6] = np.tile(np.expand_dims(tmp_semantic,axis=0),(batch_size,1,1,1))
tmp_label[0:batch_size,:] = corr_label[0:batch_size,:]
else:
tmp_data[0:num_proposal, :, :, 0:3] = np.transpose(topk_proposal[:, :, :, 0:num_proposal], [3, 0,1,2])
tmp_data[0:num_proposal, :, :, 3:6] = np.tile(np.expand_dims(tmp_semantic,axis=0),(num_proposal,1,1,1))
tmp_label[0:num_proposal, :] = corr_label[0:num_proposal, :]
print(tmp_label[0])
_,G_current,lr_value, proposal_score_val=sess.run([G_opt,G_loss,lr,proposal_score],feed_dict={label:np.squeeze(tmp_label),data:tmp_data,lr:min(base_lr*np.power(1.1,epoch-1),1e-5 )})
g_loss[global_ind]=G_current
global_ind = global_ind + 1
print("%d %d %.2f %.2f %.2f %.6f"%(epoch,global_ind,G_current,np.mean(g_loss[np.where(g_loss)]),time.time()-st, lr_value))
if epoch%1==0:
os.makedirs(save_model_path+"/%04d" % epoch)
target = open(save_model_path+"/%04d/score.txt" % epoch, 'w')
target.write("%f" % np.mean(g_loss[np.where(g_loss)]))
target.close()
saver.save(sess,save_model_path+"/model.ckpt")
if epoch%20==0:
saver.save(sess,save_model_path+"/%04d/model.ckpt"%epoch)
else:
if not os.path.isdir(save_result_path):
os.makedirs(save_result_path)
for ind in range(100001,100501):
print(ind)
dic = scipy.io.loadmat(mat_path + "%08d.mat" %ind)
proposal_all = dic['semantic_segment_mask'].astype(np.float32)/255.0
if (proposal_all.shape[0] == 0):
continue
if (proposal_all.ndim == 3 ):
print('expanding....')
proposal_all = np.expand_dims(proposal_all, axis=3)
semantic = cv2.imread(label_path+"/%08d.png" % ind)
semantic = semantic.astype(np.float32) / 255.0
tmp_semantic = semantic.copy()
tmp_semantic[:, :, 0] = semantic[:, :, 2].copy()
tmp_semantic[:, :, 1] = semantic[:, :, 1].copy()
tmp_semantic[:, :, 2] = semantic[:, :, 0].copy()
final_pred = np.zeros((proposal_all.shape[3],20), dtype=np.float32)
count_all = proposal_all.shape[3]
index = 0
while(count_all>0):
tmp_data = np.zeros((batch_size, crop_size_h, crop_size_w, input_dim), dtype=float)
if(count_all>batch_size):
tmp_data[0:batch_size,:,:,0:3] = np.transpose(proposal_all[:,:,:,index:index+batch_size],[3,0,1,2])
tmp_data[0:batch_size,:,:,3:6] = np.tile(np.expand_dims(tmp_semantic,axis=0),(batch_size,1,1,1))
final_pred[index:index+batch_size,: ] = np.squeeze(sess.run(proposal_score, feed_dict={data: tmp_data}))
count_all = count_all - batch_size
index = index+batch_size
else:
tmp_data[0:count_all, :, :, 0:3] = np.transpose(proposal_all[:, :, :, index:index + count_all],[3, 0, 1, 2])
tmp_data[0:count_all, :, :, 3:6] = np.tile(np.expand_dims(tmp_semantic, axis=0), (count_all, 1, 1, 1))
final_pred[index:index+count_all,:] = np.squeeze(sess.run(proposal_score, feed_dict={data: tmp_data}))[0:count_all,:]
index = index + count_all
count_all = 0
scipy.io.savemat(save_result_path + "/%08d.mat" % ind, {'prediction': final_pred})
|
care/facility/migrations/0063_merge_20200402_1402.py | gigincg/care | 189 | 12611618 | <gh_stars>100-1000
# Generated by Django 2.2.11 on 2020-04-02 14:02
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('facility', '0062_auto_20200402_1336'),
('facility', '0062_populate_facility_in_patient'),
]
operations = [
]
|
examples/download_df.py | Yook74/dash-extensions | 250 | 12611620 | <filename>examples/download_df.py<gh_stars>100-1000
import dash
import pandas as pd
import dash_html_components as html
from dash.dependencies import Output, Input
from dash_extensions import Download
from dash_extensions.snippets import send_data_frame
# Example data.
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [2, 1, 5, 6], 'c': ['x', 'x', 'y', 'y']})
# Create app.
app = dash.Dash(prevent_initial_callbacks=True)
app.layout = html.Div([html.Button("Download", id="btn"), Download(id="download")])
@app.callback(Output("download", "data"), [Input("btn", "n_clicks")])
def func(n_clicks):
return send_data_frame(df.to_excel, "mydf.xls")
if __name__ == '__main__':
app.run_server()
|
alipay/aop/api/domain/TemplateStyleInfoDTO.py | snowxmas/alipay-sdk-python-all | 213 | 12611625 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
class TemplateStyleInfoDTO(object):
def __init__(self):
self._background_id = None
self._banner_img_id = None
self._banner_url = None
self._bg_color = None
self._brand_name = None
self._card_show_name = None
self._color = None
self._column_info_layout = None
self._feature_descriptions = None
self._front_image_enable = None
self._front_text_list_enable = None
self._logo_id = None
self._slogan = None
self._slogan_img_id = None
@property
def background_id(self):
return self._background_id
@background_id.setter
def background_id(self, value):
self._background_id = value
@property
def banner_img_id(self):
return self._banner_img_id
@banner_img_id.setter
def banner_img_id(self, value):
self._banner_img_id = value
@property
def banner_url(self):
return self._banner_url
@banner_url.setter
def banner_url(self, value):
self._banner_url = value
@property
def bg_color(self):
return self._bg_color
@bg_color.setter
def bg_color(self, value):
self._bg_color = value
@property
def brand_name(self):
return self._brand_name
@brand_name.setter
def brand_name(self, value):
self._brand_name = value
@property
def card_show_name(self):
return self._card_show_name
@card_show_name.setter
def card_show_name(self, value):
self._card_show_name = value
@property
def color(self):
return self._color
@color.setter
def color(self, value):
self._color = value
@property
def column_info_layout(self):
return self._column_info_layout
@column_info_layout.setter
def column_info_layout(self, value):
self._column_info_layout = value
@property
def feature_descriptions(self):
return self._feature_descriptions
@feature_descriptions.setter
def feature_descriptions(self, value):
if isinstance(value, list):
self._feature_descriptions = list()
for i in value:
self._feature_descriptions.append(i)
@property
def front_image_enable(self):
return self._front_image_enable
@front_image_enable.setter
def front_image_enable(self, value):
self._front_image_enable = value
@property
def front_text_list_enable(self):
return self._front_text_list_enable
@front_text_list_enable.setter
def front_text_list_enable(self, value):
self._front_text_list_enable = value
@property
def logo_id(self):
return self._logo_id
@logo_id.setter
def logo_id(self, value):
self._logo_id = value
@property
def slogan(self):
return self._slogan
@slogan.setter
def slogan(self, value):
self._slogan = value
@property
def slogan_img_id(self):
return self._slogan_img_id
@slogan_img_id.setter
def slogan_img_id(self, value):
self._slogan_img_id = value
def to_alipay_dict(self):
params = dict()
if self.background_id:
if hasattr(self.background_id, 'to_alipay_dict'):
params['background_id'] = self.background_id.to_alipay_dict()
else:
params['background_id'] = self.background_id
if self.banner_img_id:
if hasattr(self.banner_img_id, 'to_alipay_dict'):
params['banner_img_id'] = self.banner_img_id.to_alipay_dict()
else:
params['banner_img_id'] = self.banner_img_id
if self.banner_url:
if hasattr(self.banner_url, 'to_alipay_dict'):
params['banner_url'] = self.banner_url.to_alipay_dict()
else:
params['banner_url'] = self.banner_url
if self.bg_color:
if hasattr(self.bg_color, 'to_alipay_dict'):
params['bg_color'] = self.bg_color.to_alipay_dict()
else:
params['bg_color'] = self.bg_color
if self.brand_name:
if hasattr(self.brand_name, 'to_alipay_dict'):
params['brand_name'] = self.brand_name.to_alipay_dict()
else:
params['brand_name'] = self.brand_name
if self.card_show_name:
if hasattr(self.card_show_name, 'to_alipay_dict'):
params['card_show_name'] = self.card_show_name.to_alipay_dict()
else:
params['card_show_name'] = self.card_show_name
if self.color:
if hasattr(self.color, 'to_alipay_dict'):
params['color'] = self.color.to_alipay_dict()
else:
params['color'] = self.color
if self.column_info_layout:
if hasattr(self.column_info_layout, 'to_alipay_dict'):
params['column_info_layout'] = self.column_info_layout.to_alipay_dict()
else:
params['column_info_layout'] = self.column_info_layout
if self.feature_descriptions:
if isinstance(self.feature_descriptions, list):
for i in range(0, len(self.feature_descriptions)):
element = self.feature_descriptions[i]
if hasattr(element, 'to_alipay_dict'):
self.feature_descriptions[i] = element.to_alipay_dict()
if hasattr(self.feature_descriptions, 'to_alipay_dict'):
params['feature_descriptions'] = self.feature_descriptions.to_alipay_dict()
else:
params['feature_descriptions'] = self.feature_descriptions
if self.front_image_enable:
if hasattr(self.front_image_enable, 'to_alipay_dict'):
params['front_image_enable'] = self.front_image_enable.to_alipay_dict()
else:
params['front_image_enable'] = self.front_image_enable
if self.front_text_list_enable:
if hasattr(self.front_text_list_enable, 'to_alipay_dict'):
params['front_text_list_enable'] = self.front_text_list_enable.to_alipay_dict()
else:
params['front_text_list_enable'] = self.front_text_list_enable
if self.logo_id:
if hasattr(self.logo_id, 'to_alipay_dict'):
params['logo_id'] = self.logo_id.to_alipay_dict()
else:
params['logo_id'] = self.logo_id
if self.slogan:
if hasattr(self.slogan, 'to_alipay_dict'):
params['slogan'] = self.slogan.to_alipay_dict()
else:
params['slogan'] = self.slogan
if self.slogan_img_id:
if hasattr(self.slogan_img_id, 'to_alipay_dict'):
params['slogan_img_id'] = self.slogan_img_id.to_alipay_dict()
else:
params['slogan_img_id'] = self.slogan_img_id
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = TemplateStyleInfoDTO()
if 'background_id' in d:
o.background_id = d['background_id']
if 'banner_img_id' in d:
o.banner_img_id = d['banner_img_id']
if 'banner_url' in d:
o.banner_url = d['banner_url']
if 'bg_color' in d:
o.bg_color = d['bg_color']
if 'brand_name' in d:
o.brand_name = d['brand_name']
if 'card_show_name' in d:
o.card_show_name = d['card_show_name']
if 'color' in d:
o.color = d['color']
if 'column_info_layout' in d:
o.column_info_layout = d['column_info_layout']
if 'feature_descriptions' in d:
o.feature_descriptions = d['feature_descriptions']
if 'front_image_enable' in d:
o.front_image_enable = d['front_image_enable']
if 'front_text_list_enable' in d:
o.front_text_list_enable = d['front_text_list_enable']
if 'logo_id' in d:
o.logo_id = d['logo_id']
if 'slogan' in d:
o.slogan = d['slogan']
if 'slogan_img_id' in d:
o.slogan_img_id = d['slogan_img_id']
return o
|
pgoapi/protos/pogoprotos/data/quests/multi_part_quest_pb2.py | aroo135/pgoapi | 842 | 12611691 | <reponame>aroo135/pgoapi<filename>pgoapi/protos/pogoprotos/data/quests/multi_part_quest_pb2.py<gh_stars>100-1000
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: pogoprotos/data/quests/multi_part_quest.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from pogoprotos.data.quests import quest_pb2 as pogoprotos_dot_data_dot_quests_dot_quest__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='pogoprotos/data/quests/multi_part_quest.proto',
package='pogoprotos.data.quests',
syntax='proto3',
serialized_pb=_b('\n-pogoprotos/data/quests/multi_part_quest.proto\x12\x16pogoprotos.data.quests\x1a\"pogoprotos/data/quests/quest.proto\"C\n\x0eMultiPartQuest\x12\x31\n\nsub_quests\x18\x01 \x03(\x0b\x32\x1d.pogoprotos.data.quests.Questb\x06proto3')
,
dependencies=[pogoprotos_dot_data_dot_quests_dot_quest__pb2.DESCRIPTOR,])
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_MULTIPARTQUEST = _descriptor.Descriptor(
name='MultiPartQuest',
full_name='pogoprotos.data.quests.MultiPartQuest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='sub_quests', full_name='pogoprotos.data.quests.MultiPartQuest.sub_quests', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=109,
serialized_end=176,
)
_MULTIPARTQUEST.fields_by_name['sub_quests'].message_type = pogoprotos_dot_data_dot_quests_dot_quest__pb2._QUEST
DESCRIPTOR.message_types_by_name['MultiPartQuest'] = _MULTIPARTQUEST
MultiPartQuest = _reflection.GeneratedProtocolMessageType('MultiPartQuest', (_message.Message,), dict(
DESCRIPTOR = _MULTIPARTQUEST,
__module__ = 'pogoprotos.data.quests.multi_part_quest_pb2'
# @@protoc_insertion_point(class_scope:pogoprotos.data.quests.MultiPartQuest)
))
_sym_db.RegisterMessage(MultiPartQuest)
# @@protoc_insertion_point(module_scope)
|
parakeet/models/tacotron2.py | zh794390558/DeepSpeech | 501 | 12611704 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle
from paddle import nn
from paddle.fluid.layers import sequence_mask
from paddle.nn import functional as F
from paddle.nn import initializer as I
from tqdm import trange
from parakeet.modules.attention import LocationSensitiveAttention
from parakeet.modules.conv import Conv1dBatchNorm
from parakeet.modules.losses import guided_attention_loss
from parakeet.utils import checkpoint
__all__ = ["Tacotron2", "Tacotron2Loss"]
class DecoderPreNet(nn.Layer):
"""Decoder prenet module for Tacotron2.
Parameters
----------
d_input: int
The input feature size.
d_hidden: int
The hidden size.
d_output: int
The output feature size.
dropout_rate: float
The droput probability.
"""
def __init__(self,
d_input: int,
d_hidden: int,
d_output: int,
dropout_rate: float):
super().__init__()
self.dropout_rate = dropout_rate
self.linear1 = nn.Linear(d_input, d_hidden, bias_attr=False)
self.linear2 = nn.Linear(d_hidden, d_output, bias_attr=False)
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
x: Tensor [shape=(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
Batch of the sequences of padded hidden state.
"""
x = F.dropout(F.relu(self.linear1(x)), self.dropout_rate, training=True)
output = F.dropout(
F.relu(self.linear2(x)), self.dropout_rate, training=True)
return output
class DecoderPostNet(nn.Layer):
"""Decoder postnet module for Tacotron2.
Parameters
----------
d_mels: int
The number of mel bands.
d_hidden: int
The hidden size of postnet.
kernel_size: int
The kernel size of the conv layer in postnet.
num_layers: int
The number of conv layers in postnet.
dropout: float
The droput probability.
"""
def __init__(self,
d_mels: int,
d_hidden: int,
kernel_size: int,
num_layers: int,
dropout: float):
super().__init__()
self.dropout = dropout
self.num_layers = num_layers
padding = int((kernel_size - 1) / 2)
self.conv_batchnorms = nn.LayerList()
k = math.sqrt(1.0 / (d_mels * kernel_size))
self.conv_batchnorms.append(
Conv1dBatchNorm(
d_mels,
d_hidden,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC'))
k = math.sqrt(1.0 / (d_hidden * kernel_size))
self.conv_batchnorms.extend([
Conv1dBatchNorm(
d_hidden,
d_hidden,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC') for i in range(1, num_layers - 1)
])
self.conv_batchnorms.append(
Conv1dBatchNorm(
d_hidden,
d_mels,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC'))
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
x: Tensor [shape=(B, T_mel, C)]
Output sequence of features from decoder.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
Output sequence of features after postnet.
"""
for i in range(len(self.conv_batchnorms) - 1):
x = F.dropout(
F.tanh(self.conv_batchnorms[i](x)),
self.dropout,
training=self.training)
output = F.dropout(
self.conv_batchnorms[self.num_layers - 1](x),
self.dropout,
training=self.training)
return output
class Tacotron2Encoder(nn.Layer):
"""Tacotron2 encoder module for Tacotron2.
Parameters
----------
d_hidden: int
The hidden size in encoder module.
conv_layers: int
The number of conv layers.
kernel_size: int
The kernel size of conv layers.
p_dropout: float
The droput probability.
"""
def __init__(self,
d_hidden: int,
conv_layers: int,
kernel_size: int,
p_dropout: float):
super().__init__()
k = math.sqrt(1.0 / (d_hidden * kernel_size))
self.conv_batchnorms = paddle.nn.LayerList([
Conv1dBatchNorm(
d_hidden,
d_hidden,
kernel_size,
stride=1,
padding=int((kernel_size - 1) / 2),
bias_attr=I.Uniform(-k, k),
data_format='NLC') for i in range(conv_layers)
])
self.p_dropout = p_dropout
self.hidden_size = int(d_hidden / 2)
self.lstm = nn.LSTM(
d_hidden, self.hidden_size, direction="bidirectional")
def forward(self, x, input_lens=None):
"""Calculate forward propagation of tacotron2 encoder.
Parameters
----------
x: Tensor [shape=(B, T, C)]
Input embeddings.
text_lens: Tensor [shape=(B,)], optional
Batch of lengths of each text input batch. Defaults to None.
Returns
-------
output : Tensor [shape=(B, T, C)]
Batch of the sequences of padded hidden states.
"""
for conv_batchnorm in self.conv_batchnorms:
x = F.dropout(
F.relu(conv_batchnorm(x)),
self.p_dropout,
training=self.training)
output, _ = self.lstm(inputs=x, sequence_length=input_lens)
return output
class Tacotron2Decoder(nn.Layer):
"""Tacotron2 decoder module for Tacotron2.
Parameters
----------
d_mels: int
The number of mel bands.
reduction_factor: int
The reduction factor of tacotron.
d_encoder: int
The hidden size of encoder.
d_prenet: int
The hidden size in decoder prenet.
d_attention_rnn: int
The attention rnn layer hidden size.
d_decoder_rnn: int
The decoder rnn layer hidden size.
d_attention: int
The hidden size of the linear layer in location sensitive attention.
attention_filters: int
The filter size of the conv layer in location sensitive attention.
attention_kernel_size: int
The kernel size of the conv layer in location sensitive attention.
p_prenet_dropout: float
The droput probability in decoder prenet.
p_attention_dropout: float
The droput probability in location sensitive attention.
p_decoder_dropout: float
The droput probability in decoder.
use_stop_token: bool
Whether to use a binary classifier for stop token prediction.
Defaults to False
"""
def __init__(self,
d_mels: int,
reduction_factor: int,
d_encoder: int,
d_prenet: int,
d_attention_rnn: int,
d_decoder_rnn: int,
d_attention: int,
attention_filters: int,
attention_kernel_size: int,
p_prenet_dropout: float,
p_attention_dropout: float,
p_decoder_dropout: float,
use_stop_token: bool=False):
super().__init__()
self.d_mels = d_mels
self.reduction_factor = reduction_factor
self.d_encoder = d_encoder
self.d_attention_rnn = d_attention_rnn
self.d_decoder_rnn = d_decoder_rnn
self.p_attention_dropout = p_attention_dropout
self.p_decoder_dropout = p_decoder_dropout
self.prenet = DecoderPreNet(
d_mels * reduction_factor,
d_prenet,
d_prenet,
dropout_rate=p_prenet_dropout)
# attention_rnn takes attention's context vector has an
# auxiliary input
self.attention_rnn = nn.LSTMCell(d_prenet + d_encoder, d_attention_rnn)
self.attention_layer = LocationSensitiveAttention(
d_attention_rnn, d_encoder, d_attention, attention_filters,
attention_kernel_size)
# decoder_rnn takes prenet's output and attention_rnn's input
# as input
self.decoder_rnn = nn.LSTMCell(d_attention_rnn + d_encoder,
d_decoder_rnn)
self.linear_projection = nn.Linear(d_decoder_rnn + d_encoder,
d_mels * reduction_factor)
self.use_stop_token = use_stop_token
if use_stop_token:
self.stop_layer = nn.Linear(d_decoder_rnn + d_encoder, 1)
# states - temporary attributes
self.attention_hidden = None
self.attention_cell = None
self.decoder_hidden = None
self.decoder_cell = None
self.attention_weights = None
self.attention_weights_cum = None
self.attention_context = None
self.key = None
self.mask = None
self.processed_key = None
def _initialize_decoder_states(self, key):
"""init states be used in decoder
"""
batch_size, encoder_steps, _ = key.shape
self.attention_hidden = paddle.zeros(
shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
self.attention_cell = paddle.zeros(
shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
self.decoder_hidden = paddle.zeros(
shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
self.decoder_cell = paddle.zeros(
shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
self.attention_weights = paddle.zeros(
shape=[batch_size, encoder_steps], dtype=key.dtype)
self.attention_weights_cum = paddle.zeros(
shape=[batch_size, encoder_steps], dtype=key.dtype)
self.attention_context = paddle.zeros(
shape=[batch_size, self.d_encoder], dtype=key.dtype)
self.key = key # [B, T, C]
# pre-compute projected keys to improve efficiency
self.processed_key = self.attention_layer.key_layer(key) # [B, T, C]
def _decode(self, query):
"""decode one time step
"""
cell_input = paddle.concat([query, self.attention_context], axis=-1)
# The first lstm layer (or spec encoder lstm)
_, (self.attention_hidden, self.attention_cell) = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(
self.attention_hidden,
self.p_attention_dropout,
training=self.training)
# Loaction sensitive attention
attention_weights_cat = paddle.stack(
[self.attention_weights, self.attention_weights_cum], axis=-1)
self.attention_context, self.attention_weights = self.attention_layer(
self.attention_hidden, self.processed_key, self.key,
attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
# The second lstm layer (or spec decoder lstm)
decoder_input = paddle.concat(
[self.attention_hidden, self.attention_context], axis=-1)
_, (self.decoder_hidden, self.decoder_cell) = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(
self.decoder_hidden,
p=self.p_decoder_dropout,
training=self.training)
# decode output one step
decoder_hidden_attention_context = paddle.concat(
[self.decoder_hidden, self.attention_context], axis=-1)
decoder_output = self.linear_projection(
decoder_hidden_attention_context)
if self.use_stop_token:
stop_logit = self.stop_layer(decoder_hidden_attention_context)
return decoder_output, self.attention_weights, stop_logit
return decoder_output, self.attention_weights
def forward(self, keys, querys, mask):
"""Calculate forward propagation of tacotron2 decoder.
Parameters
----------
keys: Tensor[shape=(B, T_key, C)]
Batch of the sequences of padded output from encoder.
querys: Tensor[shape(B, T_query, C)]
Batch of the sequences of padded mel spectrogram.
mask: Tensor
Mask generated with text length. Shape should be (B, T_key, 1).
Returns
-------
mel_output: Tensor [shape=(B, T_query, C)]
Output sequence of features.
alignments: Tensor [shape=(B, T_query, T_key)]
Attention weights.
"""
self._initialize_decoder_states(keys)
self.mask = mask
querys = paddle.reshape(
querys,
[querys.shape[0], querys.shape[1] // self.reduction_factor, -1])
start_step = paddle.zeros(
shape=[querys.shape[0], 1, querys.shape[-1]], dtype=querys.dtype)
querys = paddle.concat([start_step, querys], axis=1)
querys = self.prenet(querys)
mel_outputs, alignments = [], []
stop_logits = []
# Ignore the last time step
while len(mel_outputs) < querys.shape[1] - 1:
query = querys[:, len(mel_outputs), :]
if self.use_stop_token:
mel_output, attention_weights, stop_logit = self._decode(query)
else:
mel_output, attention_weights = self._decode(query)
mel_outputs.append(mel_output)
alignments.append(attention_weights)
if self.use_stop_token:
stop_logits.append(stop_logit)
alignments = paddle.stack(alignments, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
if self.use_stop_token:
stop_logits = paddle.concat(stop_logits, axis=1)
return mel_outputs, alignments, stop_logits
return mel_outputs, alignments
def infer(self, key, max_decoder_steps=1000):
"""Calculate forward propagation of tacotron2 decoder.
Parameters
----------
keys: Tensor [shape=(B, T_key, C)]
Batch of the sequences of padded output from encoder.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
mel_output: Tensor [shape=(B, T_mel, C)]
Output sequence of features.
alignments: Tensor [shape=(B, T_mel, T_key)]
Attention weights.
"""
self._initialize_decoder_states(key)
self.mask = None # mask is not needed for single instance inference
encoder_steps = key.shape[1]
# [B, C]
start_step = paddle.zeros(
shape=[key.shape[0], self.d_mels * self.reduction_factor],
dtype=key.dtype)
query = start_step # [B, C]
first_hit_end = None
mel_outputs, alignments = [], []
stop_logits = []
for i in trange(max_decoder_steps):
query = self.prenet(query)
if self.use_stop_token:
mel_output, alignment, stop_logit = self._decode(query)
else:
mel_output, alignment = self._decode(query)
mel_outputs.append(mel_output)
alignments.append(alignment) # (B=1, T)
if self.use_stop_token:
stop_logits.append(stop_logit)
if self.use_stop_token:
if F.sigmoid(stop_logit) > 0.5:
print("hit stop condition!")
break
else:
if int(paddle.argmax(alignment[0])) == encoder_steps - 1:
if first_hit_end is None:
first_hit_end = i
elif i > (first_hit_end + 20):
print("content exhausted!")
break
if len(mel_outputs) == max_decoder_steps:
print("Warning! Reached max decoder steps!!!")
break
query = mel_output
alignments = paddle.stack(alignments, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
if self.use_stop_token:
stop_logits = paddle.concat(stop_logits, axis=1)
return mel_outputs, alignments, stop_logits
return mel_outputs, alignments
class Tacotron2(nn.Layer):
"""Tacotron2 model for end-to-end text-to-speech (E2E-TTS).
This is a model of Spectrogram prediction network in Tacotron2 described
in `Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions <https://arxiv.org/abs/1712.05884>`_,
which converts the sequence of characters
into the sequence of mel spectrogram.
Parameters
----------
vocab_size : int
Vocabulary size of phons of the model.
n_tones: int
Vocabulary size of tones of the model. Defaults to None. If provided,
the model has an extra tone embedding.
d_mels: int
Number of mel bands.
d_encoder: int
Hidden size in encoder module.
encoder_conv_layers: int
Number of conv layers in encoder.
encoder_kernel_size: int
Kernel size of conv layers in encoder.
d_prenet: int
Hidden size in decoder prenet.
d_attention_rnn: int
Attention rnn layer hidden size in decoder.
d_decoder_rnn: int
Decoder rnn layer hidden size in decoder.
attention_filters: int
Filter size of the conv layer in location sensitive attention.
attention_kernel_size: int
Kernel size of the conv layer in location sensitive attention.
d_attention: int
Hidden size of the linear layer in location sensitive attention.
d_postnet: int
Hidden size of postnet.
postnet_kernel_size: int
Kernel size of the conv layer in postnet.
postnet_conv_layers: int
Number of conv layers in postnet.
reduction_factor: int
Reduction factor of tacotron2.
p_encoder_dropout: float
Droput probability in encoder.
p_prenet_dropout: float
Droput probability in decoder prenet.
p_attention_dropout: float
Droput probability in location sensitive attention.
p_decoder_dropout: float
Droput probability in decoder.
p_postnet_dropout: float
Droput probability in postnet.
d_global_condition: int
Feature size of global condition. Defaults to None. If provided, The
model assumes a global condition that is concatenated to the encoder
outputs.
"""
def __init__(self,
vocab_size,
n_tones=None,
d_mels: int=80,
d_encoder: int=512,
encoder_conv_layers: int=3,
encoder_kernel_size: int=5,
d_prenet: int=256,
d_attention_rnn: int=1024,
d_decoder_rnn: int=1024,
attention_filters: int=32,
attention_kernel_size: int=31,
d_attention: int=128,
d_postnet: int=512,
postnet_kernel_size: int=5,
postnet_conv_layers: int=5,
reduction_factor: int=1,
p_encoder_dropout: float=0.5,
p_prenet_dropout: float=0.5,
p_attention_dropout: float=0.1,
p_decoder_dropout: float=0.1,
p_postnet_dropout: float=0.5,
d_global_condition=None,
use_stop_token=False):
super().__init__()
std = math.sqrt(2.0 / (vocab_size + d_encoder))
val = math.sqrt(3.0) * std # uniform bounds for std
self.embedding = nn.Embedding(
vocab_size, d_encoder, weight_attr=I.Uniform(-val, val))
if n_tones:
self.embedding_tones = nn.Embedding(
n_tones,
d_encoder,
padding_idx=0,
weight_attr=I.Uniform(-0.1 * val, 0.1 * val))
self.toned = n_tones is not None
self.encoder = Tacotron2Encoder(d_encoder, encoder_conv_layers,
encoder_kernel_size, p_encoder_dropout)
# input augmentation scheme: concat global condition to the encoder output
if d_global_condition is not None:
d_encoder += d_global_condition
self.decoder = Tacotron2Decoder(
d_mels,
reduction_factor,
d_encoder,
d_prenet,
d_attention_rnn,
d_decoder_rnn,
d_attention,
attention_filters,
attention_kernel_size,
p_prenet_dropout,
p_attention_dropout,
p_decoder_dropout,
use_stop_token=use_stop_token)
self.postnet = DecoderPostNet(
d_mels=d_mels * reduction_factor,
d_hidden=d_postnet,
kernel_size=postnet_kernel_size,
num_layers=postnet_conv_layers,
dropout=p_postnet_dropout)
def forward(self,
text_inputs,
text_lens,
mels,
output_lens=None,
tones=None,
global_condition=None):
"""Calculate forward propagation of tacotron2.
Parameters
----------
text_inputs: Tensor [shape=(B, T_text)]
Batch of the sequencees of padded character ids.
text_lens: Tensor [shape=(B,)]
Batch of lengths of each text input batch.
mels: Tensor [shape(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
output_lens: Tensor [shape=(B,)], optional
Batch of lengths of each mels batch. Defaults to None.
tones: Tensor [shape=(B, T_text)]
Batch of sequences of padded tone ids.
global_condition: Tensor [shape(B, C)]
Batch of global conditions. Defaults to None. If the
`d_global_condition` of the model is not None, this input should be
provided.
use_stop_token: bool
Whether to include a binary classifier to predict the stop token.
Defaults to False.
Returns
-------
outputs : Dict[str, Tensor]
mel_output: output sequence of features (B, T_mel, C);
mel_outputs_postnet: output sequence of features after postnet (B, T_mel, C);
alignments: attention weights (B, T_mel, T_text);
stop_logits: output sequence of stop logits (B, T_mel)
"""
# input of embedding must be int64
text_inputs = paddle.cast(text_inputs, 'int64')
embedded_inputs = self.embedding(text_inputs)
if self.toned:
embedded_inputs += self.embedding_tones(tones)
encoder_outputs = self.encoder(embedded_inputs, text_lens)
if global_condition is not None:
global_condition = global_condition.unsqueeze(1)
global_condition = paddle.expand(global_condition,
[-1, encoder_outputs.shape[1], -1])
encoder_outputs = paddle.concat([encoder_outputs, global_condition],
-1)
# [B, T_enc, 1]
mask = sequence_mask(
text_lens, dtype=encoder_outputs.dtype).unsqueeze(-1)
if self.decoder.use_stop_token:
mel_outputs, alignments, stop_logits = self.decoder(
encoder_outputs, mels, mask=mask)
else:
mel_outputs, alignments = self.decoder(
encoder_outputs, mels, mask=mask)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
if output_lens is not None:
# [B, T_dec, 1]
mask = sequence_mask(output_lens).unsqueeze(-1)
mel_outputs = mel_outputs * mask # [B, T, C]
mel_outputs_postnet = mel_outputs_postnet * mask # [B, T, C]
outputs = {
"mel_output": mel_outputs,
"mel_outputs_postnet": mel_outputs_postnet,
"alignments": alignments
}
if self.decoder.use_stop_token:
outputs["stop_logits"] = stop_logits
return outputs
@paddle.no_grad()
def infer(self,
text_inputs,
max_decoder_steps=1000,
tones=None,
global_condition=None):
"""Generate the mel sepctrogram of features given the sequences of character ids.
Parameters
----------
text_inputs: Tensor [shape=(B, T_text)]
Batch of the sequencees of padded character ids.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
outputs : Dict[str, Tensor]
mel_output: output sequence of sepctrogram (B, T_mel, C);
mel_outputs_postnet: output sequence of sepctrogram after postnet (B, T_mel, C);
stop_logits: output sequence of stop logits (B, T_mel);
alignments: attention weights (B, T_mel, T_text). This key is only
present when `use_stop_token` is True.
"""
# input of embedding must be int64
text_inputs = paddle.cast(text_inputs, 'int64')
embedded_inputs = self.embedding(text_inputs)
if self.toned:
embedded_inputs += self.embedding_tones(tones)
encoder_outputs = self.encoder(embedded_inputs)
if global_condition is not None:
global_condition = global_condition.unsqueeze(1)
global_condition = paddle.expand(global_condition,
[-1, encoder_outputs.shape[1], -1])
encoder_outputs = paddle.concat([encoder_outputs, global_condition],
-1)
if self.decoder.use_stop_token:
mel_outputs, alignments, stop_logits = self.decoder.infer(
encoder_outputs, max_decoder_steps=max_decoder_steps)
else:
mel_outputs, alignments = self.decoder.infer(
encoder_outputs, max_decoder_steps=max_decoder_steps)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
outputs = {
"mel_output": mel_outputs,
"mel_outputs_postnet": mel_outputs_postnet,
"alignments": alignments
}
if self.decoder.use_stop_token:
outputs["stop_logits"] = stop_logits
return outputs
@classmethod
def from_pretrained(cls, config, checkpoint_path):
"""Build a Tacotron2 model from a pretrained model.
Parameters
----------
config: yacs.config.CfgNode
model configs
checkpoint_path: Path or str
the path of pretrained model checkpoint, without extension name
Returns
-------
ConditionalWaveFlow
The model built from pretrained result.
"""
model = cls(vocab_size=config.model.vocab_size,
n_tones=config.model.n_tones,
d_mels=config.data.n_mels,
d_encoder=config.model.d_encoder,
encoder_conv_layers=config.model.encoder_conv_layers,
encoder_kernel_size=config.model.encoder_kernel_size,
d_prenet=config.model.d_prenet,
d_attention_rnn=config.model.d_attention_rnn,
d_decoder_rnn=config.model.d_decoder_rnn,
attention_filters=config.model.attention_filters,
attention_kernel_size=config.model.attention_kernel_size,
d_attention=config.model.d_attention,
d_postnet=config.model.d_postnet,
postnet_kernel_size=config.model.postnet_kernel_size,
postnet_conv_layers=config.model.postnet_conv_layers,
reduction_factor=config.model.reduction_factor,
p_encoder_dropout=config.model.p_encoder_dropout,
p_prenet_dropout=config.model.p_prenet_dropout,
p_attention_dropout=config.model.p_attention_dropout,
p_decoder_dropout=config.model.p_decoder_dropout,
p_postnet_dropout=config.model.p_postnet_dropout,
d_global_condition=config.model.d_global_condition,
use_stop_token=config.model.use_stop_token)
checkpoint.load_parameters(model, checkpoint_path=checkpoint_path)
return model
class Tacotron2Loss(nn.Layer):
""" Tacotron2 Loss module
"""
def __init__(self,
use_stop_token_loss=True,
use_guided_attention_loss=False,
sigma=0.2):
"""Tacotron 2 Criterion.
Args:
use_stop_token_loss (bool, optional): Whether to use a loss for stop token prediction. Defaults to True.
use_guided_attention_loss (bool, optional): Whether to use a loss for attention weights. Defaults to False.
sigma (float, optional): Hyper-parameter sigma for guided attention loss. Defaults to 0.2.
"""
super().__init__()
self.spec_criterion = nn.MSELoss()
self.use_stop_token_loss = use_stop_token_loss
self.use_guided_attention_loss = use_guided_attention_loss
self.attn_criterion = guided_attention_loss
self.stop_criterion = paddle.nn.BCEWithLogitsLoss()
self.sigma = sigma
def forward(self,
mel_outputs,
mel_outputs_postnet,
mel_targets,
attention_weights=None,
slens=None,
plens=None,
stop_logits=None):
"""Calculate tacotron2 loss.
Parameters
----------
mel_outputs: Tensor [shape=(B, T_mel, C)]
Output mel spectrogram sequence.
mel_outputs_postnet: Tensor [shape(B, T_mel, C)]
Output mel spectrogram sequence after postnet.
mel_targets: Tensor [shape=(B, T_mel, C)]
Target mel spectrogram sequence.
attention_weights: Tensor [shape=(B, T_mel, T_enc)]
Attention weights. This should be provided when
`use_guided_attention_loss` is True.
slens: Tensor [shape=(B,)]
Number of frames of mel spectrograms. This should be provided when
`use_guided_attention_loss` is True.
plens: Tensor [shape=(B, )]
Number of text or phone ids of each utterance. This should be
provided when `use_guided_attention_loss` is True.
stop_logits: Tensor [shape=(B, T_mel)]
Stop logits of each mel spectrogram frame. This should be provided
when `use_stop_token_loss` is True.
Returns
-------
losses : Dict[str, Tensor]
loss: the sum of the other three losses;
mel_loss: MSE loss compute by mel_targets and mel_outputs;
post_mel_loss: MSE loss compute by mel_targets and mel_outputs_postnet;
guided_attn_loss: Guided attention loss for attention weights;
stop_loss: Binary cross entropy loss for stop token prediction.
"""
mel_loss = self.spec_criterion(mel_outputs, mel_targets)
post_mel_loss = self.spec_criterion(mel_outputs_postnet, mel_targets)
total_loss = mel_loss + post_mel_loss
if self.use_guided_attention_loss:
gal_loss = self.attn_criterion(attention_weights, slens, plens,
self.sigma)
total_loss += gal_loss
if self.use_stop_token_loss:
T_dec = mel_targets.shape[1]
stop_labels = F.one_hot(slens - 1, num_classes=T_dec)
stop_token_loss = self.stop_criterion(stop_logits, stop_labels)
total_loss += stop_token_loss
losses = {
"loss": total_loss,
"mel_loss": mel_loss,
"post_mel_loss": post_mel_loss
}
if self.use_guided_attention_loss:
losses["guided_attn_loss"] = gal_loss
if self.use_stop_token_loss:
losses["stop_loss"] = stop_token_loss
return losses
|
tests/threaded/test_thread.py | simatei/toolbelt | 544 | 12611711 | """Module containing the tests for requests_toolbelt.threaded.thread."""
try:
import queue # Python 3
except ImportError:
import Queue as queue
import threading
import unittest
import uuid
try:
from unittest import mock
except ImportError:
import mock
import requests.exceptions
from requests_toolbelt.threaded import thread
def _make_mocks():
return (mock.MagicMock() for _ in range(4))
def _initialize_a_session_thread(session=None, job_queue=None,
response_queue=None, exception_queue=None):
if job_queue is None:
job_queue = queue.Queue()
with mock.patch.object(threading, 'Thread') as Thread:
thread_instance = mock.MagicMock()
Thread.return_value = thread_instance
st = thread.SessionThread(
initialized_session=session,
job_queue=job_queue,
response_queue=response_queue,
exception_queue=exception_queue,
)
return (st, thread_instance, Thread)
class TestSessionThread(unittest.TestCase):
"""Tests for requests_toolbelt.threaded.thread.SessionThread."""
def test_thread_initialization(self):
"""Test the way a SessionThread is initialized.
We want to ensure that we creat a thread with a name generated by the
uuid module, and that we pass the right method to use as a target.
"""
with mock.patch.object(uuid, 'uuid4', return_value='test'):
(st, thread_instance, Thread) = _initialize_a_session_thread()
Thread.assert_called_once_with(target=st._make_request, name='test')
assert thread_instance.daemon is True
assert thread_instance._state is 0
thread_instance.start.assert_called_once_with()
def test_is_alive_proxies_to_worker(self):
"""Test that we proxy the is_alive method to the Thread."""
job_queue = queue.Queue()
with mock.patch.object(threading, 'Thread') as Thread:
thread_instance = mock.MagicMock()
Thread.return_value = thread_instance
st = thread.SessionThread(None, job_queue, None, None)
st.is_alive()
thread_instance.is_alive.assert_called_once_with()
def test_join_proxies_to_worker(self):
"""Test that we proxy the join method to the Thread."""
st, thread_instance, _ = _initialize_a_session_thread()
st.join()
thread_instance.join.assert_called_once_with()
def test_handle_valid_request(self):
"""Test that a response is added to the right queue."""
session, job_queue, response_queue, exception_queue = _make_mocks()
response = mock.MagicMock()
session.request.return_value = response
st, _, _ = _initialize_a_session_thread(
session, job_queue, response_queue, exception_queue)
st._handle_request({'method': 'GET', 'url': 'http://example.com'})
session.request.assert_called_once_with(
method='GET',
url='http://example.com'
)
response_queue.put.assert_called_once_with(
({'method': 'GET', 'url': 'http://example.com'}, response)
)
assert exception_queue.put.called is False
assert job_queue.get.called is False
assert job_queue.get_nowait.called is False
assert job_queue.get_nowait.called is False
assert job_queue.task_done.called is True
def test_handle_invalid_request(self):
"""Test that exceptions from requests are added to the right queue."""
session, job_queue, response_queue, exception_queue = _make_mocks()
exception = requests.exceptions.InvalidURL()
def _side_effect(*args, **kwargs):
raise exception
# Make the request raise an exception
session.request.side_effect = _side_effect
st, _, _ = _initialize_a_session_thread(
session, job_queue, response_queue, exception_queue)
st._handle_request({'method': 'GET', 'url': 'http://example.com'})
session.request.assert_called_once_with(
method='GET',
url='http://example.com'
)
exception_queue.put.assert_called_once_with(
({'method': 'GET', 'url': 'http://example.com'}, exception)
)
assert response_queue.put.called is False
assert job_queue.get.called is False
assert job_queue.get_nowait.called is False
assert job_queue.get_nowait.called is False
assert job_queue.task_done.called is True
def test_make_request(self):
"""Test that _make_request exits when the queue is Empty."""
job_queue = next(_make_mocks())
job_queue.get_nowait.side_effect = queue.Empty()
st, _, _ = _initialize_a_session_thread(job_queue=job_queue)
st._make_request()
job_queue.get_nowait.assert_called_once_with()
|
tools/scripts/update_assets_metadata.py | rotkehlchenio/rotkehlchen | 137 | 12611720 | <gh_stars>100-1000
#!/usr/bin/env python
import hashlib
import json
import re
import sys
from pathlib import Path
src_dir = Path(__file__).parent.parent.parent / 'rotkehlchen'
ASSETS_JSON = src_dir / 'data/all_assets.json'
ASSETS_META = src_dir / 'data/all_assets.meta'
ASSETS_TEST = src_dir / 'tests/unit/test_assets.py'
with open(ASSETS_META, 'r') as assets_meta:
old_meta = json.load(assets_meta)
with open(ASSETS_JSON, 'rb') as assets_json:
new_md5 = hashlib.new('md5')
new_md5.update(assets_json.read())
if new_md5.hexdigest() == old_meta['md5']:
print('Assets meta file is up-to-date.')
sys.exit(0)
new_meta = json.dumps({
'md5': new_md5.hexdigest(),
'version': old_meta['version'] + 1
})
with open(ASSETS_META, 'w') as assets_meta:
assets_meta.write(new_meta + '\n')
with open(ASSETS_TEST, 'r+') as assets_test:
test = assets_test.read()
assets_test.seek(0)
needle = r'last_meta = {.md5.: .*.version.: .*}'
replacement = 'last_meta = ' + new_meta.replace('"', '\'')
assets_test.write(re.sub(needle, replacement, test))
|
src/genie/libs/parser/iosxe/tests/ShowLispInstanceIdEthernetServer/cli/equal/golden_output_3_expected.py | balmasea/genieparser | 204 | 12611723 | expected_output = {
"instance_id": {
4097: {"lisp": 0},
4099: {"lisp": 0},
4100: {"lisp": 0},
8188: {
"lisp": 0,
"site_name": {
"site_uci": {
"any-mac": {
"last_register": "never",
"up": "no",
"who_last_registered": "--",
"inst_id": 8188,
},
"1416.9dff.e928/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.eae8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.eb28/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.ebc8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.1328/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.13e8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.16c8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.2428/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.10a9/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.01eb/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.1bcb/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.248b/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.254b/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.264b/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.260c/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.278b/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.d16f/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.1074/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.10b4/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.10d4/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.54f4/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.5616/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6816/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6955/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6ad5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6af5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6a16/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6d95/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6ef5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.6ff5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7095/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.70d5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7395/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.73f5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7336/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7495/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7416/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7555/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.75f5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7695/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.76f5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.77b5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7855/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7875/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7895/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.78f5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7836/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7955/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7975/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7936/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7a55/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7af5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7a36/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7b75/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7b95/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7bb5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7bf5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7c75/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7cd5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7cf5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7d55/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7dd5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.7e55/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.8436/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.8555/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.8636/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"1416.9dff.89f5/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.130.4:60995",
"inst_id": 8188,
},
"2c57.41ff.6e29/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.118:38318",
"inst_id": 8188,
},
"2c57.41ff.96ec/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.16:36870",
"inst_id": 8188,
},
"2c57.41ff.9929/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.11:52315",
"inst_id": 8188,
},
"2c57.41ff.9a41/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.22:50531",
"inst_id": 8188,
},
"2c57.41ff.9b58/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.22:50531",
"inst_id": 8188,
},
"2c57.41ff.9b78/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.33:17709",
"inst_id": 8188,
},
"2c57.41ff.9b90/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.44:27830",
"inst_id": 8188,
},
"2c57.41ff.9ba0/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.128.225:16171",
"inst_id": 8188,
},
"2c57.41ff.a6cc/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.1:25983",
"inst_id": 8188,
},
"2c57.41ff.a6d4/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.44:27830",
"inst_id": 8188,
},
"2c57.41ff.a6d8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.128.225:16171",
"inst_id": 8188,
},
"2c57.41ff.af5c/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.22:50531",
"inst_id": 8188,
},
"2c57.41ff.afa0/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.1:25983",
"inst_id": 8188,
},
"2c57.41ff.b1e0/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.1:25983",
"inst_id": 8188,
},
"2c57.41ff.b1e8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.128.254:32391",
"inst_id": 8188,
},
"2c57.41ff.b119/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.44:27830",
"inst_id": 8188,
},
"2c57.41ff.b11d/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.128.225:16171",
"inst_id": 8188,
},
"2c57.41ff.b121/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.128.225:16171",
"inst_id": 8188,
},
"2c57.41ff.b270/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.17:12848",
"inst_id": 8188,
},
"2c57.41ff.b29c/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.17:12848",
"inst_id": 8188,
},
"2c57.41ff.b2bc/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.17:12848",
"inst_id": 8188,
},
"2c57.41ff.b2d0/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.22:50531",
"inst_id": 8188,
},
"2c57.41ff.b2d8/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.22:50531",
"inst_id": 8188,
},
"2c57.41ff.b231/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.30:20273",
"inst_id": 8188,
},
"2c57.41ff.b23d/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.11:52315",
"inst_id": 8188,
},
"2c57.41ff.b245/48": {
"last_register": "2w1d",
"up": "yes#",
"who_last_registered": "10.8.129.44:27830",
"inst_id": 8188,
},
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|
lib/ch.py | Pandinosaurus/videoavatars | 531 | 12611724 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
import numpy as np
import chumpy as ch
import scipy.sparse as sp
from chumpy.utils import col
class sp_dot(ch.Ch):
terms = 'a',
dterms = 'b',
def compute_r(self):
return self.a.dot(self.b.r)
def compute(self):
# To stay consistent with numpy, we must upgrade 1D arrays to 2D
ar = sp.csr_matrix((self.a.data, self.a.indices, self.a.indptr),
shape=(max(np.sum(self.a.shape[:-1]), 1), self.a.shape[-1]))
br = col(self.b.r) if len(self.b.r.shape) < 2 else self.b.r.reshape((self.b.r.shape[0], -1))
if br.ndim <= 1:
return ar
elif br.ndim <= 2:
return sp.kron(ar, sp.eye(br.shape[1], br.shape[1]))
else:
raise NotImplementedError
def compute_dr_wrt(self, wrt):
if wrt is self.b:
return self.compute() |
scripts/build/runner/printonly.py | AnthonyDiGirolamo/connectedhomeip | 3,495 | 12611747 | # Copyright (c) 2021 Project CHIP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shlex
class PrintOnlyRunner:
def __init__(self, output_file, root: str):
self.output_file = output_file
self.dry_run = True
self.root = root
def StartCommandExecution(self):
self.output_file.write(
"# Commands will be run in CHIP project root.\n")
self.output_file.write('cd "%s"\n\n' % self.root)
def Run(self, cmd, title=None):
if title:
self.output_file.write("# " + title + "\n")
self.output_file.write(
" ".join([shlex.quote(part) for part in cmd]) + "\n")
self.output_file.write("\n")
|
pypy/module/test_lib_pypy/cffi_tests/cffi0/test_verify.py | m4sterchain/mesapy | 381 | 12611748 | # Generated by pypy/tool/import_cffi.py
import py, re
import sys, os, math, weakref
from cffi import FFI, VerificationError, VerificationMissing, model, FFIError
from pypy.module.test_lib_pypy.cffi_tests.support import *
lib_m = ['m']
if sys.platform == 'win32':
#there is a small chance this fails on Mingw via environ $CC
import distutils.ccompiler
if distutils.ccompiler.get_default_compiler() == 'msvc':
lib_m = ['msvcrt']
pass # no obvious -Werror equivalent on MSVC
else:
if (sys.platform == 'darwin' and
[int(x) for x in os.uname()[2].split('.')] >= [11, 0, 0]):
# assume a standard clang or gcc
extra_compile_args = ['-Werror', '-Wall', '-Wextra', '-Wconversion']
# special things for clang
extra_compile_args.append('-Qunused-arguments')
else:
# assume a standard gcc
extra_compile_args = ['-Werror', '-Wall', '-Wextra', '-Wconversion']
class FFI(FFI):
def verify(self, *args, **kwds):
return super(FFI, self).verify(
*args, extra_compile_args=extra_compile_args, **kwds)
def setup_module():
import cffi.verifier
cffi.verifier.cleanup_tmpdir()
#
# check that no $ sign is produced in the C file; it used to be the
# case that anonymous enums would produce '$enum_$1', which was
# used as part of a function name. GCC accepts such names, but it's
# apparently non-standard.
_r_comment = re.compile(r"/\*.*?\*/|//.*?$", re.DOTALL | re.MULTILINE)
_r_string = re.compile(r'\".*?\"')
def _write_source_and_check(self, file=None):
base_write_source(self, file)
if file is None:
f = open(self.sourcefilename)
data = f.read()
f.close()
data = _r_comment.sub(' ', data)
data = _r_string.sub('"skipped"', data)
assert '$' not in data
base_write_source = cffi.verifier.Verifier._write_source
cffi.verifier.Verifier._write_source = _write_source_and_check
def test_module_type():
import cffi.verifier
ffi = FFI()
lib = ffi.verify()
if hasattr(lib, '_cffi_python_module'):
print('verify got a PYTHON module')
if hasattr(lib, '_cffi_generic_module'):
print('verify got a GENERIC module')
expected_generic = (cffi.verifier._FORCE_GENERIC_ENGINE or
'__pypy__' in sys.builtin_module_names)
assert hasattr(lib, '_cffi_python_module') == (not expected_generic)
assert hasattr(lib, '_cffi_generic_module') == expected_generic
def test_missing_function(ffi=None):
# uses the FFI hacked above with '-Werror'
if ffi is None:
ffi = FFI()
ffi.cdef("void some_completely_unknown_function();")
try:
lib = ffi.verify()
except (VerificationError, OSError):
pass # expected case: we get a VerificationError
else:
# but depending on compiler and loader details, maybe
# 'lib' could actually be imported but will fail if we
# actually try to call the unknown function... Hard
# to test anything more.
pass
def test_missing_function_import_error():
# uses the original FFI that just gives a warning during compilation
import cffi
test_missing_function(ffi=cffi.FFI())
def test_simple_case():
ffi = FFI()
ffi.cdef("double sin(double x);")
lib = ffi.verify('#include <math.h>', libraries=lib_m)
assert lib.sin(1.23) == math.sin(1.23)
def _Wconversion(cdef, source, **kargs):
if sys.platform in ('win32', 'darwin'):
py.test.skip("needs GCC")
ffi = FFI()
ffi.cdef(cdef)
py.test.raises(VerificationError, ffi.verify, source, **kargs)
extra_compile_args_orig = extra_compile_args[:]
extra_compile_args.remove('-Wconversion')
try:
lib = ffi.verify(source, **kargs)
finally:
extra_compile_args[:] = extra_compile_args_orig
return lib
def test_Wconversion_unsigned():
_Wconversion("unsigned foo(void);",
"int foo(void) { return -1;}")
def test_Wconversion_integer():
_Wconversion("short foo(void);",
"long long foo(void) { return 1<<sizeof(short);}")
def test_Wconversion_floating():
lib = _Wconversion("float sin(double);",
"#include <math.h>", libraries=lib_m)
res = lib.sin(1.23)
assert res != math.sin(1.23) # not exact, because of double->float
assert abs(res - math.sin(1.23)) < 1E-5
def test_Wconversion_float2int():
_Wconversion("int sinf(float);",
"#include <math.h>", libraries=lib_m)
def test_Wconversion_double2int():
_Wconversion("int sin(double);",
"#include <math.h>", libraries=lib_m)
def test_rounding_1():
ffi = FFI()
ffi.cdef("double sinf(float x);")
lib = ffi.verify('#include <math.h>', libraries=lib_m)
res = lib.sinf(1.23)
assert res != math.sin(1.23) # not exact, because of double->float
assert abs(res - math.sin(1.23)) < 1E-5
def test_rounding_2():
ffi = FFI()
ffi.cdef("double sin(float x);")
lib = ffi.verify('#include <math.h>', libraries=lib_m)
res = lib.sin(1.23)
assert res != math.sin(1.23) # not exact, because of double->float
assert abs(res - math.sin(1.23)) < 1E-5
def test_strlen_exact():
ffi = FFI()
ffi.cdef("size_t strlen(const char *s);")
lib = ffi.verify("#include <string.h>")
assert lib.strlen(b"hi there!") == 9
def test_strlen_approximate():
lib = _Wconversion("int strlen(char *s);",
"#include <string.h>")
assert lib.strlen(b"hi there!") == 9
def test_return_approximate():
for typename in ['short', 'int', 'long', 'long long']:
ffi = FFI()
ffi.cdef("%s foo(signed char x);" % typename)
lib = ffi.verify("signed char foo(signed char x) { return x;}")
assert lib.foo(-128) == -128
assert lib.foo(+127) == +127
def test_strlen_array_of_char():
ffi = FFI()
ffi.cdef("size_t strlen(char[]);")
lib = ffi.verify("#include <string.h>")
assert lib.strlen(b"hello") == 5
def test_longdouble():
ffi = FFI()
ffi.cdef("long double sinl(long double x);")
lib = ffi.verify('#include <math.h>', libraries=lib_m)
for input in [1.23,
ffi.cast("double", 1.23),
ffi.cast("long double", 1.23)]:
x = lib.sinl(input)
assert repr(x).startswith("<cdata 'long double'")
assert (float(x) - math.sin(1.23)) < 1E-10
def test_longdouble_precision():
# Test that we don't loose any precision of 'long double' when
# passing through Python and CFFI.
ffi = FFI()
ffi.cdef("long double step1(long double x);")
SAME_SIZE = ffi.sizeof("long double") == ffi.sizeof("double")
lib = ffi.verify("""
long double step1(long double x)
{
return 4*x-x*x;
}
""")
def do(cast_to_double):
x = 0.9789
for i in range(10000):
x = lib.step1(x)
if cast_to_double:
x = float(x)
return float(x)
more_precise = do(False)
less_precise = do(True)
if SAME_SIZE:
assert more_precise == less_precise
else:
assert abs(more_precise - less_precise) > 0.1
# Check the particular results on Intel
import platform
if (platform.machine().startswith('i386') or
platform.machine().startswith('i486') or
platform.machine().startswith('i586') or
platform.machine().startswith('i686') or
platform.machine().startswith('x86')):
assert abs(more_precise - 0.656769) < 0.001
assert abs(less_precise - 3.99091) < 0.001
else:
py.test.skip("don't know the very exact precision of 'long double'")
all_primitive_types = model.PrimitiveType.ALL_PRIMITIVE_TYPES
if sys.platform == 'win32':
all_primitive_types = all_primitive_types.copy()
del all_primitive_types['ssize_t']
all_integer_types = sorted(tp for tp in all_primitive_types
if all_primitive_types[tp] == 'i')
all_float_types = sorted(tp for tp in all_primitive_types
if all_primitive_types[tp] == 'f')
def all_signed_integer_types(ffi):
return [x for x in all_integer_types if int(ffi.cast(x, -1)) < 0]
def all_unsigned_integer_types(ffi):
return [x for x in all_integer_types if int(ffi.cast(x, -1)) > 0]
def test_primitive_category():
for typename in all_primitive_types:
tp = model.PrimitiveType(typename)
C = tp.is_char_type()
F = tp.is_float_type()
X = tp.is_complex_type()
I = tp.is_integer_type()
assert C == (typename in ('char', 'wchar_t', 'char16_t', 'char32_t'))
assert F == (typename in ('float', 'double', 'long double'))
assert X == (typename in ('float _Complex', 'double _Complex'))
assert I + F + C + X == 1 # one and only one of them is true
def test_all_integer_and_float_types():
typenames = []
for typename in all_primitive_types:
if (all_primitive_types[typename] == 'c' or
all_primitive_types[typename] == 'j' or # complex
typename == '_Bool' or typename == 'long double'):
pass
else:
typenames.append(typename)
#
ffi = FFI()
ffi.cdef('\n'.join(["%s foo_%s(%s);" % (tp, tp.replace(' ', '_'), tp)
for tp in typenames]))
lib = ffi.verify('\n'.join(["%s foo_%s(%s x) { return (%s)(x+1); }" %
(tp, tp.replace(' ', '_'), tp, tp)
for tp in typenames]))
for typename in typenames:
foo = getattr(lib, 'foo_%s' % typename.replace(' ', '_'))
assert foo(42) == 43
if sys.version < '3':
assert foo(long(44)) == 45
assert foo(ffi.cast(typename, 46)) == 47
py.test.raises(TypeError, foo, ffi.NULL)
#
# check for overflow cases
if all_primitive_types[typename] == 'f':
continue
for value in [-2**80, -2**40, -2**20, -2**10, -2**5, -1,
2**5, 2**10, 2**20, 2**40, 2**80]:
overflows = int(ffi.cast(typename, value)) != value
if overflows:
py.test.raises(OverflowError, foo, value)
else:
assert foo(value) == value + 1
def test_var_signed_integer_types():
ffi = FFI()
lst = all_signed_integer_types(ffi)
csource = "\n".join(["%s somevar_%s;" % (tp, tp.replace(' ', '_'))
for tp in lst])
ffi.cdef(csource)
lib = ffi.verify(csource)
for tp in lst:
varname = 'somevar_%s' % tp.replace(' ', '_')
sz = ffi.sizeof(tp)
max = (1 << (8*sz-1)) - 1
min = -(1 << (8*sz-1))
setattr(lib, varname, max)
assert getattr(lib, varname) == max
setattr(lib, varname, min)
assert getattr(lib, varname) == min
py.test.raises(OverflowError, setattr, lib, varname, max+1)
py.test.raises(OverflowError, setattr, lib, varname, min-1)
def test_var_unsigned_integer_types():
ffi = FFI()
lst = all_unsigned_integer_types(ffi)
csource = "\n".join(["%s somevar_%s;" % (tp, tp.replace(' ', '_'))
for tp in lst])
ffi.cdef(csource)
lib = ffi.verify(csource)
for tp in lst:
varname = 'somevar_%s' % tp.replace(' ', '_')
sz = ffi.sizeof(tp)
if tp != '_Bool':
max = (1 << (8*sz)) - 1
else:
max = 1
setattr(lib, varname, max)
assert getattr(lib, varname) == max
setattr(lib, varname, 0)
assert getattr(lib, varname) == 0
py.test.raises(OverflowError, setattr, lib, varname, max+1)
py.test.raises(OverflowError, setattr, lib, varname, -1)
def test_fn_signed_integer_types():
ffi = FFI()
lst = all_signed_integer_types(ffi)
cdefsrc = "\n".join(["%s somefn_%s(%s);" % (tp, tp.replace(' ', '_'), tp)
for tp in lst])
ffi.cdef(cdefsrc)
verifysrc = "\n".join(["%s somefn_%s(%s x) { return x; }" %
(tp, tp.replace(' ', '_'), tp) for tp in lst])
lib = ffi.verify(verifysrc)
for tp in lst:
fnname = 'somefn_%s' % tp.replace(' ', '_')
sz = ffi.sizeof(tp)
max = (1 << (8*sz-1)) - 1
min = -(1 << (8*sz-1))
fn = getattr(lib, fnname)
assert fn(max) == max
assert fn(min) == min
py.test.raises(OverflowError, fn, max + 1)
py.test.raises(OverflowError, fn, min - 1)
def test_fn_unsigned_integer_types():
ffi = FFI()
lst = all_unsigned_integer_types(ffi)
cdefsrc = "\n".join(["%s somefn_%s(%s);" % (tp, tp.replace(' ', '_'), tp)
for tp in lst])
ffi.cdef(cdefsrc)
verifysrc = "\n".join(["%s somefn_%s(%s x) { return x; }" %
(tp, tp.replace(' ', '_'), tp) for tp in lst])
lib = ffi.verify(verifysrc)
for tp in lst:
fnname = 'somefn_%s' % tp.replace(' ', '_')
sz = ffi.sizeof(tp)
if tp != '_Bool':
max = (1 << (8*sz)) - 1
else:
max = 1
fn = getattr(lib, fnname)
assert fn(max) == max
assert fn(0) == 0
py.test.raises(OverflowError, fn, max + 1)
py.test.raises(OverflowError, fn, -1)
def test_char_type():
ffi = FFI()
ffi.cdef("char foo(char);")
lib = ffi.verify("char foo(char x) { return ++x; }")
assert lib.foo(b"A") == b"B"
py.test.raises(TypeError, lib.foo, b"bar")
py.test.raises(TypeError, lib.foo, "bar")
def test_wchar_type():
ffi = FFI()
if ffi.sizeof('wchar_t') == 2:
uniexample1 = u+'\u1234'
uniexample2 = u+'\u1235'
else:
uniexample1 = u+'\U00012345'
uniexample2 = u+'\U00012346'
#
ffi.cdef("wchar_t foo(wchar_t);")
lib = ffi.verify("wchar_t foo(wchar_t x) { return x+1; }")
assert lib.foo(uniexample1) == uniexample2
def test_char16_char32_type():
py.test.skip("XXX test or fully prevent char16_t and char32_t from "
"working in ffi.verify() mode")
def test_no_argument():
ffi = FFI()
ffi.cdef("int foo(void);")
lib = ffi.verify("int foo(void) { return 42; }")
assert lib.foo() == 42
def test_two_arguments():
ffi = FFI()
ffi.cdef("int foo(int, int);")
lib = ffi.verify("int foo(int a, int b) { return a - b; }")
assert lib.foo(40, -2) == 42
def test_macro():
ffi = FFI()
ffi.cdef("int foo(int, int);")
lib = ffi.verify("#define foo(a, b) ((a) * (b))")
assert lib.foo(-6, -7) == 42
def test_ptr():
ffi = FFI()
ffi.cdef("int *foo(int *);")
lib = ffi.verify("int *foo(int *a) { return a; }")
assert lib.foo(ffi.NULL) == ffi.NULL
p = ffi.new("int *", 42)
q = ffi.new("int *", 42)
assert lib.foo(p) == p
assert lib.foo(q) != p
def test_bogus_ptr():
ffi = FFI()
ffi.cdef("int *foo(int *);")
lib = ffi.verify("int *foo(int *a) { return a; }")
py.test.raises(TypeError, lib.foo, ffi.new("short *", 42))
def test_verify_typedefs():
py.test.skip("ignored so far")
types = ['signed char', 'unsigned char', 'int', 'long']
for cdefed in types:
for real in types:
ffi = FFI()
ffi.cdef("typedef %s foo_t;" % cdefed)
if cdefed == real:
ffi.verify("typedef %s foo_t;" % real)
else:
py.test.raises(VerificationError, ffi.verify,
"typedef %s foo_t;" % real)
def test_nondecl_struct():
ffi = FFI()
ffi.cdef("typedef struct foo_s foo_t; int bar(foo_t *);")
lib = ffi.verify("typedef struct foo_s foo_t;\n"
"int bar(foo_t *f) { (void)f; return 42; }\n")
assert lib.bar(ffi.NULL) == 42
def test_ffi_full_struct():
ffi = FFI()
ffi.cdef("struct foo_s { char x; int y; long *z; };")
ffi.verify("struct foo_s { char x; int y; long *z; };")
#
if sys.platform != 'win32': # XXX fixme: only gives warnings
py.test.raises(VerificationError, ffi.verify,
"struct foo_s { char x; int y; int *z; };")
#
py.test.raises(VerificationError, ffi.verify,
"struct foo_s { int y; long *z; };")
#
e = py.test.raises(VerificationError, ffi.verify,
"struct foo_s { int y; char x; long *z; };")
assert str(e.value) == (
"struct foo_s: wrong offset for field 'x'"
" (we have 0, but C compiler says 4)")
#
e = py.test.raises(VerificationError, ffi.verify,
"struct foo_s { char x; int y; long *z; char extra; };")
assert str(e.value) == (
"struct foo_s: wrong total size"
" (we have %d, but C compiler says %d)" % (
ffi.sizeof("struct foo_s"),
ffi.sizeof("struct foo_s") + ffi.sizeof("long*")))
#
# a corner case that we cannot really detect, but where it has no
# bad consequences: the size is the same, but there is an extra field
# that replaces what is just padding in our declaration above
ffi.verify("struct foo_s { char x, extra; int y; long *z; };")
#
e = py.test.raises(VerificationError, ffi.verify,
"struct foo_s { char x; short pad; short y; long *z; };")
assert str(e.value) == (
"struct foo_s: wrong size for field 'y'"
" (we have 4, but C compiler says 2)")
def test_ffi_nonfull_struct():
ffi = FFI()
ffi.cdef("""
struct foo_s {
int x;
...;
};
""")
py.test.raises(VerificationMissing, ffi.sizeof, 'struct foo_s')
py.test.raises(VerificationMissing, ffi.offsetof, 'struct foo_s', 'x')
py.test.raises(VerificationMissing, ffi.new, 'struct foo_s *')
ffi.verify("""
struct foo_s {
int a, b, x, c, d, e;
};
""")
assert ffi.sizeof('struct foo_s') == 6 * ffi.sizeof('int')
assert ffi.offsetof('struct foo_s', 'x') == 2 * ffi.sizeof('int')
def test_ffi_nonfull_alignment():
ffi = FFI()
ffi.cdef("struct foo_s { char x; ...; };")
ffi.verify("struct foo_s { int a, b; char x; };")
assert ffi.sizeof('struct foo_s') == 3 * ffi.sizeof('int')
assert ffi.alignof('struct foo_s') == ffi.sizeof('int')
def _check_field_match(typename, real, expect_mismatch):
ffi = FFI()
testing_by_size = (expect_mismatch == 'by_size')
if testing_by_size:
expect_mismatch = ffi.sizeof(typename) != ffi.sizeof(real)
ffi.cdef("struct foo_s { %s x; ...; };" % typename)
try:
ffi.verify("struct foo_s { %s x; };" % real)
except VerificationError:
if not expect_mismatch:
if testing_by_size and typename != real:
print("ignoring mismatch between %s* and %s* even though "
"they have the same size" % (typename, real))
return
raise AssertionError("unexpected mismatch: %s should be accepted "
"as equal to %s" % (typename, real))
else:
if expect_mismatch:
raise AssertionError("mismatch not detected: "
"%s != %s" % (typename, real))
def test_struct_bad_sized_integer():
for typename in ['int8_t', 'int16_t', 'int32_t', 'int64_t']:
for real in ['int8_t', 'int16_t', 'int32_t', 'int64_t']:
_check_field_match(typename, real, "by_size")
def test_struct_bad_sized_float():
for typename in all_float_types:
for real in all_float_types:
_check_field_match(typename, real, "by_size")
def test_struct_signedness_ignored():
_check_field_match("int", "unsigned int", expect_mismatch=False)
_check_field_match("unsigned short", "signed short", expect_mismatch=False)
def test_struct_float_vs_int():
if sys.platform == 'win32':
py.test.skip("XXX fixme: only gives warnings")
ffi = FFI()
for typename in all_signed_integer_types(ffi):
for real in all_float_types:
_check_field_match(typename, real, expect_mismatch=True)
for typename in all_float_types:
for real in all_signed_integer_types(ffi):
_check_field_match(typename, real, expect_mismatch=True)
def test_struct_array_field():
ffi = FFI()
ffi.cdef("struct foo_s { int a[17]; ...; };")
ffi.verify("struct foo_s { int x; int a[17]; int y; };")
assert ffi.sizeof('struct foo_s') == 19 * ffi.sizeof('int')
s = ffi.new("struct foo_s *")
assert ffi.sizeof(s.a) == 17 * ffi.sizeof('int')
def test_struct_array_no_length():
ffi = FFI()
ffi.cdef("struct foo_s { int a[]; int y; ...; };\n"
"int bar(struct foo_s *);\n")
lib = ffi.verify("struct foo_s { int x; int a[17]; int y; };\n"
"int bar(struct foo_s *f) { return f->a[14]; }\n")
assert ffi.sizeof('struct foo_s') == 19 * ffi.sizeof('int')
s = ffi.new("struct foo_s *")
assert ffi.typeof(s.a) is ffi.typeof('int[]') # implicit max length
assert len(s.a) == 18 # max length, computed from the size and start offset
s.a[14] = 4242
assert lib.bar(s) == 4242
# with no declared length, out-of-bound accesses are not detected
s.a[17] = -521
assert s.y == s.a[17] == -521
#
s = ffi.new("struct foo_s *", {'a': list(range(17))})
assert s.a[16] == 16
# overflows at construction time not detected either
s = ffi.new("struct foo_s *", {'a': list(range(18))})
assert s.y == s.a[17] == 17
def test_struct_array_guess_length():
ffi = FFI()
ffi.cdef("struct foo_s { int a[...]; };")
ffi.verify("struct foo_s { int x; int a[17]; int y; };")
assert ffi.sizeof('struct foo_s') == 19 * ffi.sizeof('int')
s = ffi.new("struct foo_s *")
assert ffi.sizeof(s.a) == 17 * ffi.sizeof('int')
py.test.raises(IndexError, 's.a[17]')
def test_struct_array_c99_1():
if sys.platform == 'win32':
py.test.skip("requires C99")
ffi = FFI()
ffi.cdef("struct foo_s { int x; int a[]; };")
ffi.verify("struct foo_s { int x; int a[]; };")
assert ffi.sizeof('struct foo_s') == 1 * ffi.sizeof('int')
s = ffi.new("struct foo_s *", [424242, 4])
assert ffi.sizeof(ffi.typeof(s[0])) == 1 * ffi.sizeof('int')
assert ffi.sizeof(s[0]) == 5 * ffi.sizeof('int')
# ^^^ explanation: if you write in C: "char x[5];", then
# "sizeof(x)" will evaluate to 5. The behavior above is
# a generalization of that to "struct foo_s[len(a)=5] x;"
# if you could do that in C.
assert s.a[3] == 0
s = ffi.new("struct foo_s *", [424242, [-40, -30, -20, -10]])
assert ffi.sizeof(s[0]) == 5 * ffi.sizeof('int')
assert s.a[3] == -10
s = ffi.new("struct foo_s *")
assert ffi.sizeof(s[0]) == 1 * ffi.sizeof('int')
s = ffi.new("struct foo_s *", [424242])
assert ffi.sizeof(s[0]) == 1 * ffi.sizeof('int')
def test_struct_array_c99_2():
if sys.platform == 'win32':
py.test.skip("requires C99")
ffi = FFI()
ffi.cdef("struct foo_s { int x; int a[]; ...; };")
ffi.verify("struct foo_s { int x, y; int a[]; };")
assert ffi.sizeof('struct foo_s') == 2 * ffi.sizeof('int')
s = ffi.new("struct foo_s *", [424242, 4])
assert ffi.sizeof(s[0]) == 6 * ffi.sizeof('int')
assert s.a[3] == 0
s = ffi.new("struct foo_s *", [424242, [-40, -30, -20, -10]])
assert ffi.sizeof(s[0]) == 6 * ffi.sizeof('int')
assert s.a[3] == -10
s = ffi.new("struct foo_s *")
assert ffi.sizeof(s[0]) == 2 * ffi.sizeof('int')
s = ffi.new("struct foo_s *", [424242])
assert ffi.sizeof(s[0]) == 2 * ffi.sizeof('int')
def test_struct_ptr_to_array_field():
ffi = FFI()
ffi.cdef("struct foo_s { int (*a)[17]; ...; }; struct bar_s { ...; };")
ffi.verify("struct foo_s { int x; int (*a)[17]; int y; };\n"
"struct bar_s { int x; int *a; int y; };")
assert ffi.sizeof('struct foo_s') == ffi.sizeof("struct bar_s")
s = ffi.new("struct foo_s *")
assert ffi.sizeof(s.a) == ffi.sizeof('int(*)[17]') == ffi.sizeof("int *")
def test_struct_with_bitfield_exact():
ffi = FFI()
ffi.cdef("struct foo_s { int a:2, b:3; };")
ffi.verify("struct foo_s { int a:2, b:3; };")
s = ffi.new("struct foo_s *")
s.b = 3
py.test.raises(OverflowError, "s.b = 4")
assert s.b == 3
def test_struct_with_bitfield_enum():
ffi = FFI()
code = """
typedef enum { AA, BB, CC } foo_e;
typedef struct { foo_e f:2; } foo_s;
"""
ffi.cdef(code)
ffi.verify(code)
s = ffi.new("foo_s *")
s.f = 2
assert s.f == 2
def test_unsupported_struct_with_bitfield_ellipsis():
ffi = FFI()
py.test.raises(NotImplementedError, ffi.cdef,
"struct foo_s { int a:2, b:3; ...; };")
def test_global_constants():
ffi = FFI()
# use 'static const int', as generally documented, although in this
# case the 'static' is completely ignored.
ffi.cdef("static const int AA, BB, CC, DD;")
lib = ffi.verify("#define AA 42\n"
"#define BB (-43) // blah\n"
"#define CC (22*2) /* foobar */\n"
"#define DD ((unsigned int)142) /* foo\nbar */\n")
assert lib.AA == 42
assert lib.BB == -43
assert lib.CC == 44
assert lib.DD == 142
def test_global_const_int_size():
# integer constants: ignore the declared type, always just use the value
for value in [-2**63, -2**31, -2**15,
2**15-1, 2**15, 2**31-1, 2**31, 2**32-1, 2**32,
2**63-1, 2**63, 2**64-1]:
ffi = FFI()
if value == int(ffi.cast("long long", value)):
if value < 0:
vstr = '(-%dLL-1)' % (~value,)
else:
vstr = '%dLL' % value
elif value == int(ffi.cast("unsigned long long", value)):
vstr = '%dULL' % value
else:
raise AssertionError(value)
ffi.cdef("static const unsigned short AA;")
lib = ffi.verify("#define AA %s\n" % vstr)
assert lib.AA == value
assert type(lib.AA) is type(int(lib.AA))
def test_global_constants_non_int():
ffi = FFI()
ffi.cdef("static char *const PP;")
lib = ffi.verify('static char *const PP = "testing!";\n')
assert ffi.typeof(lib.PP) == ffi.typeof("char *")
assert ffi.string(lib.PP) == b"testing!"
def test_nonfull_enum():
ffi = FFI()
ffi.cdef("enum ee { EE1, EE2, EE3, ... \n \t };")
py.test.raises(VerificationMissing, ffi.cast, 'enum ee', 'EE2')
ffi.verify("enum ee { EE1=10, EE2, EE3=-10, EE4 };")
assert ffi.string(ffi.cast('enum ee', 11)) == "EE2"
assert ffi.string(ffi.cast('enum ee', -10)) == "EE3"
#
# try again
ffi.verify("enum ee { EE1=10, EE2, EE3=-10, EE4 };")
assert ffi.string(ffi.cast('enum ee', 11)) == "EE2"
#
assert ffi.typeof("enum ee").relements == {'EE1': 10, 'EE2': 11, 'EE3': -10}
assert ffi.typeof("enum ee").elements == {10: 'EE1', 11: 'EE2', -10: 'EE3'}
def test_full_enum():
ffi = FFI()
ffi.cdef("enum ee { EE1, EE2, EE3 };")
ffi.verify("enum ee { EE1, EE2, EE3 };")
py.test.raises(VerificationError, ffi.verify, "enum ee { EE1, EE2 };")
e = py.test.raises(VerificationError, ffi.verify,
"enum ee { EE1, EE3, EE2 };")
assert str(e.value) == 'enum ee: EE2 has the real value 2, not 1'
# extra items cannot be seen and have no bad consequence anyway
lib = ffi.verify("enum ee { EE1, EE2, EE3, EE4 };")
assert lib.EE3 == 2
def test_enum_usage():
ffi = FFI()
ffi.cdef("enum ee { EE1,EE2 }; typedef struct { enum ee x; } *sp;")
lib = ffi.verify("enum ee { EE1,EE2 }; typedef struct { enum ee x; } *sp;")
assert lib.EE2 == 1
s = ffi.new("sp", [lib.EE2])
assert s.x == 1
s.x = 17
assert s.x == 17
def test_anonymous_enum():
ffi = FFI()
ffi.cdef("enum { EE1 }; enum { EE2, EE3 };")
lib = ffi.verify("enum { EE1 }; enum { EE2, EE3 };")
assert lib.EE1 == 0
assert lib.EE2 == 0
assert lib.EE3 == 1
def test_nonfull_anonymous_enum():
ffi = FFI()
ffi.cdef("enum { EE1, ... }; enum { EE3, ... };")
lib = ffi.verify("enum { EE2, EE1 }; enum { EE3 };")
assert lib.EE1 == 1
assert lib.EE3 == 0
def test_nonfull_enum_syntax2():
ffi = FFI()
ffi.cdef("enum ee { EE1, EE2=\t..., EE3 };")
py.test.raises(VerificationMissing, ffi.cast, 'enum ee', 'EE1')
ffi.verify("enum ee { EE1=10, EE2, EE3=-10, EE4 };")
assert ffi.string(ffi.cast('enum ee', 11)) == 'EE2'
assert ffi.string(ffi.cast('enum ee', -10)) == 'EE3'
#
ffi = FFI()
ffi.cdef("enum ee { EE1, EE2=\t... };")
py.test.raises(VerificationMissing, ffi.cast, 'enum ee', 'EE1')
ffi.verify("enum ee { EE1=10, EE2, EE3=-10, EE4 };")
assert ffi.string(ffi.cast('enum ee', 11)) == 'EE2'
#
ffi = FFI()
ffi.cdef("enum ee2 { EE4=..., EE5=..., ... };")
ffi.verify("enum ee2 { EE4=-1234-5, EE5 }; ")
assert ffi.string(ffi.cast('enum ee2', -1239)) == 'EE4'
assert ffi.string(ffi.cast('enum ee2', -1238)) == 'EE5'
def test_nonfull_enum_bug3():
ffi = FFI()
ffi.cdef("enum ee2 { EE4=..., EE5=... };")
ffi.cdef("enum ee6 { EE7=10, EE8=..., EE9=... };")
def test_get_set_errno():
ffi = FFI()
ffi.cdef("int foo(int);")
lib = ffi.verify("""
static int foo(int x)
{
errno += 1;
return x * 7;
}
""")
ffi.errno = 15
assert lib.foo(6) == 42
assert ffi.errno == 16
def test_define_int():
ffi = FFI()
ffi.cdef("#define FOO ...\n"
"\t#\tdefine\tBAR\t...\t\n"
"#define BAZ ...\n")
lib = ffi.verify("#define FOO 42\n"
"#define BAR (-44)\n"
"#define BAZ 0xffffffffffffffffULL\n")
assert lib.FOO == 42
assert lib.BAR == -44
assert lib.BAZ == 0xffffffffffffffff
def test_access_variable():
ffi = FFI()
ffi.cdef("int foo(void);\n"
"int somenumber;")
lib = ffi.verify("""
static int somenumber = 2;
static int foo(void) {
return somenumber * 7;
}
""")
assert lib.somenumber == 2
assert lib.foo() == 14
lib.somenumber = -6
assert lib.foo() == -42
assert lib.somenumber == -6
lib.somenumber = 2 # reset for the next run, if any
def test_access_address_of_variable():
# access the address of 'somenumber': need a trick
ffi = FFI()
ffi.cdef("int somenumber; static int *const somenumberptr;")
lib = ffi.verify("""
static int somenumber = 2;
#define somenumberptr (&somenumber)
""")
assert lib.somenumber == 2
lib.somenumberptr[0] = 42
assert lib.somenumber == 42
lib.somenumber = 2 # reset for the next run, if any
def test_access_array_variable(length=5):
ffi = FFI()
ffi.cdef("int foo(int);\n"
"int somenumber[%s];" % (length,))
lib = ffi.verify("""
static int somenumber[] = {2, 2, 3, 4, 5};
static int foo(int i) {
return somenumber[i] * 7;
}
""")
if length == '':
# a global variable of an unknown array length is implicitly
# transformed into a global pointer variable, because we can only
# work with array instances whose length we know. using a pointer
# instead of an array gives the correct effects.
assert repr(lib.somenumber).startswith("<cdata 'int *' 0x")
py.test.raises(TypeError, len, lib.somenumber)
else:
assert repr(lib.somenumber).startswith("<cdata 'int[%s]' 0x" % length)
assert len(lib.somenumber) == 5
assert lib.somenumber[3] == 4
assert lib.foo(3) == 28
lib.somenumber[3] = -6
assert lib.foo(3) == -42
assert lib.somenumber[3] == -6
assert lib.somenumber[4] == 5
lib.somenumber[3] = 4 # reset for the next run, if any
def test_access_array_variable_length_hidden():
test_access_array_variable(length='')
def test_access_struct_variable():
ffi = FFI()
ffi.cdef("struct foo { int x; ...; };\n"
"int foo(int);\n"
"struct foo stuff;")
lib = ffi.verify("""
struct foo { int x, y, z; };
static struct foo stuff = {2, 5, 8};
static int foo(int i) {
switch (i) {
case 0: return stuff.x * 7;
case 1: return stuff.y * 7;
case 2: return stuff.z * 7;
}
return -1;
}
""")
assert lib.stuff.x == 2
assert lib.foo(0) == 14
assert lib.foo(1) == 35
assert lib.foo(2) == 56
lib.stuff.x = -6
assert lib.foo(0) == -42
assert lib.foo(1) == 35
lib.stuff.x = 2 # reset for the next run, if any
def test_access_callback():
ffi = FFI()
ffi.cdef("int (*cb)(int);\n"
"int foo(int);\n"
"void reset_cb(void);")
lib = ffi.verify("""
static int g(int x) { return x * 7; }
static int (*cb)(int);
static int foo(int i) { return cb(i) - 1; }
static void reset_cb(void) { cb = g; }
""")
lib.reset_cb()
assert lib.foo(6) == 41
my_callback = ffi.callback("int(*)(int)", lambda n: n * 222)
lib.cb = my_callback
assert lib.foo(4) == 887
def test_access_callback_function_typedef():
ffi = FFI()
ffi.cdef("typedef int mycallback_t(int);\n"
"mycallback_t *cb;\n"
"int foo(int);\n"
"void reset_cb(void);")
lib = ffi.verify("""
static int g(int x) { return x * 7; }
static int (*cb)(int);
static int foo(int i) { return cb(i) - 1; }
static void reset_cb(void) { cb = g; }
""")
lib.reset_cb()
assert lib.foo(6) == 41
my_callback = ffi.callback("int(*)(int)", lambda n: n * 222)
lib.cb = my_callback
assert lib.foo(4) == 887
def test_ctypes_backend_forces_generic_engine():
from cffi.backend_ctypes import CTypesBackend
ffi = FFI(backend=CTypesBackend())
ffi.cdef("int func(int a);")
lib = ffi.verify("int func(int a) { return a * 42; }")
assert not hasattr(lib, '_cffi_python_module')
assert hasattr(lib, '_cffi_generic_module')
assert lib.func(100) == 4200
def test_call_with_struct_ptr():
ffi = FFI()
ffi.cdef("typedef struct { int x; ...; } foo_t; int foo(foo_t *);")
lib = ffi.verify("""
typedef struct { int y, x; } foo_t;
static int foo(foo_t *f) { return f->x * 7; }
""")
f = ffi.new("foo_t *")
f.x = 6
assert lib.foo(f) == 42
def test_unknown_type():
ffi = FFI()
ffi.cdef("""
typedef ... token_t;
int foo(token_t *);
#define TOKEN_SIZE ...
""")
lib = ffi.verify("""
typedef float token_t;
static int foo(token_t *tk) {
if (!tk)
return -42;
*tk += 1.601f;
return (int)*tk;
}
#define TOKEN_SIZE sizeof(token_t)
""")
# we cannot let ffi.new("token_t *") work, because we don't know ahead of
# time if it's ok to ask 'sizeof(token_t)' in the C code or not.
# See test_unknown_type_2. Workaround.
tkmem = ffi.new("char[]", lib.TOKEN_SIZE) # zero-initialized
tk = ffi.cast("token_t *", tkmem)
results = [lib.foo(tk) for i in range(6)]
assert results == [1, 3, 4, 6, 8, 9]
assert lib.foo(ffi.NULL) == -42
def test_unknown_type_2():
ffi = FFI()
ffi.cdef("typedef ... token_t;")
lib = ffi.verify("typedef struct token_s token_t;")
# assert did not crash, even though 'sizeof(token_t)' is not valid in C.
def test_unknown_type_3():
ffi = FFI()
ffi.cdef("""
typedef ... *token_p;
token_p foo(token_p);
""")
lib = ffi.verify("""
typedef struct _token_s *token_p;
token_p foo(token_p arg) {
if (arg)
return (token_p)0x12347;
else
return (token_p)0x12345;
}
""")
p = lib.foo(ffi.NULL)
assert int(ffi.cast("intptr_t", p)) == 0x12345
q = lib.foo(p)
assert int(ffi.cast("intptr_t", q)) == 0x12347
def test_varargs():
ffi = FFI()
ffi.cdef("int foo(int x, ...);")
lib = ffi.verify("""
int foo(int x, ...) {
va_list vargs;
va_start(vargs, x);
x -= va_arg(vargs, int);
x -= va_arg(vargs, int);
va_end(vargs);
return x;
}
""")
assert lib.foo(50, ffi.cast("int", 5), ffi.cast("int", 3)) == 42
def test_varargs_exact():
if sys.platform == 'win32':
py.test.skip("XXX fixme: only gives warnings")
ffi = FFI()
ffi.cdef("int foo(int x, ...);")
py.test.raises(VerificationError, ffi.verify, """
int foo(long long x, ...) {
return x;
}
""")
def test_varargs_struct():
ffi = FFI()
ffi.cdef("struct foo_s { char a; int b; }; int foo(int x, ...);")
lib = ffi.verify("""
struct foo_s {
char a; int b;
};
int foo(int x, ...) {
va_list vargs;
struct foo_s s;
va_start(vargs, x);
s = va_arg(vargs, struct foo_s);
va_end(vargs);
return s.a - s.b;
}
""")
s = ffi.new("struct foo_s *", [b'B', 1])
assert lib.foo(50, s[0]) == ord('A')
def test_autofilled_struct_as_argument():
ffi = FFI()
ffi.cdef("struct foo_s { long a; double b; ...; };\n"
"int foo(struct foo_s);")
lib = ffi.verify("""
struct foo_s {
double b;
long a;
};
int foo(struct foo_s s) {
return (int)s.a - (int)s.b;
}
""")
s = ffi.new("struct foo_s *", [100, 1])
assert lib.foo(s[0]) == 99
assert lib.foo([100, 1]) == 99
def test_autofilled_struct_as_argument_dynamic():
ffi = FFI()
ffi.cdef("struct foo_s { long a; ...; };\n"
"int (*foo)(struct foo_s);")
lib = ffi.verify("""
struct foo_s {
double b;
long a;
};
int foo1(struct foo_s s) {
return (int)s.a - (int)s.b;
}
int (*foo)(struct foo_s s) = &foo1;
""")
e = py.test.raises(NotImplementedError, lib.foo, "?")
msg = ("ctype 'struct foo_s' not supported as argument. It is a struct "
'declared with "...;", but the C calling convention may depend on '
"the missing fields; or, it contains anonymous struct/unions. "
"Such structs are only supported as argument "
"if the function is 'API mode' and non-variadic (i.e. declared "
"inside ffibuilder.cdef()+ffibuilder.set_source() and not taking "
"a final '...' argument)")
assert str(e.value) == msg
def test_func_returns_struct():
ffi = FFI()
ffi.cdef("""
struct foo_s { int aa, bb; };
struct foo_s foo(int a, int b);
""")
lib = ffi.verify("""
struct foo_s { int aa, bb; };
struct foo_s foo(int a, int b) {
struct foo_s r;
r.aa = a*a;
r.bb = b*b;
return r;
}
""")
s = lib.foo(6, 7)
assert repr(s) == "<cdata 'struct foo_s' owning 8 bytes>"
assert s.aa == 36
assert s.bb == 49
def test_func_as_funcptr():
ffi = FFI()
ffi.cdef("int *(*const fooptr)(void);")
lib = ffi.verify("""
int *foo(void) {
return (int*)"foobar";
}
int *(*fooptr)(void) = foo;
""")
foochar = ffi.cast("char *(*)(void)", lib.fooptr)
s = foochar()
assert ffi.string(s) == b"foobar"
def test_funcptr_as_argument():
ffi = FFI()
ffi.cdef("""
void qsort(void *base, size_t nel, size_t width,
int (*compar)(const void *, const void *));
""")
ffi.verify("#include <stdlib.h>")
def test_func_as_argument():
ffi = FFI()
ffi.cdef("""
void qsort(void *base, size_t nel, size_t width,
int compar(const void *, const void *));
""")
ffi.verify("#include <stdlib.h>")
def test_array_as_argument():
ffi = FFI()
ffi.cdef("""
size_t strlen(char string[]);
""")
ffi.verify("#include <string.h>")
def test_enum_as_argument():
ffi = FFI()
ffi.cdef("""
enum foo_e { AA, BB, ... };
int foo_func(enum foo_e);
""")
lib = ffi.verify("""
enum foo_e { AA, CC, BB };
int foo_func(enum foo_e e) { return (int)e; }
""")
assert lib.foo_func(lib.BB) == 2
py.test.raises(TypeError, lib.foo_func, "BB")
def test_enum_as_function_result():
ffi = FFI()
ffi.cdef("""
enum foo_e { AA, BB, ... };
enum foo_e foo_func(int x);
""")
lib = ffi.verify("""
enum foo_e { AA, CC, BB };
enum foo_e foo_func(int x) { return (enum foo_e)x; }
""")
assert lib.foo_func(lib.BB) == lib.BB == 2
def test_enum_values():
ffi = FFI()
ffi.cdef("enum enum1_e { AA, BB };")
lib = ffi.verify("enum enum1_e { AA, BB };")
assert lib.AA == 0
assert lib.BB == 1
assert ffi.string(ffi.cast("enum enum1_e", 1)) == 'BB'
def test_typedef_complete_enum():
ffi = FFI()
ffi.cdef("typedef enum { AA, BB } enum1_t;")
lib = ffi.verify("typedef enum { AA, BB } enum1_t;")
assert ffi.string(ffi.cast("enum1_t", 1)) == 'BB'
assert lib.AA == 0
assert lib.BB == 1
def test_typedef_broken_complete_enum():
ffi = FFI()
ffi.cdef("typedef enum { AA, BB } enum1_t;")
py.test.raises(VerificationError, ffi.verify,
"typedef enum { AA, CC, BB } enum1_t;")
def test_typedef_incomplete_enum():
ffi = FFI()
ffi.cdef("typedef enum { AA, BB, ... } enum1_t;")
lib = ffi.verify("typedef enum { AA, CC, BB } enum1_t;")
assert ffi.string(ffi.cast("enum1_t", 1)) == '1'
assert ffi.string(ffi.cast("enum1_t", 2)) == 'BB'
assert lib.AA == 0
assert lib.BB == 2
def test_typedef_enum_as_argument():
ffi = FFI()
ffi.cdef("""
typedef enum { AA, BB, ... } foo_t;
int foo_func(foo_t);
""")
lib = ffi.verify("""
typedef enum { AA, CC, BB } foo_t;
int foo_func(foo_t e) { return (int)e; }
""")
assert lib.foo_func(lib.BB) == lib.BB == 2
py.test.raises(TypeError, lib.foo_func, "BB")
def test_typedef_enum_as_function_result():
ffi = FFI()
ffi.cdef("""
typedef enum { AA, BB, ... } foo_t;
foo_t foo_func(int x);
""")
lib = ffi.verify("""
typedef enum { AA, CC, BB } foo_t;
foo_t foo_func(int x) { return (foo_t)x; }
""")
assert lib.foo_func(lib.BB) == lib.BB == 2
def test_function_typedef():
ffi = FFI()
ffi.cdef("""
typedef double func_t(double);
func_t sin;
""")
lib = ffi.verify('#include <math.h>', libraries=lib_m)
assert lib.sin(1.23) == math.sin(1.23)
def test_opaque_integer_as_function_result():
#import platform
#if platform.machine().startswith('sparc'):
# py.test.skip('Breaks horribly on sparc (SIGILL + corrupted stack)')
#elif platform.machine() == 'mips64' and sys.maxsize > 2**32:
# py.test.skip('Segfaults on mips64el')
# XXX bad abuse of "struct { ...; }". It only works a bit by chance
# anyway. XXX think about something better :-(
ffi = FFI()
ffi.cdef("""
typedef struct { ...; } myhandle_t;
myhandle_t foo(void);
""")
lib = ffi.verify("""
typedef short myhandle_t;
myhandle_t foo(void) { return 42; }
""")
h = lib.foo()
assert ffi.sizeof(h) == ffi.sizeof("short")
def test_return_partial_struct():
ffi = FFI()
ffi.cdef("""
typedef struct { int x; ...; } foo_t;
foo_t foo(void);
""")
lib = ffi.verify("""
typedef struct { int y, x; } foo_t;
foo_t foo(void) { foo_t r = { 45, 81 }; return r; }
""")
h = lib.foo()
assert ffi.sizeof(h) == 2 * ffi.sizeof("int")
assert h.x == 81
def test_take_and_return_partial_structs():
ffi = FFI()
ffi.cdef("""
typedef struct { int x; ...; } foo_t;
foo_t foo(foo_t, foo_t);
""")
lib = ffi.verify("""
typedef struct { int y, x; } foo_t;
foo_t foo(foo_t a, foo_t b) {
foo_t r = { 100, a.x * 5 + b.x * 7 };
return r;
}
""")
args = ffi.new("foo_t[3]")
args[0].x = 1000
args[2].x = -498
h = lib.foo(args[0], args[2])
assert ffi.sizeof(h) == 2 * ffi.sizeof("int")
assert h.x == 1000 * 5 - 498 * 7
def test_cannot_name_struct_type():
ffi = FFI()
ffi.cdef("typedef struct { int x; } **sp; void foo(sp);")
e = py.test.raises(VerificationError, ffi.verify,
"typedef struct { int x; } **sp; void foo(sp x) { }")
assert 'in argument of foo: unknown type name' in str(e.value)
def test_dont_check_unnamable_fields():
ffi = FFI()
ffi.cdef("struct foo_s { struct { int x; } someone; };")
ffi.verify("struct foo_s { struct { int x; } someone; };")
# assert did not crash
def test_nested_anonymous_struct_exact():
if sys.platform == 'win32':
py.test.skip("nested anonymous struct/union")
ffi = FFI()
ffi.cdef("""
struct foo_s { struct { int a; char b; }; union { char c, d; }; };
""")
ffi.verify("""
struct foo_s { struct { int a; char b; }; union { char c, d; }; };
""")
p = ffi.new("struct foo_s *")
assert ffi.sizeof(p[0]) == 3 * ffi.sizeof("int") # with alignment
p.a = 1234567
p.b = b'X'
p.c = b'Y'
assert p.a == 1234567
assert p.b == b'X'
assert p.c == b'Y'
assert p.d == b'Y'
def test_nested_anonymous_struct_exact_error():
if sys.platform == 'win32':
py.test.skip("nested anonymous struct/union")
ffi = FFI()
ffi.cdef("""
struct foo_s { struct { int a; char b; }; union { char c, d; }; };
""")
py.test.raises(VerificationError, ffi.verify, """
struct foo_s { struct { int a; short b; }; union { char c, d; }; };
""")
py.test.raises(VerificationError, ffi.verify, """
struct foo_s { struct { int a; char e, b; }; union { char c, d; }; };
""")
def test_nested_anonymous_struct_inexact_1():
ffi = FFI()
ffi.cdef("""
struct foo_s { struct { char b; ...; }; union { char c, d; }; };
""")
ffi.verify("""
struct foo_s { int a, padding; char c, d, b; };
""")
assert ffi.sizeof("struct foo_s") == 3 * ffi.sizeof("int")
def test_nested_anonymous_struct_inexact_2():
ffi = FFI()
ffi.cdef("""
struct foo_s { union { char c, d; }; struct { int a; char b; }; ...; };
""")
ffi.verify("""
struct foo_s { int a, padding; char c, d, b; };
""")
assert ffi.sizeof("struct foo_s") == 3 * ffi.sizeof("int")
def test_ffi_union():
ffi = FFI()
ffi.cdef("union foo_u { char x; long *z; };")
ffi.verify("union foo_u { char x; int y; long *z; };")
def test_ffi_union_partial():
ffi = FFI()
ffi.cdef("union foo_u { char x; ...; };")
ffi.verify("union foo_u { char x; int y; };")
assert ffi.sizeof("union foo_u") == 4
def test_ffi_union_with_partial_struct():
ffi = FFI()
ffi.cdef("struct foo_s { int x; ...; }; union foo_u { struct foo_s s; };")
ffi.verify("struct foo_s { int a; int x; }; "
"union foo_u { char b[32]; struct foo_s s; };")
assert ffi.sizeof("struct foo_s") == 8
assert ffi.sizeof("union foo_u") == 32
def test_ffi_union_partial_2():
ffi = FFI()
ffi.cdef("typedef union { char x; ...; } u1;")
ffi.verify("typedef union { char x; int y; } u1;")
assert ffi.sizeof("u1") == 4
def test_ffi_union_with_partial_struct_2():
ffi = FFI()
ffi.cdef("typedef struct { int x; ...; } s1;"
"typedef union { s1 s; } u1;")
ffi.verify("typedef struct { int a; int x; } s1; "
"typedef union { char b[32]; s1 s; } u1;")
assert ffi.sizeof("s1") == 8
assert ffi.sizeof("u1") == 32
assert ffi.offsetof("u1", "s") == 0
def test_ffi_struct_packed():
if sys.platform == 'win32':
py.test.skip("needs a GCC extension")
ffi = FFI()
ffi.cdef("struct foo_s { int b; ...; };")
ffi.verify("""
struct foo_s {
char a;
int b;
} __attribute__((packed));
""")
def test_tmpdir():
import tempfile, os
from pypy.module.test_lib_pypy.cffi_tests.udir import udir
tmpdir = tempfile.mkdtemp(dir=str(udir))
ffi = FFI()
ffi.cdef("int foo(int);")
lib = ffi.verify("int foo(int a) { return a + 42; }", tmpdir=tmpdir)
assert os.listdir(tmpdir)
assert lib.foo(100) == 142
def test_relative_to():
import tempfile, os
from pypy.module.test_lib_pypy.cffi_tests.udir import udir
tmpdir = tempfile.mkdtemp(dir=str(udir))
ffi = FFI()
ffi.cdef("int foo(int);")
f = open(os.path.join(tmpdir, 'foo.h'), 'w')
f.write("int foo(int a) { return a + 42; }\n")
f.close()
lib = ffi.verify('#include "foo.h"',
include_dirs=['.'],
relative_to=os.path.join(tmpdir, 'x'))
assert lib.foo(100) == 142
def test_bug1():
ffi = FFI()
ffi.cdef("""
typedef struct tdlhandle_s { ...; } *tdl_handle_t;
typedef struct my_error_code_ {
tdl_handle_t *rh;
} my_error_code_t;
""")
ffi.verify("""
typedef struct tdlhandle_s { int foo; } *tdl_handle_t;
typedef struct my_error_code_ {
tdl_handle_t *rh;
} my_error_code_t;
""")
def test_bool():
if sys.platform == 'win32':
py.test.skip("_Bool not in MSVC")
ffi = FFI()
ffi.cdef("struct foo_s { _Bool x; };"
"_Bool foo(_Bool); _Bool (*foop)(_Bool);")
lib = ffi.verify("""
struct foo_s { _Bool x; };
int foo(int arg) {
return !arg;
}
_Bool _foofunc(_Bool x) {
return !x;
}
_Bool (*foop)(_Bool) = _foofunc;
""")
p = ffi.new("struct foo_s *")
p.x = 1
assert p.x is True
py.test.raises(OverflowError, "p.x = -1")
py.test.raises(TypeError, "p.x = 0.0")
assert lib.foop(1) is False
assert lib.foop(True) is False
assert lib.foop(0) is True
py.test.raises(OverflowError, lib.foop, 42)
py.test.raises(TypeError, lib.foop, 0.0)
assert lib.foo(1) is False
assert lib.foo(True) is False
assert lib.foo(0) is True
py.test.raises(OverflowError, lib.foo, 42)
py.test.raises(TypeError, lib.foo, 0.0)
assert int(ffi.cast("_Bool", long(1))) == 1
assert int(ffi.cast("_Bool", long(0))) == 0
assert int(ffi.cast("_Bool", long(-1))) == 1
assert int(ffi.cast("_Bool", 10**200)) == 1
assert int(ffi.cast("_Bool", 10**40000)) == 1
#
class Foo(object):
def __int__(self):
self.seen = 1
return result
f = Foo()
f.seen = 0
result = 42
assert int(ffi.cast("_Bool", f)) == 1
assert f.seen
f.seen = 0
result = 0
assert int(ffi.cast("_Bool", f)) == 0
assert f.seen
#
py.test.raises(TypeError, ffi.cast, "_Bool", [])
def test_bool_on_long_double():
if sys.platform == 'win32':
py.test.skip("_Bool not in MSVC")
f = 1E-250
if f == 0.0 or f*f != 0.0:
py.test.skip("unexpected precision")
ffi = FFI()
ffi.cdef("long double square(long double f); _Bool opposite(_Bool);")
lib = ffi.verify("long double square(long double f) { return f*f; }\n"
"_Bool opposite(_Bool x) { return !x; }")
f0 = lib.square(0.0)
f2 = lib.square(f)
f3 = lib.square(f * 2.0)
if repr(f2) == repr(f3):
py.test.skip("long double doesn't have enough precision")
assert float(f0) == float(f2) == float(f3) == 0.0 # too tiny for 'double'
assert int(ffi.cast("_Bool", f2)) == 1
assert int(ffi.cast("_Bool", f3)) == 1
assert int(ffi.cast("_Bool", f0)) == 0
py.test.raises(TypeError, lib.opposite, f2)
def test_cannot_pass_float():
for basetype in ['char', 'short', 'int', 'long', 'long long']:
for sign in ['signed', 'unsigned']:
type = '%s %s' % (sign, basetype)
ffi = FFI()
ffi.cdef("struct foo_s { %s x; };\n"
"int foo(%s);" % (type, type))
lib = ffi.verify("""
struct foo_s { %s x; };
int foo(%s arg) {
return !arg;
}
""" % (type, type))
p = ffi.new("struct foo_s *")
py.test.raises(TypeError, "p.x = 0.0")
assert lib.foo(42) == 0
assert lib.foo(0) == 1
py.test.raises(TypeError, lib.foo, 0.0)
def test_cast_from_int_type_to_bool():
ffi = FFI()
for basetype in ['char', 'short', 'int', 'long', 'long long']:
for sign in ['signed', 'unsigned']:
type = '%s %s' % (sign, basetype)
assert int(ffi.cast("_Bool", ffi.cast(type, 42))) == 1
assert int(ffi.cast("bool", ffi.cast(type, 42))) == 1
assert int(ffi.cast("_Bool", ffi.cast(type, 0))) == 0
def test_addressof():
ffi = FFI()
ffi.cdef("""
struct point_s { int x, y; };
struct foo_s { int z; struct point_s point; };
struct point_s sum_coord(struct point_s *);
""")
lib = ffi.verify("""
struct point_s { int x, y; };
struct foo_s { int z; struct point_s point; };
struct point_s sum_coord(struct point_s *point) {
struct point_s r;
r.x = point->x + point->y;
r.y = point->x - point->y;
return r;
}
""")
p = ffi.new("struct foo_s *")
p.point.x = 16
p.point.y = 9
py.test.raises(TypeError, lib.sum_coord, p.point)
res = lib.sum_coord(ffi.addressof(p.point))
assert res.x == 25
assert res.y == 7
res2 = lib.sum_coord(ffi.addressof(res))
assert res2.x == 32
assert res2.y == 18
py.test.raises(TypeError, lib.sum_coord, res2)
def test_callback_in_thread():
if sys.platform == 'win32':
py.test.skip("pthread only")
import os, subprocess, imp
arg = os.path.join(os.path.dirname(__file__), 'callback_in_thread.py')
g = subprocess.Popen([sys.executable, arg,
os.path.dirname(imp.find_module('cffi')[1])])
result = g.wait()
assert result == 0
def test_keepalive_lib():
ffi = FFI()
ffi.cdef("int foobar(void);")
lib = ffi.verify("int foobar(void) { return 42; }")
func = lib.foobar
ffi_r = weakref.ref(ffi)
lib_r = weakref.ref(lib)
del ffi
import gc; gc.collect() # lib stays alive
assert lib_r() is not None
assert ffi_r() is not None
assert func() == 42
def test_keepalive_ffi():
ffi = FFI()
ffi.cdef("int foobar(void);")
lib = ffi.verify("int foobar(void) { return 42; }")
func = lib.foobar
ffi_r = weakref.ref(ffi)
lib_r = weakref.ref(lib)
del lib
import gc; gc.collect() # ffi stays alive
assert ffi_r() is not None
assert lib_r() is not None
assert func() == 42
def test_FILE_stored_in_stdout():
if not sys.platform.startswith('linux'):
py.test.skip("likely, we cannot assign to stdout")
ffi = FFI()
ffi.cdef("int printf(const char *, ...); FILE *setstdout(FILE *);")
lib = ffi.verify("""
#include <stdio.h>
FILE *setstdout(FILE *f) {
FILE *result = stdout;
stdout = f;
return result;
}
""")
import os
fdr, fdw = os.pipe()
fw1 = os.fdopen(fdw, 'wb', 256)
old_stdout = lib.setstdout(fw1)
try:
#
fw1.write(b"X")
r = lib.printf(b"hello, %d!\n", ffi.cast("int", 42))
fw1.close()
assert r == len("hello, 42!\n")
#
finally:
lib.setstdout(old_stdout)
#
result = os.read(fdr, 256)
os.close(fdr)
# the 'X' might remain in the user-level buffer of 'fw1' and
# end up showing up after the 'hello, 42!\n'
assert result == b"Xhello, 42!\n" or result == b"hello, 42!\nX"
def test_FILE_stored_explicitly():
ffi = FFI()
ffi.cdef("int myprintf11(const char *, int); FILE *myfile;")
lib = ffi.verify("""
#include <stdio.h>
FILE *myfile;
int myprintf11(const char *out, int value) {
return fprintf(myfile, out, value);
}
""")
import os
fdr, fdw = os.pipe()
fw1 = os.fdopen(fdw, 'wb', 256)
lib.myfile = ffi.cast("FILE *", fw1)
#
fw1.write(b"X")
r = lib.myprintf11(b"hello, %d!\n", ffi.cast("int", 42))
fw1.close()
assert r == len("hello, 42!\n")
#
result = os.read(fdr, 256)
os.close(fdr)
# the 'X' might remain in the user-level buffer of 'fw1' and
# end up showing up after the 'hello, 42!\n'
assert result == b"Xhello, 42!\n" or result == b"hello, 42!\nX"
def test_global_array_with_missing_length():
ffi = FFI()
ffi.cdef("int fooarray[];")
lib = ffi.verify("int fooarray[50];")
assert repr(lib.fooarray).startswith("<cdata 'int *'")
def test_global_array_with_dotdotdot_length():
ffi = FFI()
ffi.cdef("int fooarray[...];")
lib = ffi.verify("int fooarray[50];")
assert repr(lib.fooarray).startswith("<cdata 'int[50]'")
def test_bad_global_array_with_dotdotdot_length():
ffi = FFI()
ffi.cdef("int fooarray[...];")
py.test.raises(VerificationError, ffi.verify, "char fooarray[23];")
def test_struct_containing_struct():
ffi = FFI()
ffi.cdef("struct foo_s { ...; }; struct bar_s { struct foo_s f; ...; };")
ffi.verify("struct foo_s { int x; }; struct bar_s { struct foo_s f; };")
#
ffi = FFI()
ffi.cdef("struct foo_s { struct bar_s f; ...; }; struct bar_s { ...; };")
ffi.verify("struct bar_s { int x; }; struct foo_s { struct bar_s f; };")
def test_struct_returned_by_func():
ffi = FFI()
ffi.cdef("typedef ... foo_t; foo_t myfunc(void);")
e = py.test.raises(TypeError, ffi.verify,
"typedef struct { int x; } foo_t; "
"foo_t myfunc(void) { foo_t x = { 42 }; return x; }")
assert str(e.value) == (
"function myfunc: 'foo_t' is used as result type, but is opaque")
def test_include():
ffi1 = FFI()
ffi1.cdef("typedef struct { int x; ...; } foo_t;")
ffi1.verify("typedef struct { int y, x; } foo_t;")
ffi2 = FFI()
ffi2.include(ffi1)
ffi2.cdef("int myfunc(foo_t *);")
lib = ffi2.verify("typedef struct { int y, x; } foo_t;"
"int myfunc(foo_t *p) { return 42 * p->x; }")
res = lib.myfunc(ffi2.new("foo_t *", {'x': 10}))
assert res == 420
res = lib.myfunc(ffi1.new("foo_t *", {'x': -10}))
assert res == -420
def test_include_enum():
ffi1 = FFI()
ffi1.cdef("enum foo_e { AA, ... };")
lib1 = ffi1.verify("enum foo_e { CC, BB, AA };")
ffi2 = FFI()
ffi2.include(ffi1)
ffi2.cdef("int myfunc(enum foo_e);")
lib2 = ffi2.verify("enum foo_e { CC, BB, AA };"
"int myfunc(enum foo_e x) { return (int)x; }")
res = lib2.myfunc(lib2.AA)
assert res == 2
def test_named_pointer_as_argument():
ffi = FFI()
ffi.cdef("typedef struct { int x; } *mystruct_p;\n"
"mystruct_p ff5a(mystruct_p);")
lib = ffi.verify("typedef struct { int x; } *mystruct_p;\n"
"mystruct_p ff5a(mystruct_p p) { p->x += 40; return p; }")
p = ffi.new("mystruct_p", [-2])
q = lib.ff5a(p)
assert q == p
assert p.x == 38
def test_enum_size():
cases = [('123', 4, 4294967295),
('4294967295U', 4, 4294967295),
('-123', 4, -1),
('-2147483647-1', 4, -1),
]
if FFI().sizeof("long") == 8:
cases += [('4294967296L', 8, 2**64-1),
('%dUL' % (2**64-1), 8, 2**64-1),
('-2147483649L', 8, -1),
('%dL-1L' % (1-2**63), 8, -1)]
for hidden_value, expected_size, expected_minus1 in cases:
if sys.platform == 'win32' and 'U' in hidden_value:
continue # skipped on Windows
ffi = FFI()
ffi.cdef("enum foo_e { AA, BB, ... };")
lib = ffi.verify("enum foo_e { AA, BB=%s };" % hidden_value)
assert lib.AA == 0
assert lib.BB == eval(hidden_value.replace('U', '').replace('L', ''))
assert ffi.sizeof("enum foo_e") == expected_size
assert int(ffi.cast("enum foo_e", -1)) == expected_minus1
# test with the large value hidden:
# disabled so far, doesn't work
## for hidden_value, expected_size, expected_minus1 in cases:
## ffi = FFI()
## ffi.cdef("enum foo_e { AA, BB, ... };")
## lib = ffi.verify("enum foo_e { AA, BB=%s };" % hidden_value)
## assert lib.AA == 0
## assert ffi.sizeof("enum foo_e") == expected_size
## assert int(ffi.cast("enum foo_e", -1)) == expected_minus1
def test_enum_bug118():
maxulong = 256 ** FFI().sizeof("unsigned long") - 1
for c1, c2, c2c in [(0xffffffff, -1, ''),
(maxulong, -1, ''),
(-1, 0xffffffff, 'U'),
(-1, maxulong, 'UL')]:
if c2c and sys.platform == 'win32':
continue # enums may always be signed with MSVC
ffi = FFI()
ffi.cdef("enum foo_e { AA=%s };" % c1)
e = py.test.raises(VerificationError, ffi.verify,
"enum foo_e { AA=%s%s };" % (c2, c2c))
assert str(e.value) == ('enum foo_e: AA has the real value %d, not %d'
% (c2, c1))
def test_string_to_voidp_arg():
ffi = FFI()
ffi.cdef("int myfunc(void *);")
lib = ffi.verify("int myfunc(void *p) { return ((signed char *)p)[0]; }")
res = lib.myfunc(b"hi!")
assert res == ord(b"h")
p = ffi.new("char[]", b"gah")
res = lib.myfunc(p)
assert res == ord(b"g")
res = lib.myfunc(ffi.cast("void *", p))
assert res == ord(b"g")
res = lib.myfunc(ffi.cast("int *", p))
assert res == ord(b"g")
def test_callback_indirection():
ffi = FFI()
ffi.cdef("""
int (*python_callback)(int how_many, int *values);
int (*const c_callback)(int,...); /* pass this ptr to C routines */
int some_c_function(int(*cb)(int,...));
""")
lib = ffi.verify("""
#include <stdarg.h>
#ifdef _WIN32
#include <malloc.h>
#define alloca _alloca
#else
# ifdef __FreeBSD__
# include <stdlib.h>
# else
# include <alloca.h>
# endif
#endif
static int (*python_callback)(int how_many, int *values);
static int c_callback(int how_many, ...) {
va_list ap;
/* collect the "..." arguments into the values[] array */
int i, *values = alloca((size_t)how_many * sizeof(int));
va_start(ap, how_many);
for (i=0; i<how_many; i++)
values[i] = va_arg(ap, int);
va_end(ap);
return python_callback(how_many, values);
}
int some_c_function(int(*cb)(int,...)) {
int result = cb(2, 10, 20);
result += cb(3, 30, 40, 50);
return result;
}
""")
seen = []
@ffi.callback("int(int, int*)")
def python_callback(how_many, values):
seen.append([values[i] for i in range(how_many)])
return 42
lib.python_callback = python_callback
res = lib.some_c_function(lib.c_callback)
assert res == 84
assert seen == [[10, 20], [30, 40, 50]]
def test_floatstar_argument():
ffi = FFI()
ffi.cdef("float sum3floats(float *);")
lib = ffi.verify("""
float sum3floats(float *f) {
return f[0] + f[1] + f[2];
}
""")
assert lib.sum3floats((1.5, 2.5, 3.5)) == 7.5
p = ffi.new("float[]", (1.5, 2.5, 3.5))
assert lib.sum3floats(p) == 7.5
def test_charstar_argument():
ffi = FFI()
ffi.cdef("char sum3chars(char *);")
lib = ffi.verify("""
char sum3chars(char *f) {
return (char)(f[0] + f[1] + f[2]);
}
""")
assert lib.sum3chars((b'\x10', b'\x20', b'\x30')) == b'\x60'
p = ffi.new("char[]", b'\x10\x20\x30')
assert lib.sum3chars(p) == b'\x60'
def test_passing_string_or_NULL():
ffi = FFI()
ffi.cdef("int seeme1(char *); int seeme2(int *);")
lib = ffi.verify("""
int seeme1(char *x) {
return (x == NULL);
}
int seeme2(int *x) {
return (x == NULL);
}
""")
assert lib.seeme1(b"foo") == 0
assert lib.seeme1(ffi.NULL) == 1
assert lib.seeme2([42, 43]) == 0
assert lib.seeme2(ffi.NULL) == 1
py.test.raises(TypeError, lib.seeme1, None)
py.test.raises(TypeError, lib.seeme2, None)
py.test.raises(TypeError, lib.seeme1, 0.0)
py.test.raises(TypeError, lib.seeme2, 0.0)
py.test.raises(TypeError, lib.seeme1, 0)
py.test.raises(TypeError, lib.seeme2, 0)
zeroL = 99999999999999999999
zeroL -= 99999999999999999999
py.test.raises(TypeError, lib.seeme2, zeroL)
def test_typeof_function():
ffi = FFI()
ffi.cdef("int foo(int, char);")
lib = ffi.verify("int foo(int x, char y) { (void)x; (void)y; return 42; }")
ctype = ffi.typeof(lib.foo)
assert len(ctype.args) == 2
assert ctype.result == ffi.typeof("int")
def test_call_with_voidstar_arg():
ffi = FFI()
ffi.cdef("int f(void *);")
lib = ffi.verify("int f(void *x) { return ((char*)x)[0]; }")
assert lib.f(b"foobar") == ord(b"f")
def test_dir():
ffi = FFI()
ffi.cdef("""void somefunc(void);
extern int somevar, somearray[2];
static char *const sv2;
enum my_e { AA, BB, ... };
#define FOO ...""")
lib = ffi.verify("""void somefunc(void) { }
int somevar, somearray[2];
#define sv2 "text"
enum my_e { AA, BB };
#define FOO 42""")
assert dir(lib) == ['AA', 'BB', 'FOO', 'somearray',
'somefunc', 'somevar', 'sv2']
def test_typeof_func_with_struct_argument():
ffi = FFI()
ffi.cdef("""struct s { int a; }; int foo(struct s);""")
lib = ffi.verify("""struct s { int a; };
int foo(struct s x) { return x.a; }""")
s = ffi.new("struct s *", [-1234])
m = lib.foo(s[0])
assert m == -1234
assert repr(ffi.typeof(lib.foo)) == "<ctype 'int(*)(struct s)'>"
def test_bug_const_char_ptr_array_1():
ffi = FFI()
ffi.cdef("""const char *a[...];""")
lib = ffi.verify("""const char *a[5];""")
assert repr(ffi.typeof(lib.a)) == "<ctype 'char *[5]'>"
def test_bug_const_char_ptr_array_2():
from cffi import FFI # ignore warnings
ffi = FFI()
ffi.cdef("""const int a[];""")
lib = ffi.verify("""const int a[5];""")
assert repr(ffi.typeof(lib.a)) == "<ctype 'int *'>"
def _test_various_calls(force_libffi):
cdef_source = """
int xvalue;
long long ivalue, rvalue;
float fvalue;
double dvalue;
long double Dvalue;
signed char tf_bb(signed char x, signed char c);
unsigned char tf_bB(signed char x, unsigned char c);
short tf_bh(signed char x, short c);
unsigned short tf_bH(signed char x, unsigned short c);
int tf_bi(signed char x, int c);
unsigned int tf_bI(signed char x, unsigned int c);
long tf_bl(signed char x, long c);
unsigned long tf_bL(signed char x, unsigned long c);
long long tf_bq(signed char x, long long c);
unsigned long long tf_bQ(signed char x, unsigned long long c);
float tf_bf(signed char x, float c);
double tf_bd(signed char x, double c);
long double tf_bD(signed char x, long double c);
"""
if force_libffi:
cdef_source = (cdef_source
.replace('tf_', '(*const tf_')
.replace('(signed char x', ')(signed char x'))
ffi = FFI()
ffi.cdef(cdef_source)
lib = ffi.verify("""
int xvalue;
long long ivalue, rvalue;
float fvalue;
double dvalue;
long double Dvalue;
typedef signed char b_t;
typedef unsigned char B_t;
typedef short h_t;
typedef unsigned short H_t;
typedef int i_t;
typedef unsigned int I_t;
typedef long l_t;
typedef unsigned long L_t;
typedef long long q_t;
typedef unsigned long long Q_t;
typedef float f_t;
typedef double d_t;
typedef long double D_t;
#define S(letter) xvalue = (int)x; letter##value = (letter##_t)c;
#define R(letter) return (letter##_t)rvalue;
signed char tf_bb(signed char x, signed char c) { S(i) R(b) }
unsigned char tf_bB(signed char x, unsigned char c) { S(i) R(B) }
short tf_bh(signed char x, short c) { S(i) R(h) }
unsigned short tf_bH(signed char x, unsigned short c) { S(i) R(H) }
int tf_bi(signed char x, int c) { S(i) R(i) }
unsigned int tf_bI(signed char x, unsigned int c) { S(i) R(I) }
long tf_bl(signed char x, long c) { S(i) R(l) }
unsigned long tf_bL(signed char x, unsigned long c) { S(i) R(L) }
long long tf_bq(signed char x, long long c) { S(i) R(q) }
unsigned long long tf_bQ(signed char x, unsigned long long c) { S(i) R(Q) }
float tf_bf(signed char x, float c) { S(f) R(f) }
double tf_bd(signed char x, double c) { S(d) R(d) }
long double tf_bD(signed char x, long double c) { S(D) R(D) }
""")
lib.rvalue = 0x7182838485868788
for kind, cname in [('b', 'signed char'),
('B', 'unsigned char'),
('h', 'short'),
('H', 'unsigned short'),
('i', 'int'),
('I', 'unsigned int'),
('l', 'long'),
('L', 'unsigned long'),
('q', 'long long'),
('Q', 'unsigned long long'),
('f', 'float'),
('d', 'double'),
('D', 'long double')]:
sign = +1 if 'unsigned' in cname else -1
lib.xvalue = 0
lib.ivalue = 0
lib.fvalue = 0
lib.dvalue = 0
lib.Dvalue = 0
fun = getattr(lib, 'tf_b' + kind)
res = fun(-42, sign * 99)
if kind == 'D':
res = float(res)
assert res == int(ffi.cast(cname, 0x7182838485868788))
assert lib.xvalue == -42
if kind in 'fdD':
assert float(getattr(lib, kind + 'value')) == -99.0
else:
assert lib.ivalue == sign * 99
def test_various_calls_direct():
_test_various_calls(force_libffi=False)
def test_various_calls_libffi():
_test_various_calls(force_libffi=True)
def test_ptr_to_opaque():
ffi = FFI()
ffi.cdef("typedef ... foo_t; int f1(foo_t*); foo_t *f2(int);")
lib = ffi.verify("""
#include <stdlib.h>
typedef struct { int x; } foo_t;
int f1(foo_t* p) {
int x = p->x;
free(p);
return x;
}
foo_t *f2(int x) {
foo_t *p = malloc(sizeof(foo_t));
p->x = x;
return p;
}
""")
p = lib.f2(42)
x = lib.f1(p)
assert x == 42
def _run_in_multiple_threads(test1):
test1()
import sys
try:
import thread
except ImportError:
import _thread as thread
errors = []
def wrapper(lock):
try:
test1()
except:
errors.append(sys.exc_info())
lock.release()
locks = []
for i in range(10):
_lock = thread.allocate_lock()
_lock.acquire()
thread.start_new_thread(wrapper, (_lock,))
locks.append(_lock)
for _lock in locks:
_lock.acquire()
if errors:
raise errors[0][1]
def test_errno_working_even_with_pypys_jit():
ffi = FFI()
ffi.cdef("int f(int);")
lib = ffi.verify("""
#include <errno.h>
int f(int x) { return (errno = errno + x); }
""")
@_run_in_multiple_threads
def test1():
ffi.errno = 0
for i in range(10000):
e = lib.f(1)
assert e == i + 1
assert ffi.errno == e
for i in range(10000):
ffi.errno = i
e = lib.f(42)
assert e == i + 42
def test_getlasterror_working_even_with_pypys_jit():
if sys.platform != 'win32':
py.test.skip("win32-only test")
ffi = FFI()
ffi.cdef("void SetLastError(DWORD);")
lib = ffi.dlopen("Kernel32.dll")
@_run_in_multiple_threads
def test1():
for i in range(10000):
n = (1 << 29) + i
lib.SetLastError(n)
assert ffi.getwinerror()[0] == n
def test_verify_dlopen_flags():
# Careful with RTLD_GLOBAL. If by chance the FFI is not deleted
# promptly, like on PyPy, then other tests may see the same
# exported symbols as well. So we must not export a simple name
# like 'foo'!
ffi1 = FFI()
ffi1.cdef("int foo_verify_dlopen_flags;")
lib1 = ffi1.verify("int foo_verify_dlopen_flags;",
flags=ffi1.RTLD_GLOBAL | ffi1.RTLD_LAZY)
lib2 = get_second_lib()
lib1.foo_verify_dlopen_flags = 42
assert lib2.foo_verify_dlopen_flags == 42
lib2.foo_verify_dlopen_flags += 1
assert lib1.foo_verify_dlopen_flags == 43
def get_second_lib():
# Hack, using modulename makes the test fail
ffi2 = FFI()
ffi2.cdef("int foo_verify_dlopen_flags;")
lib2 = ffi2.verify("int foo_verify_dlopen_flags;",
flags=ffi2.RTLD_GLOBAL | ffi2.RTLD_LAZY)
return lib2
def test_consider_not_implemented_function_type():
ffi = FFI()
ffi.cdef("typedef union { int a; float b; } Data;"
"typedef struct { int a:2; } MyStr;"
"typedef void (*foofunc_t)(Data);"
"typedef Data (*bazfunc_t)(void);"
"typedef MyStr (*barfunc_t)(void);")
fooptr = ffi.cast("foofunc_t", 123)
bazptr = ffi.cast("bazfunc_t", 123)
barptr = ffi.cast("barfunc_t", 123)
# assert did not crash so far
e = py.test.raises(NotImplementedError, fooptr, ffi.new("Data *"))
assert str(e.value) == (
"ctype 'Data' not supported as argument by libffi. Unions are only "
"supported as argument if the function is 'API mode' and "
"non-variadic (i.e. declared inside ffibuilder.cdef()+"
"ffibuilder.set_source() and not taking a final '...' argument)")
e = py.test.raises(NotImplementedError, bazptr)
assert str(e.value) == (
"ctype 'Data' not supported as return value by libffi. Unions are "
"only supported as return value if the function is 'API mode' and "
"non-variadic (i.e. declared inside ffibuilder.cdef()+"
"ffibuilder.set_source() and not taking a final '...' argument)")
e = py.test.raises(NotImplementedError, barptr)
assert str(e.value) == (
"ctype 'MyStr' not supported as return value. It is a struct with "
"bit fields, which libffi does not support. Such structs are only "
"supported as return value if the function is 'API mode' and non-"
"variadic (i.e. declared inside ffibuilder.cdef()+ffibuilder."
"set_source() and not taking a final '...' argument)")
def test_verify_extra_arguments():
ffi = FFI()
ffi.cdef("#define ABA ...")
lib = ffi.verify("", define_macros=[('ABA', '42')])
assert lib.ABA == 42
def test_implicit_unicode_on_windows():
if sys.platform != 'win32':
py.test.skip("win32-only test")
ffi = FFI()
e = py.test.raises(FFIError, ffi.cdef, "int foo(LPTSTR);")
assert str(e.value) == ("The Windows type 'LPTSTR' is only available after"
" you call ffi.set_unicode()")
for with_unicode in [True, False]:
ffi = FFI()
ffi.set_unicode(with_unicode)
ffi.cdef("""
DWORD GetModuleFileName(HMODULE hModule, LPTSTR lpFilename,
DWORD nSize);
""")
lib = ffi.verify("""
#include <windows.h>
""", libraries=['Kernel32'])
outbuf = ffi.new("TCHAR[]", 200)
n = lib.GetModuleFileName(ffi.NULL, outbuf, 500)
assert 0 < n < 500
for i in range(n):
#print repr(outbuf[i])
assert ord(outbuf[i]) != 0
assert ord(outbuf[n]) == 0
assert ord(outbuf[0]) < 128 # should be a letter, or '\'
def test_use_local_dir():
ffi = FFI()
lib = ffi.verify("", modulename="test_use_local_dir")
this_dir = os.path.dirname(__file__)
pycache_files = os.listdir(os.path.join(this_dir, '__pycache__'))
assert any('test_use_local_dir' in s for s in pycache_files)
def test_define_known_value():
ffi = FFI()
ffi.cdef("#define FOO 0x123")
lib = ffi.verify("#define FOO 0x123")
assert lib.FOO == 0x123
def test_define_wrong_value():
ffi = FFI()
ffi.cdef("#define FOO 123")
e = py.test.raises(VerificationError, ffi.verify, "#define FOO 124")
assert str(e.value).endswith("FOO has the real value 124, not 123")
def test_static_const_int_known_value():
ffi = FFI()
ffi.cdef("static const int FOO = 0x123;")
lib = ffi.verify("#define FOO 0x123")
assert lib.FOO == 0x123
def test_static_const_int_wrong_value():
ffi = FFI()
ffi.cdef("static const int FOO = 123;")
e = py.test.raises(VerificationError, ffi.verify, "#define FOO 124")
assert str(e.value).endswith("FOO has the real value 124, not 123")
def test_const_struct_global():
ffi = FFI()
ffi.cdef("typedef struct { int x; ...; } T; const T myglob;")
lib = ffi.verify("typedef struct { double y; int x; } T;"
"const T myglob = { 0.1, 42 };")
assert ffi.typeof(lib.myglob) == ffi.typeof("T")
assert lib.myglob.x == 42
def test_dont_support_int_dotdotdot():
ffi = FFI()
ffi.cdef("typedef int... t1;")
e = py.test.raises(VerificationError, ffi.verify, "")
assert str(e.value) == ("feature not supported with ffi.verify(), but only "
"with ffi.set_source(): 'typedef int... t1'")
ffi = FFI()
ffi.cdef("typedef double ... t1;")
e = py.test.raises(VerificationError, ffi.verify, "")
assert str(e.value) == ("feature not supported with ffi.verify(), but only "
"with ffi.set_source(): 'typedef float... t1'")
def test_const_fields():
ffi = FFI()
ffi.cdef("""struct foo_s { const int a; void *const b; };""")
ffi.verify("""struct foo_s { const int a; void *const b; };""")
foo_s = ffi.typeof("struct foo_s")
assert foo_s.fields[0][0] == 'a'
assert foo_s.fields[0][1].type is ffi.typeof("int")
assert foo_s.fields[1][0] == 'b'
assert foo_s.fields[1][1].type is ffi.typeof("void *")
def test_win32_calling_convention_0():
ffi = FFI()
ffi.cdef("""
int call1(int(__cdecl *cb)(int));
int (*const call2)(int(__stdcall *cb)(int));
""")
lib = ffi.verify(r"""
#ifndef _MSC_VER
# define __stdcall /* nothing */
#endif
int call1(int(*cb)(int)) {
int i, result = 0;
//printf("call1: cb = %p\n", cb);
for (i = 0; i < 1000; i++)
result += cb(i);
//printf("result = %d\n", result);
return result;
}
int call2(int(__stdcall *cb)(int)) {
int i, result = 0;
//printf("call2: cb = %p\n", cb);
for (i = 0; i < 1000; i++)
result += cb(-i);
//printf("result = %d\n", result);
return result;
}
""")
@ffi.callback("int(int)")
def cb1(x):
return x * 2
@ffi.callback("int __stdcall(int)")
def cb2(x):
return x * 3
#print 'cb1 =', cb1
res = lib.call1(cb1)
assert res == 500*999*2
#print 'cb2 =', cb2
#print ffi.typeof(lib.call2)
#print 'call2 =', lib.call2
res = lib.call2(cb2)
#print '...'
assert res == -500*999*3
#print 'done'
if sys.platform == 'win32' and sys.maxsize < 2**32:
assert '__stdcall' in str(ffi.typeof(cb2))
assert '__stdcall' not in str(ffi.typeof(cb1))
py.test.raises(TypeError, lib.call1, cb2)
py.test.raises(TypeError, lib.call2, cb1)
else:
assert '__stdcall' not in str(ffi.typeof(cb2))
assert ffi.typeof(cb2) is ffi.typeof(cb1)
def test_win32_calling_convention_1():
ffi = FFI()
ffi.cdef("""
int __cdecl call1(int(__cdecl *cb)(int));
int __stdcall call2(int(__stdcall *cb)(int));
int (__cdecl *const cb1)(int);
int (__stdcall *const cb2)(int);
""")
lib = ffi.verify(r"""
#ifndef _MSC_VER
# define __cdecl
# define __stdcall
#endif
int __cdecl cb1(int x) { return x * 2; }
int __stdcall cb2(int x) { return x * 3; }
int __cdecl call1(int(__cdecl *cb)(int)) {
int i, result = 0;
//printf("here1\n");
//printf("cb = %p, cb1 = %p\n", cb, (void *)cb1);
for (i = 0; i < 1000; i++)
result += cb(i);
//printf("result = %d\n", result);
return result;
}
int __stdcall call2(int(__stdcall *cb)(int)) {
int i, result = 0;
//printf("here1\n");
//printf("cb = %p, cb2 = %p\n", cb, (void *)cb2);
for (i = 0; i < 1000; i++)
result += cb(-i);
//printf("result = %d\n", result);
return result;
}
""")
assert lib.call1(lib.cb1) == 500*999*2
assert lib.call2(lib.cb2) == -500*999*3
def test_win32_calling_convention_2():
# any mistake in the declaration of plain function (including the
# precise argument types and, here, the calling convention) are
# automatically corrected. But this does not apply to the 'cb'
# function pointer argument.
ffi = FFI()
ffi.cdef("""
int __stdcall call1(int(__cdecl *cb)(int));
int __cdecl call2(int(__stdcall *cb)(int));
int (__cdecl *const cb1)(int);
int (__stdcall *const cb2)(int);
""")
lib = ffi.verify(r"""
#ifndef _MSC_VER
# define __cdecl
# define __stdcall
#endif
int __cdecl call1(int(__cdecl *cb)(int)) {
int i, result = 0;
for (i = 0; i < 1000; i++)
result += cb(i);
return result;
}
int __stdcall call2(int(__stdcall *cb)(int)) {
int i, result = 0;
for (i = 0; i < 1000; i++)
result += cb(-i);
return result;
}
int __cdecl cb1(int x) { return x * 2; }
int __stdcall cb2(int x) { return x * 3; }
""")
assert lib.call1(lib.cb1) == 500*999*2
assert lib.call2(lib.cb2) == -500*999*3
def test_win32_calling_convention_3():
ffi = FFI()
ffi.cdef("""
struct point { int x, y; };
int (*const cb1)(struct point);
int (__stdcall *const cb2)(struct point);
struct point __stdcall call1(int(*cb)(struct point));
struct point call2(int(__stdcall *cb)(struct point));
""")
lib = ffi.verify(r"""
#ifndef _MSC_VER
# define __cdecl
# define __stdcall
#endif
struct point { int x, y; };
int cb1(struct point pt) { return pt.x + 10 * pt.y; }
int __stdcall cb2(struct point pt) { return pt.x + 100 * pt.y; }
struct point __stdcall call1(int(__cdecl *cb)(struct point)) {
int i;
struct point result = { 0, 0 };
//printf("here1\n");
//printf("cb = %p, cb1 = %p\n", cb, (void *)cb1);
for (i = 0; i < 1000; i++) {
struct point p = { i, -i };
int r = cb(p);
result.x += r;
result.y -= r;
}
return result;
}
struct point __cdecl call2(int(__stdcall *cb)(struct point)) {
int i;
struct point result = { 0, 0 };
for (i = 0; i < 1000; i++) {
struct point p = { -i, i };
int r = cb(p);
result.x += r;
result.y -= r;
}
return result;
}
""")
if sys.platform == 'win32' and sys.maxsize < 2**32:
py.test.raises(TypeError, lib.call1, lib.cb2)
py.test.raises(TypeError, lib.call2, lib.cb1)
pt = lib.call1(lib.cb1)
assert (pt.x, pt.y) == (-9*500*999, 9*500*999)
pt = lib.call2(lib.cb2)
assert (pt.x, pt.y) == (99*500*999, -99*500*999)
def _only_test_on_linux_intel():
if not sys.platform.startswith('linux'):
py.test.skip('only running the memory-intensive test on Linux')
import platform
machine = platform.machine()
if 'x86' not in machine and 'x64' not in machine:
py.test.skip('only running the memory-intensive test on x86/x64')
def test_ffi_gc_size_arg():
# with PyPy's GC, these calls to ffi.gc() would rapidly consume
# 40 GB of RAM without the third argument
_only_test_on_linux_intel()
ffi = FFI()
ffi.cdef("void *malloc(size_t); void free(void *);")
lib = ffi.verify(r"""
#include <stdlib.h>
""")
for i in range(2000):
p = lib.malloc(20*1024*1024) # 20 MB
p1 = ffi.cast("char *", p)
for j in range(0, 20*1024*1024, 4096):
p1[j] = b'!'
p = ffi.gc(p, lib.free, 20*1024*1024)
del p
def test_ffi_gc_size_arg_2():
# a variant of the above: this "attack" works on cpython's cyclic gc too
# and I found no obvious way to prevent that. So for now, this test
# is skipped on CPython, where it eats all the memory.
if '__pypy__' not in sys.builtin_module_names:
py.test.skip("find a way to tweak the cyclic GC of CPython")
_only_test_on_linux_intel()
ffi = FFI()
ffi.cdef("void *malloc(size_t); void free(void *);")
lib = ffi.verify(r"""
#include <stdlib.h>
""")
class X(object):
pass
for i in range(2000):
p = lib.malloc(50*1024*1024) # 50 MB
p1 = ffi.cast("char *", p)
for j in range(0, 50*1024*1024, 4096):
p1[j] = b'!'
p = ffi.gc(p, lib.free, 50*1024*1024)
x = X()
x.p = p
x.cyclic = x
del p, x
def test_ffi_new_with_cycles():
# still another variant, with ffi.new()
if '__pypy__' not in sys.builtin_module_names:
py.test.skip("find a way to tweak the cyclic GC of CPython")
ffi = FFI()
ffi.cdef("")
lib = ffi.verify("")
class X(object):
pass
for i in range(2000):
p = ffi.new("char[]", 50*1024*1024) # 50 MB
for j in range(0, 50*1024*1024, 4096):
p[j] = b'!'
x = X()
x.p = p
x.cyclic = x
del p, x
|
tests/functional/startup.py | bianhaoyi/necache | 351 | 12611756 | __doc__ = '''
Testing the various startup parameters.
'''
__author__ = "<NAME> <<EMAIL>>"
__version__ = "0.1-1.45"
import os
import sys
import time
import subprocess
try:
from lib import memcache
except ImportError:
print "Check your sys.path setting to include lib/memcache.py."
sys.exit()
try:
import unittest2 as unittest
except ImportError:
import unittest
# handling server and data configurations
from config.defaults import *
from lib.utilities import *
from lib.logging import print_module_title, print_module_done
class Args(object):
def __init__(self, command=None, config=None):
self.command = command
self.config = config
def setUpModule():
print_module_title(__name__)
global counter
counter = 0
def tearDownModule():
print_module_done(__name__)
class FunctionalStartup(unittest.TestCase):
# setup&teardown client
def setUp(self):
global counter
counter += 1
print " running test %d" % counter
self.server = None
self.mc = memcache.Client(["%s:%s" % (SERVER, PORT)], debug=0)
def tearDown(self):
if self.server:
stopServer(self.server)
#
# tests
#
def test_default(self):
'''use default settings.'''
config = os.path.expanduser(TESTS_PATH) + '/config/server/default.py'
execfile(config)
args = Args(config=config)
self.server = startServer(args)
self.assertIsNotNone(self.server)
stats = self.mc.get_stats('settings')
values = stats[0][1]
self.assertEqual(str(THREADS), values['num_workers'])
self.assertEqual(str(PORT), values['tcpport'])
self.assertEqual(str(SERVER), values['interface'])
self.assertEqual(str(MAX_MEMORY * 1024 * 1024), values['maxbytes'])
self.assertEqual(str(CONNECTIONS), values['maxconns'])
self.assertEqual(str(ITEM_MIN_SIZE), values['chunk_size'])
self.assertEqual(str(BACKLOG), values['tcp_backlog'])
self.assertEqual(str(SLAB_SIZE), values['slab_size'])
self.assertEqual(str(FACTOR), values['growth_factor'])
def test_cas(self):
'''disable cas, -C'''
args = Args(command='CAS = True')
self.server = startServer(args)
self.assertIsNotNone(self.server)
stats = self.mc.get_stats('settings')
self.assertEqual('0', stats[0][1]['cas_enabled'])
def test_prealloc(self):
'''prealloc, -E'''
args = Args(command='PREALLOC = True\nMAX_MEMORY = 2\nEVICTION = 1')
self.server = startServer(args)
self.assertIsNotNone(self.server)
stats =self.mc.get_stats('settings')
self.assertEqual('1', stats[0][1]['prealloc'])
self.assertEqual(True, self.mc.set("foo", "bar"))
self.assertEqual(True, self.mc.set("foo" * 10, "bar" * 10))
self.assertEqual(False, self.mc.set("foo" * 50, "bar" * 50))
def test_daemon(self):
'''daemon, -d'''
args = Args(command='DAEMON = True\nPIDFILE = "/tmp/mcpid"')
self.server = startServer(args)
self.assertIsNotNone(self.server)
pid = open("/tmp/mcpid").read().rstrip()
stats =self.mc.get_stats()
self.assertEqual(pid, stats[0][1]['pid'])
subprocess.Popen(['kill', pid])
time.sleep(SHUTDOWN_DELAY)
self.assertEqual(False, self.mc.set("foo", "bar"))# cannot connect
def test_maxconn(self):
'''maximum connections, -c'''
args = Args(command='CONNECTIONS = 10')
self.server = startServer(args)
self.assertIsNotNone(self.server)
conns = {}
for i in range(0, 10):
conns[i] = memcache.Client(["%s:%s" % (SERVER, PORT)])
self.assertTrue(conns[i].set("%d" % i, "%d" % i))
def test_aggrintrvl(self):
'''aggregation interval, -A'''
args = Args(command='AGGR_INTERVAL = 1000000')
self.server = startServer(args)
self.assertIsNotNone(self.server)
stats = self.mc.get_stats('settings')
self.assertEqual(1.0, float(stats[0][1]['stats_agg_intvl']))
def test_slabsize(self):
'''Choose a different slab/max-item size, -I'''
# jumbo slabs
args = Args(command='SLAB_SIZE = 1024 * 1024 * 4')
slabsize = 1024 * 1024 * 4
self.server = startServer(args)
self.assertIsNotNone(self.server)
# here we use a different memcache client to set value length differently
mc = memcache.Client(["%s:%s" % (SERVER, PORT)], debug=0, server_max_value_length=slabsize)
mc.set("lval", 'a' * (slabsize - 512))
self.assertEqual(slabsize - 512, len(mc.get("lval")))
def test_slabfile(self):
'''Initalize slab classes with a size profile, -z'''
# create a slab profile first
args = Args(command='SLAB_PROFILE = "64,128,256"')
self.server = startServer(args)
self.assertIsNotNone(self.server)
if __name__ == '__main__':
functional_startup = unittest.TestLoader().loadTestsFromTestCase(FunctionalStartup)
unittest.TextTestRunner(verbosity=2).run(functional_startup)
|
venv/Lib/site-packages/debugpy/launcher/output.py | ajayiagbebaku/NFL-Model | 695 | 12611795 | <gh_stars>100-1000
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in the project root
# for license information.
from __future__ import absolute_import, division, print_function, unicode_literals
import codecs
import os
import sys
import threading
from debugpy import launcher
from debugpy.common import log
class CaptureOutput(object):
"""Captures output from the specified file descriptor, and tees it into another
file descriptor while generating DAP "output" events for it.
"""
instances = {}
"""Keys are output categories, values are CaptureOutput instances."""
def __init__(self, whose, category, fd, stream):
assert category not in self.instances
self.instances[category] = self
log.info("Capturing {0} of {1}.", category, whose)
self.category = category
self._whose = whose
self._fd = fd
self._decoder = codecs.getincrementaldecoder("utf-8")(errors="surrogateescape")
if stream is None:
# Can happen if running under pythonw.exe.
self._stream = None
else:
self._stream = stream if sys.version_info < (3,) else stream.buffer
encoding = stream.encoding
if encoding is None or encoding == "cp65001":
encoding = "utf-8"
try:
self._encode = codecs.getencoder(encoding)
except Exception:
log.swallow_exception(
"Unsupported {0} encoding {1!r}; falling back to UTF-8.",
category,
encoding,
level="warning",
)
self._encode = codecs.getencoder("utf-8")
self._worker_thread = threading.Thread(target=self._worker, name=category)
self._worker_thread.start()
def __del__(self):
fd = self._fd
if fd is not None:
try:
os.close(fd)
except Exception:
pass
def _worker(self):
while self._fd is not None:
try:
s = os.read(self._fd, 0x1000)
except Exception:
break
if not len(s):
break
self._process_chunk(s)
# Flush any remaining data in the incremental decoder.
self._process_chunk(b"", final=True)
def _process_chunk(self, s, final=False):
s = self._decoder.decode(s, final=final)
if len(s) == 0:
return
try:
launcher.channel.send_event(
"output", {"category": self.category, "output": s.replace("\r\n", "\n")}
)
except Exception:
pass # channel to adapter is already closed
if self._stream is None:
return
s, _ = self._encode(s, "surrogateescape")
size = len(s)
i = 0
while i < size:
# On Python 2, all writes are full writes, and write() returns None.
# On Python 3, writes can be partial, and write() returns the count.
written = self._stream.write(s[i:])
self._stream.flush()
if written is None: # full write
break
elif written == 0:
# This means that the output stream was closed from the other end.
# Do the same to the debuggee, so that it knows as well.
os.close(self._fd)
self._fd = None
break
i += written
def wait_for_remaining_output():
"""Waits for all remaining output to be captured and propagated.
"""
for category, instance in CaptureOutput.instances.items():
log.info("Waiting for remaining {0} of {1}.", category, instance._whose)
instance._worker_thread.join()
|
test/tests/fun.py | RangelReale/libpypa | 152 | 12611802 | def fun(a, b, k=None, *argc, **kwargs):
pass
|
tests/test_50_server.py | brunato/pysaml2 | 249 | 12611811 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import base64
import copy
import os
from contextlib import closing
from six.moves.urllib.parse import parse_qs
import uuid
import re
from saml2.cert import OpenSSLWrapper
from saml2.sigver import make_temp, DecryptError, EncryptError, CertificateError
from saml2.assertion import Policy
from saml2.authn_context import INTERNETPROTOCOLPASSWORD
from saml2.saml import NameID, NAMEID_FORMAT_TRANSIENT
from saml2.samlp import response_from_string
from saml2.server import Server
from saml2 import samlp
from saml2 import saml
from saml2 import client
from saml2 import config
from saml2 import extension_elements_to_elements
from saml2 import s_utils
from saml2 import sigver
from saml2 import time_util
from saml2 import VERSION
from saml2.s_utils import OtherError
from saml2.s_utils import do_attribute_statement
from saml2.s_utils import factory
from saml2.s_utils import sid
from saml2.soap import make_soap_enveloped_saml_thingy
from saml2 import BINDING_HTTP_POST
from saml2 import BINDING_HTTP_REDIRECT
from saml2.time_util import instant
from pytest import raises
from pathutils import full_path
import saml2.xmldsig as ds
nid = NameID(name_qualifier="foo", format=NAMEID_FORMAT_TRANSIENT,
text="123456")
AUTHN = {
"class_ref": INTERNETPROTOCOLPASSWORD,
"authn_auth": "http://www.example.com/login"
}
def response_factory(**kwargs):
response = samlp.Response(id=sid(), version=VERSION, issue_instant=instant())
for key, val in kwargs.items():
setattr(response, key, val)
return response
def _eq(l1, l2):
return set(l1) == set(l2)
BASEDIR = os.path.abspath(os.path.dirname(__file__))
def get_ava(assertion):
ava = {}
for statement in assertion.attribute_statement:
for attr in statement.attribute:
value = []
for tmp_val in attr.attribute_value:
value.append(tmp_val.text)
key = attr.friendly_name
if key is None or len(key) == 0:
key = attr.text
ava[key] = value
return ava
def generate_cert():
sn = uuid.uuid4().urn
cert_info = {
"cn": "localhost",
"country_code": "se",
"state": "ac",
"city": "Umea",
"organization": "ITS",
"organization_unit": "DIRG"
}
osw = OpenSSLWrapper()
ca_cert_str = osw.read_str_from_file(
full_path("root_cert/localhost.ca.crt"))
ca_key_str = osw.read_str_from_file(
full_path("root_cert/localhost.ca.key"))
req_cert_str, req_key_str = osw.create_certificate(cert_info, request=True,
sn=sn, key_length=2048)
cert_str = osw.create_cert_signed_certificate(ca_cert_str, ca_key_str,
req_cert_str)
return cert_str, req_key_str
class TestServer1():
def setup_class(self):
self.server = Server("idp_conf")
conf = config.SPConfig()
conf.load_file("server_conf")
self.client = client.Saml2Client(conf)
self.name_id = self.server.ident.transient_nameid(
"urn:mace:example.com:saml:roland:sp", "id12")
self.ava = {"givenName": ["Derek"], "sn": ["Jeter"],
"mail": ["<EMAIL>"], "title": "The man"}
def teardown_class(self):
self.server.close()
def verify_assertion(self, assertion):
assert assertion
assert assertion[0].attribute_statement
ava = ava = get_ava(assertion[0])
assert ava ==\
{'mail': ['<EMAIL>'], 'givenName': ['Derek'],
'sn': ['Jeter'], 'title': ['The man']}
def verify_encrypted_assertion(self, assertion, decr_text):
self.verify_assertion(assertion)
assert assertion[0].signature is None
assert re.search(
r':EncryptedAssertion><encas[0-9]:Assertion ([^ >]* )*xmlns:encas[0-9]="urn:oasis:names:tc:SAML:2.0:assertion"',
decr_text,
)
def verify_advice_assertion(self, resp, decr_text):
assert resp.assertion[0].signature is None
assert resp.assertion[0].advice.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.assertion[0].advice.encrypted_assertion[0].extension_elements,
[saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_issuer(self):
issuer = self.server._issuer()
assert isinstance(issuer, saml.Issuer)
assert _eq(issuer.keyswv(), ["text", "format"])
assert issuer.format == saml.NAMEID_FORMAT_ENTITY
assert issuer.text == self.server.config.entityid
def test_assertion(self):
assertion = s_utils.assertion_factory(
subject=factory(
saml.Subject, text="_aaa",
name_id=factory(saml.NameID,
format=saml.NAMEID_FORMAT_TRANSIENT)),
attribute_statement=do_attribute_statement(
{
("", "", "sn"): ("Jeter", ""),
("", "", "givenName"): ("Derek", ""),
}
),
issuer=self.server._issuer(),
)
assert _eq(assertion.keyswv(), ['attribute_statement', 'issuer', 'id',
'subject', 'issue_instant', 'version'])
assert assertion.version == "2.0"
assert assertion.issuer.text == "urn:mace:example.com:saml:roland:idp"
#
assert assertion.attribute_statement
attribute_statement = assertion.attribute_statement
assert len(attribute_statement.attribute) == 2
attr0 = attribute_statement.attribute[0]
attr1 = attribute_statement.attribute[1]
if attr0.attribute_value[0].text == "Derek":
assert attr0.friendly_name == "givenName"
assert attr1.friendly_name == "sn"
assert attr1.attribute_value[0].text == "Jeter"
else:
assert attr1.friendly_name == "givenName"
assert attr1.attribute_value[0].text == "Derek"
assert attr0.friendly_name == "sn"
assert attr0.attribute_value[0].text == "Jeter"
subject = assertion.subject
assert _eq(subject.keyswv(), ["text", "name_id"])
assert subject.text == "_aaa"
assert subject.name_id.format == saml.NAMEID_FORMAT_TRANSIENT
def test_response(self):
response = response_factory(
in_response_to="_012345",
destination="https:#www.example.com",
status=s_utils.success_status_factory(),
assertion=s_utils.assertion_factory(
subject=factory(saml.Subject, text="_aaa",
name_id=saml.NAMEID_FORMAT_TRANSIENT),
attribute_statement=do_attribute_statement(
{
("", "", "sn"): ("Jeter", ""),
("", "", "givenName"): ("Derek", ""),
}
),
issuer=self.server._issuer(),
),
issuer=self.server._issuer(),
)
print(response.keyswv())
assert _eq(response.keyswv(), ['destination', 'assertion', 'status',
'in_response_to', 'issue_instant',
'version', 'issuer', 'id'])
assert response.version == "2.0"
assert response.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert response.destination == "https:#www.example.com"
assert response.in_response_to == "_012345"
#
status = response.status
print(status)
assert status.status_code.value == samlp.STATUS_SUCCESS
def test_parse_faulty_request(self):
req_id, authn_request = self.client.create_authn_request(
destination="http://www.example.com", id="id1")
# should raise an error because faulty spentityid
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(
binding, "%s" % authn_request, "http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
with raises(OtherError):
self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
def test_parse_faulty_request_to_err_status(self):
req_id, authn_request = self.client.create_authn_request(
destination="http://www.example.com")
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(binding, "%s" % authn_request,
"http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
try:
self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
status = None
except OtherError as oe:
print(oe.args)
status = s_utils.error_status_factory(oe)
assert status
print(status)
assert _eq(status.keyswv(), ["status_code", "status_message"])
assert status.status_message.text == 'Not destined for me!'
status_code = status.status_code
assert _eq(status_code.keyswv(), ["status_code", "value"])
assert status_code.value == samlp.STATUS_RESPONDER
assert status_code.status_code.value == samlp.STATUS_UNKNOWN_PRINCIPAL
def test_parse_ok_request(self):
req_id, authn_request = self.client.create_authn_request(
message_id="id1",
destination="http://localhost:8088/sso",
nameid_format=saml.NAMEID_FORMAT_TRANSIENT,
)
print(authn_request)
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(binding, "%s" % authn_request,
"http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
req = self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
# returns a dictionary
print(req)
resp_args = self.server.response_args(req.message, [BINDING_HTTP_POST])
assert resp_args["destination"] == "http://lingon.catalogix.se:8087/"
assert resp_args["in_response_to"] == "id1"
name_id_policy = resp_args["name_id_policy"]
assert _eq(name_id_policy.keyswv(), ["format"])
assert name_id_policy.format == saml.NAMEID_FORMAT_TRANSIENT
assert resp_args[
"sp_entity_id"] == "urn:mace:example.com:saml:roland:sp"
def test_sso_response_with_identity(self):
name_id = self.server.ident.transient_nameid(
"https://example.com/sp", "id12")
resp = self.server.create_authn_response(
{
"eduPersonEntitlement": "Short stop",
"sn": "Jeter",
"givenName": "Derek",
"mail": "<EMAIL>",
"title": "The man"
},
"id12", # in_response_to
"http://localhost:8087/", # destination
"https://example.com/sp", # sp_entity_id
name_id=name_id,
authn=AUTHN
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'assertion',
'in_response_to', 'issue_instant',
'version', 'id', 'issuer'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
assert resp.status.status_code.value == samlp.STATUS_SUCCESS
assert resp.assertion
assertion = resp.assertion
print(assertion)
assert assertion.authn_statement
assert assertion.conditions
assert assertion.attribute_statement
attribute_statement = assertion.attribute_statement
print(attribute_statement)
assert len(attribute_statement[0].attribute) == 4
# Pick out one attribute
attr = None
for attr in attribute_statement[0].attribute:
if attr.friendly_name == "givenName":
break
assert len(attr.attribute_value) == 1
assert attr.name == "urn:mace:dir:attribute-def:givenName"
assert attr.name_format == "urn:oasis:names:tc:SAML:2.0:attrname-format:basic"
value = attr.attribute_value[0]
assert value.text.strip() == "Derek"
assert value.get_type() == "xs:string"
assert assertion.subject
assert assertion.subject.name_id
assert assertion.subject.subject_confirmation
confirmation = assertion.subject.subject_confirmation[0]
print(confirmation.keyswv())
print(confirmation.subject_confirmation_data)
assert confirmation.subject_confirmation_data.in_response_to == "id12"
def test_sso_response_without_identity(self):
resp = self.server.create_authn_response(
{},
"id12", # in_response_to
"http://localhost:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
userid="USER1",
authn=AUTHN,
release_policy=Policy(),
best_effort=True
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer',
'assertion'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
assert resp.status.status_code.value == samlp.STATUS_SUCCESS
assert resp.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert not resp.assertion.attribute_statement
def test_sso_response_specific_instant(self):
_authn = AUTHN.copy()
_authn["authn_instant"] = 1234567890
resp = self.server.create_authn_response(
{},
"id12", # in_response_to
"http://localhost:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
userid="USER1",
authn=_authn,
best_effort=True
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer',
'assertion'])
authn_statement = resp.assertion.authn_statement[0]
assert authn_statement.authn_instant == '2009-02-13T23:31:30Z'
def test_sso_failure_response(self):
exc = s_utils.MissingValue("eduPersonAffiliation missing")
resp = self.server.create_error_response(
"id12", "http://localhost:8087/", exc)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
print(resp.status)
assert resp.status.status_code.value == samlp.STATUS_RESPONDER
assert resp.status.status_code.status_code.value == \
samlp.STATUS_REQUEST_UNSUPPORTED
assert resp.status.status_message.text == \
"eduPersonAffiliation missing"
assert resp.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert not resp.assertion
def test_authn_response_0(self):
conf = config.SPConfig()
conf.load_file("server_conf")
self.client = client.Saml2Client(conf)
ava = {"givenName": ["Derek"], "sn": ["Jeter"],
"mail": ["<EMAIL>"], "title": "The man"}
npolicy = samlp.NameIDPolicy(format=saml.NAMEID_FORMAT_TRANSIENT,
allow_create="true")
resp_str = "%s" % self.server.create_authn_response(
ava, "id1", "http://local:8087/",
"urn:mace:example.com:saml:roland:sp", npolicy,
"<EMAIL>", authn=AUTHN)
response = samlp.response_from_string(resp_str)
print(response.keyswv())
assert _eq(response.keyswv(), ['status', 'destination', 'assertion',
'in_response_to', 'issue_instant',
'version', 'issuer', 'id'])
print(response.assertion[0].keyswv())
assert len(response.assertion) == 1
assert _eq(response.assertion[0].keyswv(), ['attribute_statement',
'issue_instant', 'version',
'subject', 'conditions',
'id', 'issuer',
'authn_statement'])
assertion = response.assertion[0]
assert len(assertion.attribute_statement) == 1
astate = assertion.attribute_statement[0]
print(astate)
assert len(astate.attribute) == 4
def test_signed_response(self):
name_id = self.server.ident.transient_nameid(
"urn:mace:example.com:saml:roland:sp", "id12")
ava = {"givenName": ["Derek"], "sn": ["Jeter"],
"mail": ["<EMAIL>"], "title": "The man"}
signed_resp = self.server.create_authn_response(
ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=name_id,
sign_assertion=True
)
print(signed_resp)
assert signed_resp
sresponse = response_from_string(signed_resp)
# It's the assertions that are signed not the response per se
assert len(sresponse.assertion) == 1
assertion = sresponse.assertion[0]
# Since the reponse is created dynamically I don't know the signature
# value. Just that there should be one
assert assertion.signature.signature_value.text != ""
def test_signed_response_1(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id)
assert valid
self.verify_assertion(sresponse.assertion)
def test_signed_response_2(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=False,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
assert sresponse.assertion[0].signature == None
def test_signed_response_3(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=True,
)
sresponse = response_from_string(signed_resp)
assert sresponse.signature == None
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id)
assert valid
self.verify_assertion(sresponse.assertion)
def test_encrypted_signed_response_1(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(
signed_resp, self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id
)
assert valid
valid = self.server.sec.verify_signature(
signed_resp, self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id
)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(signed_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
assert resp.assertion[0].advice.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(
resp.assertion[0].advice.encrypted_assertion[0].extension_elements,
[saml, samlp])
self.verify_assertion(assertion)
#PEFIM never signs assertions.
assert assertion[0].signature is None
#valid = self.server.sec.verify_signature(decr_text,
# self.server.config.cert_file,
# node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
# node_id=assertion[0].id)
assert valid
def test_encrypted_signed_response_2(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
decr_text_old = copy.deepcopy("%s" % signed_resp)
with raises(DecryptError):
decr_text = self.server.sec.decrypt(
signed_resp,
self.client.config.encryption_keypairs[0]["key_file"],
)
decr_text = self.server.sec.decrypt(signed_resp, self.client.config.encryption_keypairs[1]["key_file"])
assert decr_text != decr_text_old
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
assert resp.assertion[0].signature == None
self.verify_assertion(resp.assertion)
def test_encrypted_signed_response_3(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=True,
encrypt_assertion_self_contained=False,
encrypt_cert_assertion=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(signed_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
valid = self.server.sec.verify_signature(decr_text,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=resp.assertion[0].id)
assert valid
self.verify_assertion(resp.assertion)
assert 'xmlns:encas' not in decr_text
def test_encrypted_signed_response_4(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
decr_text = self.server.sec.decrypt(signed_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
valid = self.server.sec.verify_signature(decr_text,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=resp.assertion[0].id)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(decr_text, key_fd.name)
resp = samlp.response_from_string(decr_text)
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
assertion = \
extension_elements_to_elements(assertion[0].advice.encrypted_assertion[0].extension_elements,[saml, samlp])
self.verify_assertion(assertion)
#PEFIM never signs assertion in advice
assert assertion[0].signature is None
#valid = self.server.sec.verify_signature(decr_text,
# self.server.config.cert_file,
# node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
# node_id=assertion[0].id)
assert valid
def test_encrypted_response_1(self):
cert_str_advice, cert_key_str_advice = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
)
_resp = "%s" % _resp
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd = make_temp(cert_key_str_advice, decode=False)
decr_text = self.server.sec.decrypt(_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
self.verify_advice_assertion(resp, decr_text)
def test_encrypted_response_2(self):
cert_str_advice, cert_key_str_advice = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text_1 = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
key_fd = make_temp(cert_key_str_advice, decode=False)
decr_text_2 = self.server.sec.decrypt(decr_text_1, key_fd.name)
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_3(self):
cert_str_assertion, cert_key_str_assertion = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion=cert_str_assertion
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd = make_temp(cert_key_str_assertion, decode=False)
decr_text = self.server.sec.decrypt(_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
assert resp.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_encrypted_response_4(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
assert resp.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_encrypted_response_5(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True
)
_resp = "%s" % _resp
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
self.verify_advice_assertion(resp, decr_text)
def test_encrypted_response_6(self):
_server = Server("idp_conf_verify_cert")
cert_str_advice, cert_key_str_advice = generate_cert()
cert_str_assertion, cert_key_str_assertion = generate_cert()
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
encrypt_cert_assertion=cert_str_assertion
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd1 = make_temp(cert_key_str_assertion, decode=False)
decr_text_1 = _server.sec.decrypt(_resp, key_fd1.name)
key_fd2 = make_temp(cert_key_str_advice, decode=False)
decr_text_2 = _server.sec.decrypt(decr_text_1, key_fd2.name)
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_7(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text_1 = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
decr_text_2 = self.server.sec.decrypt(decr_text_1, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_8(self):
with raises(EncryptError):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
encrypt_cert_assertion="whatever"
)
with raises(EncryptError):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
)
with raises(EncryptError):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion="whatever"
)
_server = Server("idp_conf_verify_cert")
with raises(CertificateError):
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
encrypt_cert_assertion="whatever"
)
with raises(CertificateError):
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
)
with raises(CertificateError):
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion="whatever"
)
def test_encrypted_response_9(self):
_server = Server("idp_conf_sp_no_encrypt")
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
)
self.verify_assertion(_resp.assertion.advice.assertion)
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True
)
self.verify_assertion(_resp.assertion.advice.assertion)
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
)
self.verify_assertion([_resp.assertion])
def test_slo_http_post(self):
soon = time_util.in_a_while(days=1)
sinfo = {
"name_id": nid,
"issuer": "urn:mace:example.com:saml:roland:idp",
"not_on_or_after": soon,
"user": {
"givenName": "Leo",
"sn": "Laport",
}
}
self.client.users.add_information_about_person(sinfo)
req_id, logout_request = self.client.create_logout_request(
destination="http://localhost:8088/slop", name_id=nid,
issuer_entity_id="urn:mace:example.com:saml:roland:idp",
reason="I'm tired of this")
intermed = base64.b64encode(str(logout_request).encode('utf-8'))
#saml_soap = make_soap_enveloped_saml_thingy(logout_request)
request = self.server.parse_logout_request(intermed, BINDING_HTTP_POST)
assert request
def test_slo_soap(self):
soon = time_util.in_a_while(days=1)
sinfo = {
"name_id": nid,
"issuer": "urn:mace:example.com:saml:roland:idp",
"not_on_or_after": soon,
"user": {
"givenName": "Leo",
"sn": "Laport",
}
}
sp = client.Saml2Client(config_file="server_conf")
sp.users.add_information_about_person(sinfo)
req_id, logout_request = sp.create_logout_request(
name_id=nid, destination="http://localhost:8088/slo",
issuer_entity_id="urn:mace:example.com:saml:roland:idp",
reason="I'm tired of this")
#_ = s_utils.deflate_and_base64_encode("%s" % (logout_request,))
saml_soap = make_soap_enveloped_saml_thingy(logout_request)
self.server.ident.close()
with closing(Server("idp_soap_conf")) as idp:
request = idp.parse_logout_request(saml_soap)
idp.ident.close()
assert request
# ------------------------------------------------------------------------
class TestServer1NonAsciiAva():
def setup_class(self):
self.server = Server("idp_conf")
conf = config.SPConfig()
conf.load_file("server_conf")
self.client = client.Saml2Client(conf)
self.name_id = self.server.ident.transient_nameid(
"urn:mace:example.com:saml:roland:sp", "id12")
self.ava = {"givenName": ["Dave"], "sn": ["Concepción"],
"mail": ["<EMAIL>"], "title": "#13"}
def teardown_class(self):
self.server.close()
def verify_assertion(self, assertion):
assert assertion
assert assertion[0].attribute_statement
ava = get_ava(assertion[0])
assert ava == \
{"givenName": ["Dave"], "sn": [u"Concepción"],
"mail": ["<EMAIL>"], "title": ["#13"]}
def verify_encrypted_assertion(self, assertion, decr_text):
self.verify_assertion(assertion)
assert assertion[0].signature is None
assert re.search(
r':EncryptedAssertion><encas[0-9]:Assertion ([^ >]* )*xmlns:encas[0-9]="urn:oasis:names:tc:SAML:2.0:assertion"',
decr_text,
)
def verify_advice_assertion(self, resp, decr_text):
assert resp.assertion[0].signature is None
assert resp.assertion[0].advice.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.assertion[0].advice.encrypted_assertion[0].extension_elements,
[saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_issuer(self):
issuer = self.server._issuer()
assert isinstance(issuer, saml.Issuer)
assert _eq(issuer.keyswv(), ["text", "format"])
assert issuer.format == saml.NAMEID_FORMAT_ENTITY
assert issuer.text == self.server.config.entityid
def test_assertion(self):
assertion = s_utils.assertion_factory(
subject=factory(
saml.Subject, text="_aaa",
name_id=factory(saml.NameID,
format=saml.NAMEID_FORMAT_TRANSIENT)),
attribute_statement=do_attribute_statement(
{
("", "", "sn"): ("Jeter", ""),
("", "", "givenName"): ("Derek", ""),
}
),
issuer=self.server._issuer(),
)
assert _eq(assertion.keyswv(), ['attribute_statement', 'issuer', 'id',
'subject', 'issue_instant', 'version'])
assert assertion.version == "2.0"
assert assertion.issuer.text == "urn:mace:example.com:saml:roland:idp"
#
assert assertion.attribute_statement
attribute_statement = assertion.attribute_statement
assert len(attribute_statement.attribute) == 2
attr0 = attribute_statement.attribute[0]
attr1 = attribute_statement.attribute[1]
if attr0.attribute_value[0].text == "Derek":
assert attr0.friendly_name == "givenName"
assert attr1.friendly_name == "sn"
assert attr1.attribute_value[0].text == "Jeter"
else:
assert attr1.friendly_name == "givenName"
assert attr1.attribute_value[0].text == "Derek"
assert attr0.friendly_name == "sn"
assert attr0.attribute_value[0].text == "Jeter"
#
subject = assertion.subject
assert _eq(subject.keyswv(), ["text", "name_id"])
assert subject.text == "_aaa"
assert subject.name_id.format == saml.NAMEID_FORMAT_TRANSIENT
def test_response(self):
response = response_factory(
in_response_to="_012345",
destination="https:#www.example.com",
status=s_utils.success_status_factory(),
assertion=s_utils.assertion_factory(
subject=factory(saml.Subject, text="_aaa",
name_id=saml.NAMEID_FORMAT_TRANSIENT),
attribute_statement=do_attribute_statement(
{
("", "", "sn"): ("Jeter", ""),
("", "", "givenName"): ("Derek", ""),
}
),
issuer=self.server._issuer(),
),
issuer=self.server._issuer(),
)
print(response.keyswv())
assert _eq(response.keyswv(), ['destination', 'assertion', 'status',
'in_response_to', 'issue_instant',
'version', 'issuer', 'id'])
assert response.version == "2.0"
assert response.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert response.destination == "https:#www.example.com"
assert response.in_response_to == "_012345"
#
status = response.status
print(status)
assert status.status_code.value == samlp.STATUS_SUCCESS
def test_parse_faulty_request(self):
req_id, authn_request = self.client.create_authn_request(
destination="http://www.example.com", id="id1")
# should raise an error because faulty spentityid
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(
binding, "%s" % authn_request, "http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
with raises(OtherError):
self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
def test_parse_faulty_request_to_err_status(self):
req_id, authn_request = self.client.create_authn_request(
destination="http://www.example.com")
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(binding, "%s" % authn_request,
"http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
try:
self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
status = None
except OtherError as oe:
print(oe.args)
status = s_utils.error_status_factory(oe)
assert status
print(status)
assert _eq(status.keyswv(), ["status_code", "status_message"])
assert status.status_message.text == 'Not destined for me!'
status_code = status.status_code
assert _eq(status_code.keyswv(), ["status_code", "value"])
assert status_code.value == samlp.STATUS_RESPONDER
assert status_code.status_code.value == samlp.STATUS_UNKNOWN_PRINCIPAL
def test_parse_ok_request(self):
req_id, authn_request = self.client.create_authn_request(
message_id="id1",
destination="http://localhost:8088/sso",
nameid_format=saml.NAMEID_FORMAT_TRANSIENT,
)
print(authn_request)
binding = BINDING_HTTP_REDIRECT
htargs = self.client.apply_binding(binding, "%s" % authn_request,
"http://www.example.com", "abcd")
_dict = parse_qs(htargs["headers"][0][1].split('?')[1])
print(_dict)
req = self.server.parse_authn_request(_dict["SAMLRequest"][0], binding)
# returns a dictionary
print(req)
resp_args = self.server.response_args(req.message, [BINDING_HTTP_POST])
assert resp_args["destination"] == "http://lingon.catalogix.se:8087/"
assert resp_args["in_response_to"] == "id1"
name_id_policy = resp_args["name_id_policy"]
assert _eq(name_id_policy.keyswv(), ["format"])
assert name_id_policy.format == saml.NAMEID_FORMAT_TRANSIENT
assert resp_args[
"sp_entity_id"] == "urn:mace:example.com:saml:roland:sp"
def test_sso_response_with_identity(self):
name_id = self.server.ident.transient_nameid(
"https://example.com/sp", "id12")
resp = self.server.create_authn_response(
{
"eduPersonEntitlement": "Short stop",
"sn": "Jeter",
"givenName": "Derek",
"mail": "<EMAIL>",
"title": "The man"
},
"id12", # in_response_to
"http://localhost:8087/", # destination
"https://example.com/sp", # sp_entity_id
name_id=name_id,
authn=AUTHN
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'assertion',
'in_response_to', 'issue_instant',
'version', 'id', 'issuer'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
assert resp.status.status_code.value == samlp.STATUS_SUCCESS
assert resp.assertion
assertion = resp.assertion
print(assertion)
assert assertion.authn_statement
assert assertion.conditions
assert assertion.attribute_statement
attribute_statement = assertion.attribute_statement
print(attribute_statement)
assert len(attribute_statement[0].attribute) == 4
# Pick out one attribute
attr = None
for attr in attribute_statement[0].attribute:
if attr.friendly_name == "givenName":
break
assert len(attr.attribute_value) == 1
assert attr.name == "urn:mace:dir:attribute-def:givenName"
assert attr.name_format == "urn:oasis:names:tc:SAML:2.0:attrname-format:basic"
value = attr.attribute_value[0]
assert value.text.strip() == "Derek"
assert value.get_type() == "xs:string"
assert assertion.subject
assert assertion.subject.name_id
assert assertion.subject.subject_confirmation
confirmation = assertion.subject.subject_confirmation[0]
print(confirmation.keyswv())
print(confirmation.subject_confirmation_data)
assert confirmation.subject_confirmation_data.in_response_to == "id12"
def test_sso_response_without_identity(self):
resp = self.server.create_authn_response(
{},
"id12", # in_response_to
"http://localhost:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
userid="USER1",
authn=AUTHN,
release_policy=Policy(),
best_effort=True
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer',
'assertion'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
assert resp.status.status_code.value == samlp.STATUS_SUCCESS
assert resp.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert not resp.assertion.attribute_statement
def test_sso_response_specific_instant(self):
_authn = AUTHN.copy()
_authn["authn_instant"] = 1234567890
resp = self.server.create_authn_response(
{},
"id12", # in_response_to
"http://localhost:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
userid="USER1",
authn=_authn,
best_effort=True
)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer',
'assertion'])
authn_statement = resp.assertion.authn_statement[0]
assert authn_statement.authn_instant == '2009-02-13T23:31:30Z'
def test_sso_failure_response(self):
exc = s_utils.MissingValue("eduPersonAffiliation missing")
resp = self.server.create_error_response(
"id12", "http://localhost:8087/", exc)
print(resp.keyswv())
assert _eq(resp.keyswv(), ['status', 'destination', 'in_response_to',
'issue_instant', 'version', 'id', 'issuer'])
assert resp.destination == "http://localhost:8087/"
assert resp.in_response_to == "id12"
assert resp.status
print(resp.status)
assert resp.status.status_code.value == samlp.STATUS_RESPONDER
assert resp.status.status_code.status_code.value == \
samlp.STATUS_REQUEST_UNSUPPORTED
assert resp.status.status_message.text == \
"eduPersonAffiliation missing"
assert resp.issuer.text == "urn:mace:example.com:saml:roland:idp"
assert not resp.assertion
def test_authn_response_0(self):
conf = config.SPConfig()
conf.load_file("server_conf")
self.client = client.Saml2Client(conf)
ava = {"givenName": ["Derek"], "sn": ["Jeter"],
"mail": ["<EMAIL>"], "title": "The man"}
npolicy = samlp.NameIDPolicy(format=saml.NAMEID_FORMAT_TRANSIENT,
allow_create="true")
resp_str = "%s" % self.server.create_authn_response(
ava, "id1", "http://local:8087/",
"urn:mace:example.com:saml:roland:sp", npolicy,
"<EMAIL>", authn=AUTHN)
response = samlp.response_from_string(resp_str)
print(response.keyswv())
assert _eq(response.keyswv(), ['status', 'destination', 'assertion',
'in_response_to', 'issue_instant',
'version', 'issuer', 'id'])
print(response.assertion[0].keyswv())
assert len(response.assertion) == 1
assert _eq(response.assertion[0].keyswv(), ['attribute_statement',
'issue_instant', 'version',
'subject', 'conditions',
'id', 'issuer',
'authn_statement'])
assertion = response.assertion[0]
assert len(assertion.attribute_statement) == 1
astate = assertion.attribute_statement[0]
print(astate)
assert len(astate.attribute) == 4
def test_signed_response(self):
name_id = self.server.ident.transient_nameid(
"urn:mace:example.com:saml:roland:sp", "id12")
ava = {"givenName": ["Derek"], "sn": ["Jeter"],
"mail": ["<EMAIL>"], "title": "The man"}
signed_resp = self.server.create_authn_response(
ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=name_id,
sign_assertion=True
)
print(signed_resp)
assert signed_resp
sresponse = response_from_string(signed_resp)
# It's the assertions that are signed not the response per se
assert len(sresponse.assertion) == 1
assertion = sresponse.assertion[0]
# Since the reponse is created dynamically I don't know the signature
# value. Just that there should be one
assert assertion.signature.signature_value.text != ""
def test_signed_response_1(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id)
assert valid
self.verify_assertion(sresponse.assertion)
def test_signed_response_2(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=False,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
assert sresponse.assertion[0].signature == None
def test_signed_response_3(self):
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=True,
)
sresponse = response_from_string(signed_resp)
assert sresponse.signature == None
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id)
assert valid
self.verify_assertion(sresponse.assertion)
def test_encrypted_signed_response_1(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(
signed_resp, self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id,
)
assert valid
valid = self.server.sec.verify_signature(
signed_resp, self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=sresponse.assertion[0].id,
)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(signed_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
assert resp.assertion[0].advice.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(
resp.assertion[0].advice.encrypted_assertion[0].extension_elements,
[saml, samlp])
self.verify_assertion(assertion)
#PEFIM never signs assertions.
assert assertion[0].signature is None
#valid = self.server.sec.verify_signature(decr_text,
# self.server.config.cert_file,
# node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
# node_id=assertion[0].id)
assert valid
def test_encrypted_signed_response_2(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
decr_text_old = copy.deepcopy("%s" % signed_resp)
with raises(DecryptError):
decr_text = self.server.sec.decrypt(
signed_resp,
self.client.config.encryption_keypairs[0]["key_file"],
)
decr_text = self.server.sec.decrypt(signed_resp, self.client.config.encryption_keypairs[1]["key_file"])
assert decr_text != decr_text_old
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
assert resp.assertion[0].signature == None
self.verify_assertion(resp.assertion)
def test_encrypted_signed_response_3(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=True,
encrypt_assertion_self_contained=False,
encrypt_cert_assertion=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(signed_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
valid = self.server.sec.verify_signature(decr_text,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=resp.assertion[0].id)
assert valid
self.verify_assertion(resp.assertion)
assert 'xmlns:encas' not in decr_text
def test_encrypted_signed_response_4(self):
cert_str, cert_key_str = generate_cert()
signed_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=True,
sign_assertion=True,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str,
)
sresponse = response_from_string(signed_resp)
valid = self.server.sec.verify_signature(signed_resp,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:protocol:Response',
node_id=sresponse.id)
assert valid
decr_text = self.server.sec.decrypt(signed_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
valid = self.server.sec.verify_signature(decr_text,
self.server.config.cert_file,
node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
node_id=resp.assertion[0].id)
assert valid
key_fd = make_temp(cert_key_str, decode=False)
decr_text = self.server.sec.decrypt(decr_text, key_fd.name)
resp = samlp.response_from_string(decr_text)
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
assertion = \
extension_elements_to_elements(assertion[0].advice.encrypted_assertion[0].extension_elements,[saml, samlp])
self.verify_assertion(assertion)
#PEFIM never signs assertion in advice
assert assertion[0].signature is None
#valid = self.server.sec.verify_signature(decr_text,
# self.server.config.cert_file,
# node_name='urn:oasis:names:tc:SAML:2.0:assertion:Assertion',
# node_id=assertion[0].id)
assert valid
def test_encrypted_response_1(self):
cert_str_advice, cert_key_str_advice = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
)
_resp = "%s" % _resp
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd = make_temp(cert_key_str_advice, decode=False)
decr_text = self.server.sec.decrypt(_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
self.verify_advice_assertion(resp, decr_text)
def test_encrypted_response_2(self):
cert_str_advice, cert_key_str_advice = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text_1 = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
key_fd = make_temp(cert_key_str_advice, decode=False)
decr_text_2 = self.server.sec.decrypt(decr_text_1, key_fd.name)
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_3(self):
cert_str_assertion, cert_key_str_assertion = generate_cert()
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion=cert_str_assertion
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd = make_temp(cert_key_str_assertion, decode=False)
decr_text = self.server.sec.decrypt(_resp, key_fd.name)
resp = samlp.response_from_string(decr_text)
assert resp.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_encrypted_response_4(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
assert resp.encrypted_assertion[0].extension_elements
assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_encrypted_assertion(assertion, decr_text)
def test_encrypted_response_5(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True
)
_resp = "%s" % _resp
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text)
self.verify_advice_assertion(resp, decr_text)
def test_encrypted_response_6(self):
_server = Server("idp_conf_verify_cert")
cert_str_advice, cert_key_str_advice = generate_cert()
cert_str_assertion, cert_key_str_assertion = generate_cert()
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice=cert_str_advice,
encrypt_cert_assertion=cert_str_assertion
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
key_fd1 = make_temp(cert_key_str_assertion, decode=False)
decr_text_1 = _server.sec.decrypt(_resp, key_fd1.name)
key_fd2 = make_temp(cert_key_str_advice, decode=False)
decr_text_2 = _server.sec.decrypt(decr_text_1, key_fd2.name)
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_7(self):
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True
)
sresponse = response_from_string(_resp)
assert sresponse.signature is None
decr_text_1 = self.server.sec.decrypt(_resp, self.client.config.encryption_keypairs[1]["key_file"])
decr_text_2 = self.server.sec.decrypt(decr_text_1, self.client.config.encryption_keypairs[1]["key_file"])
resp = samlp.response_from_string(decr_text_2)
resp.assertion = extension_elements_to_elements(resp.encrypted_assertion[0].extension_elements, [saml, samlp])
self.verify_advice_assertion(resp, decr_text_2)
def test_encrypted_response_8(self):
try:
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
encrypt_cert_assertion="whatever"
)
assert False, "Must throw an exception"
except EncryptError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
try:
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
)
assert False, "Must throw an exception"
except EncryptError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
try:
_resp = self.server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion="whatever"
)
assert False, "Must throw an exception"
except EncryptError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
_server = Server("idp_conf_verify_cert")
try:
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
encrypt_cert_assertion="whatever"
)
assert False, "Must throw an exception"
except CertificateError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
try:
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True,
encrypt_cert_advice="whatever",
)
assert False, "Must throw an exception"
except CertificateError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
try:
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
encrypt_cert_assertion="whatever"
)
assert False, "Must throw an exception"
except CertificateError as ex:
pass
except Exception as ex:
assert False, "Wrong exception!"
def test_encrypted_response_9(self):
_server = Server("idp_conf_sp_no_encrypt")
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
pefim=True,
)
self.verify_assertion(_resp.assertion.advice.assertion)
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=False,
encrypt_assertion_self_contained=True,
pefim=True
)
self.verify_assertion(_resp.assertion.advice.assertion)
_resp = _server.create_authn_response(
self.ava,
"id12", # in_response_to
"http://lingon.catalogix.se:8087/", # consumer_url
"urn:mace:example.com:saml:roland:sp", # sp_entity_id
name_id=self.name_id,
sign_response=False,
sign_assertion=False,
encrypt_assertion=True,
encrypt_assertion_self_contained=True,
encrypted_advice_attributes=False,
)
self.verify_assertion([_resp.assertion])
def test_slo_http_post(self):
soon = time_util.in_a_while(days=1)
sinfo = {
"name_id": nid,
"issuer": "urn:mace:example.com:saml:roland:idp",
"not_on_or_after": soon,
"user": {
"givenName": "Leo",
"sn": "Laport",
}
}
self.client.users.add_information_about_person(sinfo)
req_id, logout_request = self.client.create_logout_request(
destination="http://localhost:8088/slop", name_id=nid,
issuer_entity_id="urn:mace:example.com:saml:roland:idp",
reason="I'm tired of this")
intermed = base64.b64encode(str(logout_request).encode('utf-8'))
#saml_soap = make_soap_enveloped_saml_thingy(logout_request)
request = self.server.parse_logout_request(intermed, BINDING_HTTP_POST)
assert request
def test_slo_soap(self):
soon = time_util.in_a_while(days=1)
sinfo = {
"name_id": nid,
"issuer": "urn:mace:example.com:saml:roland:idp",
"not_on_or_after": soon,
"user": {
"givenName": "Leo",
"sn": "Laport",
}
}
sp = client.Saml2Client(config_file="server_conf")
sp.users.add_information_about_person(sinfo)
req_id, logout_request = sp.create_logout_request(
name_id=nid, destination="http://localhost:8088/slo",
issuer_entity_id="urn:mace:example.com:saml:roland:idp",
reason="I'm tired of this")
#_ = s_utils.deflate_and_base64_encode("%s" % (logout_request,))
saml_soap = make_soap_enveloped_saml_thingy(logout_request)
self.server.ident.close()
with closing(Server("idp_soap_conf")) as idp:
request = idp.parse_logout_request(saml_soap)
idp.ident.close()
assert request
# ------------------------------------------------------------------------
IDENTITY = {"eduPersonAffiliation": ["staff", "member"],
"sn": ["Jeter"], "givenName": ["Derek"],
"mail": ["<EMAIL>"], "title": "The man"}
class TestServer2():
def setup_class(self):
self.server = Server("restrictive_idp_conf")
def teardown_class(self):
self.server.close()
def test_do_attribute_reponse(self):
aa_policy = self.server.config.getattr("policy", "idp")
print(aa_policy.__dict__)
response = self.server.create_attribute_response(
IDENTITY.copy(), "aaa", "http://example.com/sp/",
"http://www.example.com/roland/sp")
assert response is not None
assert response.destination == "http://example.com/sp/"
assert response.in_response_to == "aaa"
assert response.version == "2.0"
assert response.issuer.text == "urn:mace:example.com:saml:roland:idpr"
assert response.status.status_code.value == samlp.STATUS_SUCCESS
assert response.assertion
assertion = response.assertion
assert assertion.version == "2.0"
subject = assertion.subject
#assert subject.name_id.format == saml.NAMEID_FORMAT_TRANSIENT
assert subject.subject_confirmation
subject_conf = subject.subject_confirmation[0]
assert subject_conf.subject_confirmation_data.in_response_to == "aaa"
def _logout_request(conf_file):
conf = config.SPConfig()
conf.load_file(conf_file)
sp = client.Saml2Client(conf)
soon = time_util.in_a_while(days=1)
sinfo = {
"name_id": nid,
"issuer": "urn:mace:example.com:saml:roland:idp",
"not_on_or_after": soon,
"user": {
"givenName": "Leo",
"sn": "Laport",
}
}
sp.users.add_information_about_person(sinfo)
return sp.create_logout_request(
name_id=nid,
destination="http://localhost:8088/slo",
issuer_entity_id="urn:mace:example.com:saml:roland:idp",
reason="I'm tired of this")
class TestServerLogout():
def test_1(self):
with closing(Server("idp_slo_redirect_conf")) as server:
req_id, request = _logout_request("sp_slo_redirect_conf")
print(request)
bindings = [BINDING_HTTP_REDIRECT]
response = server.create_logout_response(request, bindings)
binding, destination = server.pick_binding(
"single_logout_service", bindings, "spsso", request
)
http_args = server.apply_binding(
binding, "%s" % response, destination, "relay_state", response=True
)
assert len(http_args) == 5
assert http_args["headers"][0][0] == "Location"
assert http_args["data"] == []
assert http_args["status"] == 303
assert http_args['url'] == 'http://lingon.catalogix.se:8087/sloresp'
def test_2(self):
with closing(Server("idp_slo_redirect_conf")) as server:
req_id, request = _logout_request("sp_slo_redirect_conf")
print(request)
bindings = [BINDING_HTTP_POST]
response = server.create_logout_response(request, bindings)
binding, destination = server.pick_binding(
"single_logout_service", bindings, "spsso", request
)
http_args = server.apply_binding(
binding, "%s" % response, destination, "relay_state", response=True
)
assert len(http_args) == 5
assert len(http_args["data"]) > 0
assert http_args["method"] == "POST"
assert http_args['url'] == 'http://lingon.catalogix.se:8087/slo'
assert http_args['status'] == 200
if __name__ == "__main__":
ts = TestServer1()
ts.setup_class()
ts.test_encrypted_signed_response_1()
|
nbterm/format.py | vrthra-forks/nbterm | 568 | 12611817 | import json
from pathlib import Path
from typing import Optional
from .cell import Cell
class Format:
nb_path: Path
save_path: Optional[Path]
def read_nb(self) -> None:
with open(self.nb_path) as f:
self.json = json.load(f)
self.set_language() # type: ignore
self.cells = [
Cell(self, cell_json=cell_json) for cell_json in self.json["cells"]
]
del self.json["cells"]
def save(self, path: Optional[Path] = None) -> None:
self.dirty = False
path = path or self.save_path or self.nb_path
nb_json = {"cells": [cell.json for cell in self.cells]}
nb_json.update(self.json)
with open(path, "wt") as f:
json.dump(nb_json, f, indent=1)
f.write("\n")
def create_nb(self) -> None:
self.json = {
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3",
},
"language_info": {
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"version": "3.9.2",
},
},
"nbformat": 4,
"nbformat_minor": 4,
}
self.set_language() # type: ignore
self.cells = [Cell(self)]
|
fairseq/data/noising.py | kayoyin/DialogueMT | 651 | 12611831 | <filename>fairseq/data/noising.py
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from fairseq.data import data_utils
class WordNoising(object):
"""Generate a noisy version of a sentence, without changing words themselves."""
def __init__(self, dictionary, bpe_cont_marker="@@", bpe_end_marker=None):
self.dictionary = dictionary
self.bpe_end = None
if bpe_cont_marker:
self.bpe_end = np.array([
not self.dictionary[i].endswith(bpe_cont_marker)
for i in range(len(self.dictionary))
])
elif bpe_end_marker:
self.bpe_end = np.array([
self.dictionary[i].endswith(bpe_end_marker)
for i in range(len(self.dictionary))
])
self.get_word_idx = (
self._get_bpe_word_idx
if self.bpe_end is not None
else self._get_token_idx
)
def noising(self, x, lengths, noising_prob=0.0):
raise NotImplementedError()
def _get_bpe_word_idx(self, x):
"""
Given a list of BPE tokens, for every index in the tokens list,
return the index of the word grouping that it belongs to.
For example, for input x corresponding to ["how", "are", "y@@", "ou"],
return [[0], [1], [2], [2]].
"""
# x: (T x B)
bpe_end = self.bpe_end[x]
if (x.size(0) == 1 and x.size(1) == 1):
# Special case when we only have one word in x. If x = [[N]],
# bpe_end is a scalar (bool) instead of a 2-dim array of bools,
# which makes the sum operation below fail.
return np.array([[0]])
# do a reduce front sum to generate word ids
word_idx = bpe_end[::-1].cumsum(0)[::-1]
word_idx = word_idx.max(0)[None, :] - word_idx
return word_idx
def _get_token_idx(self, x):
"""
This is to extend noising functions to be able to apply to non-bpe
tokens, e.g. word or characters.
"""
x = torch.t(x)
word_idx = np.array([range(len(x_i)) for x_i in x])
return np.transpose(word_idx)
class WordDropout(WordNoising):
"""Randomly drop input words. If not passing blank_idx (default is None),
then dropped words will be removed. Otherwise, it will be replaced by the
blank_idx."""
def __init__(self, dictionary, default_dropout_prob=0.1, bpe_cont_marker="@@", bpe_end_marker=None):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_dropout_prob = default_dropout_prob
def noising(self, x, lengths, dropout_prob=None, blank_idx=None):
if dropout_prob is None:
dropout_prob = self.default_dropout_prob
# x: (T x B), lengths: B
if dropout_prob == 0:
return x, lengths
assert 0 < dropout_prob < 1
# be sure to drop entire words
word_idx = self.get_word_idx(x)
sentences = []
modified_lengths = []
for i in range(lengths.size(0)):
# Since dropout probabilities need to apply over non-pad tokens,
# it is not trivial to generate the keep mask without consider
# input lengths; otherwise, this could be done outside the loop
# We want to drop whole words based on word_idx grouping
num_words = max(word_idx[:, i]) + 1
# ith example: [x0, x1, ..., eos, pad, ..., pad]
# We should only generate keep probs for non-EOS tokens. Thus if the
# input sentence ends in EOS, the last word idx is not included in
# the dropout mask generation and we append True to always keep EOS.
# Otherwise, just generate the dropout mask for all word idx
# positions.
has_eos = x[lengths[i] - 1, i] == self.dictionary.eos()
if has_eos: # has eos?
keep = np.random.rand(num_words - 1) >= dropout_prob
keep = np.append(keep, [True]) # keep EOS symbol
else:
keep = np.random.rand(num_words) >= dropout_prob
words = x[:lengths[i], i].tolist()
# TODO: speed up the following loop
# drop words from the input according to keep
new_s = [
w if keep[word_idx[j, i]] else blank_idx
for j, w in enumerate(words)
]
new_s = [w for w in new_s if w is not None]
# we need to have at least one word in the sentence (more than the
# start / end sentence symbols)
if len(new_s) <= 1:
# insert at beginning in case the only token left is EOS
# EOS should be at end of list.
new_s.insert(0, words[np.random.randint(0, len(words))])
assert len(new_s) >= 1 and (
not has_eos # Either don't have EOS at end or last token is EOS
or (len(new_s) >= 2 and new_s[-1] == self.dictionary.eos())
), "New sentence is invalid."
sentences.append(new_s)
modified_lengths.append(len(new_s))
# re-construct input
modified_lengths = torch.LongTensor(modified_lengths)
modified_x = torch.LongTensor(
modified_lengths.max(),
modified_lengths.size(0)
).fill_(self.dictionary.pad())
for i in range(modified_lengths.size(0)):
modified_x[:modified_lengths[i], i].copy_(torch.LongTensor(sentences[i]))
return modified_x, modified_lengths
class WordShuffle(WordNoising):
"""Shuffle words by no more than k positions."""
def __init__(self, dictionary, default_max_shuffle_distance=3, bpe_cont_marker="@@", bpe_end_marker=None):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_max_shuffle_distance = 3
def noising(self, x, lengths, max_shuffle_distance=None):
if max_shuffle_distance is None:
max_shuffle_distance = self.default_max_shuffle_distance
# x: (T x B), lengths: B
if max_shuffle_distance == 0:
return x, lengths
# max_shuffle_distance < 1 will return the same sequence
assert max_shuffle_distance > 1
# define noise word scores
noise = np.random.uniform(
0,
max_shuffle_distance,
size=(x.size(0), x.size(1)),
)
noise[0] = -1 # do not move start sentence symbol
# be sure to shuffle entire words
word_idx = self.get_word_idx(x)
x2 = x.clone()
for i in range(lengths.size(0)):
length_no_eos = lengths[i]
if x[lengths[i] - 1, i] == self.dictionary.eos():
length_no_eos = lengths[i] - 1
# generate a random permutation
scores = word_idx[:length_no_eos, i] + noise[word_idx[:length_no_eos, i], i]
# ensure no reordering inside a word
scores += 1e-6 * np.arange(length_no_eos.item())
permutation = scores.argsort()
# shuffle words
x2[:length_no_eos, i].copy_(
x2[:length_no_eos, i][torch.from_numpy(permutation)]
)
return x2, lengths
class UnsupervisedMTNoising(WordNoising):
"""
Implements the default configuration for noising in UnsupervisedMT
(github.com/facebookresearch/UnsupervisedMT)
"""
def __init__(
self,
dictionary,
max_word_shuffle_distance,
word_dropout_prob,
word_blanking_prob,
bpe_cont_marker="@@",
bpe_end_marker=None,
):
super().__init__(dictionary)
self.max_word_shuffle_distance = max_word_shuffle_distance
self.word_dropout_prob = word_dropout_prob
self.word_blanking_prob = word_blanking_prob
self.word_dropout = WordDropout(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
self.word_shuffle = WordShuffle(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
def noising(self, x, lengths):
# 1. Word Shuffle
noisy_src_tokens, noisy_src_lengths = self.word_shuffle.noising(
x=x,
lengths=lengths,
max_shuffle_distance=self.max_word_shuffle_distance,
)
# 2. Word Dropout
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_dropout_prob,
)
# 3. Word Blanking
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_blanking_prob,
blank_idx=self.dictionary.unk(),
)
return noisy_src_tokens
class NoisingDataset(torch.utils.data.Dataset):
def __init__(
self,
src_dataset,
src_dict,
seed,
noiser=None,
noising_class=UnsupervisedMTNoising,
**kwargs
):
"""
Wrap a :class:`~torch.utils.data.Dataset` and apply noise to the
samples based on the supplied noising configuration.
Args:
src_dataset (~torch.utils.data.Dataset): dataset to wrap.
to build self.src_dataset --
a LanguagePairDataset with src dataset as the source dataset and
None as the target dataset. Should NOT have padding so that
src_lengths are accurately calculated by language_pair_dataset
collate function.
We use language_pair_dataset here to encapsulate the tgt_dataset
so we can re-use the LanguagePairDataset collater to format the
batches in the structure that SequenceGenerator expects.
src_dict (~fairseq.data.Dictionary): source dictionary
seed (int): seed to use when generating random noise
noiser (WordNoising): a pre-initialized :class:`WordNoising`
instance. If this is None, a new instance will be created using
*noising_class* and *kwargs*.
noising_class (class, optional): class to use to initialize a
default :class:`WordNoising` instance.
kwargs (dict, optional): arguments to initialize the default
:class:`WordNoising` instance given by *noiser*.
"""
self.src_dataset = src_dataset
self.src_dict = src_dict
self.seed = seed
self.noiser = noiser if noiser is not None else noising_class(
dictionary=src_dict, **kwargs,
)
def __getitem__(self, index):
"""
Returns a single noisy sample. Multiple samples are fed to the collater
create a noising dataset batch.
"""
src_tokens = self.src_dataset[index]
src_lengths = torch.LongTensor([len(src_tokens)])
src_tokens = src_tokens.unsqueeze(0)
# Transpose src tokens to fit expected shape of x in noising function
# (batch size, sequence length) -> (sequence length, batch size)
src_tokens_t = torch.t(src_tokens)
with data_utils.numpy_seed(self.seed + index):
noisy_src_tokens = self.noiser.noising(src_tokens_t, src_lengths)
# Transpose back to expected src_tokens format
# (sequence length, 1) -> (1, sequence length)
noisy_src_tokens = torch.t(noisy_src_tokens)
return noisy_src_tokens[0]
def __len__(self):
"""
The length of the noising dataset is the length of src.
"""
return len(self.src_dataset)
@property
def supports_prefetch(self):
return self.src_dataset.supports_prefetch
def prefetch(self, indices):
if self.src_dataset.supports_prefetch:
self.src_dataset.prefetch(indices)
|
blueoil/converter/core/data_types.py | msakai/blueoil | 248 | 12611850 | <filename>blueoil/converter/core/data_types.py
# -*- coding: utf-8 -*-
# Copyright 2018 The Blueoil Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from typing import List
import numpy as np
def quantized_packed_type(t):
return f"QuantizedPacked<{t}>"
class DataType(object):
def __str__(self) -> str:
return type(self).__name__
@classmethod
def pytype(cls):
raise NotImplementedError
@classmethod
def cpptype(cls):
raise NotImplementedError
@classmethod
def nptype(cls):
raise NotImplementedError
@classmethod
def name(cls):
return cls.__name__
def __eq__(self, other):
return type(self).__name__ == type(other).__name__
class Primitive(DataType):
@classmethod
def is_primitive(cls):
True
class Special(DataType):
@classmethod
def is_primitive(cls):
False
class Int(Special):
@classmethod
def pytype(cls):
return int
@classmethod
def cpptype(cls):
return 'int'
class UInt(Special):
@classmethod
def cpptype(cls):
return 'unsigned'
class Int8(Primitive):
@classmethod
def cpptype(cls):
return 'int8_t'
@classmethod
def nptype(cls):
return np.int8
class Uint8(Primitive):
@classmethod
def cpptype(cls):
return 'uint8_t'
@classmethod
def nptype(cls):
return np.uint8
class Int16(Primitive):
@classmethod
def cpptype(cls):
return 'int16_t'
@classmethod
def nptype(cls):
return np.int16
class Uint16(Primitive):
@classmethod
def cpptype(cls):
return 'uint16_t'
@classmethod
def nptype(cls):
return np.uint16
class Int32(Primitive):
@classmethod
def cpptype(cls):
return 'int32_t'
@classmethod
def nptype(cls):
return np.int32
class Uint32(Primitive):
@classmethod
def cpptype(cls):
return 'uint32_t'
@classmethod
def nptype(cls):
return np.uint32
class PackedUint32(Primitive):
@classmethod
def cpptype(cls):
return 'QuantizedPacked<uint32_t>'
@classmethod
def nptype(cls):
return np.uint32
class Int64(Primitive):
@classmethod
def cpptype(cls):
return 'int64_t'
@classmethod
def nptype(cls):
return np.int64
class Uint64(Primitive):
@classmethod
def cpptype(cls):
return 'uint64_t'
@classmethod
def nptype(cls):
return np.uint64
class PackedUint64(Primitive):
@classmethod
def cpptype(cls):
return 'QuantizedPacked<uint64_t>'
@classmethod
def nptype(cls):
return np.uint64
class Float(Special, float):
@classmethod
def pytype(cls):
return float
@classmethod
def cpptype(cls):
return 'float'
class Float32(Primitive, float):
@classmethod
def cpptype(cls):
return 'float'
@classmethod
def nptype(cls):
return np.float32
class Float64(Primitive, float):
@classmethod
def cpptype(cls):
return 'double'
@classmethod
def nptype(cls):
return np.float64
class String(DataType):
@classmethod
def cpptype(cls):
return 'std::string'
class Void(DataType):
@classmethod
def pytype(cls):
return None
@classmethod
def cpptype(cls):
return 'void'
class Any(DataType):
@classmethod
def cpptype(cls):
return 'std::any'
class Bool(DataType):
@classmethod
def cpptype(cls):
return 'bool'
@classmethod
def nptype(cls):
return np.bool_
class Size(DataType):
@classmethod
def cpptype(cls):
return 'size_t'
class Shape(DataType, List[Size]):
@classmethod
def cpptype(cls):
return 'shape_t'
class Vec(DataType, List[Primitive]):
@classmethod
def cpptype(cls):
return 'vec_t'
class QUANTIZED_NOT_PACKED(Primitive):
@classmethod
def cpptype(cls):
return 'QUANTIZED_NOT_PACKED'
@classmethod
def nptype(cls):
return np.int8
class QUANTIZED_PACKED(Primitive):
@classmethod
def cpptype(cls):
return 'QUANTIZED_PACKED'
@classmethod
def nptype(cls):
return np.uint32
class QUANTIZED_PACKED_KERNEL(Primitive):
@classmethod
def cpptype(cls):
return 'QUANTIZED_PACKED_KERNEL'
@classmethod
def nptype(cls):
return np.uint32
|
hpccm/templates/sed.py | robertmaynard/hpc-container-maker | 340 | 12611878 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=invalid-name, too-few-public-methods
"""sed template"""
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
from six.moves import shlex_quote
import logging # pylint: disable=unused-import
import hpccm.base_object
class sed(hpccm.base_object):
"""sed template"""
def __init__(self, **kwargs):
"""Initialize sed template"""
super(sed, self).__init__(**kwargs)
self.sed_opts = kwargs.get('opts', [])
def sed_step(self, file=None, in_place=True, patterns=[]):
"""Generate sed command line string"""
if not file:
logging.error('file is not defined')
return ''
if not patterns:
logging.error('patterns is not defined')
return ''
# Copy so not to modify the member variable
opts = list(self.sed_opts)
if in_place:
opts.append('-i')
opt_string = ' '.join(opts)
quoted_patterns = ['-e {}'.format(shlex_quote(patterns[0]))]
quoted_patterns.extend(' -e {}'.format(shlex_quote(x)) for x in patterns[1:])
quoted_pattern_string = ' \\\n'.join(quoted_patterns)
return 'sed {0} {1} {2}'.format(opt_string, quoted_pattern_string, file)
|
tests/daemon/unit/test_dockerize.py | vishalbelsare/jina | 15,179 | 12611914 | import platform
from daemon.dockerize import Dockerizer
import pytest
@pytest.mark.parametrize(
'value, expected',
(['Linux', '//var/run/docker.sock'], ['Darwin', '/var/run/docker.sock']),
)
def test_sock(value, expected, monkeypatch):
monkeypatch.setattr(platform, 'system', lambda: value)
assert Dockerizer.dockersock == expected
|
pex/pex_warnings.py | ShellAddicted/pex | 2,160 | 12611938 | <reponame>ShellAddicted/pex<filename>pex/pex_warnings.py<gh_stars>1000+
# coding=utf-8
# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import absolute_import
import warnings
from pex.typing import TYPE_CHECKING
if TYPE_CHECKING:
from pex.pex_info import PexInfo
from pex.variables import Variables
from typing import Optional
class PEXWarning(Warning):
"""Indicates a warning from PEX about suspect buildtime or runtime configuration."""
def configure_warnings(
env, # type: Variables
pex_info=None, # type: Optional[PexInfo]
):
# type: (...) -> None
if env.PEX_VERBOSE > 0:
emit_warnings = True
elif env.PEX_EMIT_WARNINGS is not None:
emit_warnings = env.PEX_EMIT_WARNINGS
elif pex_info:
emit_warnings = pex_info.emit_warnings
else:
emit_warnings = True
action = "default" if emit_warnings else "ignore"
warnings.filterwarnings(action, category=PEXWarning)
def warn(message):
# type: (str) -> None
warnings.warn(message, category=PEXWarning, stacklevel=2)
|
tests/unit/modules/nxos/nxos_n93k.py | waynegemmell/salt | 9,425 | 12611944 | <reponame>waynegemmell/salt
"""
:codeauthor: <NAME> <<EMAIL>>
"""
# Copyright (c) 2018 Cisco and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from tests.unit.modules.nxos.nxos_platform import NXOSPlatform
class N93KPlatform(NXOSPlatform):
"""Cisco Systems N93K Platform Unit Test Object"""
chassis = "Nexus9000 C9396PX Chassis"
# Captured output from: show install all nxos <image>
show_install_all_impact = """
Installer will perform impact only check. Please wait.
[####################] 100% -- SUCCESS
Verifying image type.
[####################] 100% -- SUCCESS
Preparing "nxos" version info using image bootflash:$IMAGE.
[####################] 100% -- SUCCESS
Preparing "bios" version info using image bootflash:$IMAGE.
[####################] 100% -- SUCCESS
Performing module support checks.
[####################] 100% -- SUCCESS
Notifying services about system upgrade.
[####################] 100% -- SUCCESS
Compatibility check is done:
Module bootable Impact Install-type Reason
------ -------- -------------- ------------ ------
1 yes disruptive reset default upgrade is not hitless
Images will be upgraded according to following table:
Module Image Running-Version(pri:alt) New-Version Upg-Required
------ ---------- ---------------------------------------- -------------------- ------------
1 nxos $CVER $NVER $REQ
1 bios v07.64(05/16/2018):v07.06(03/02/2014) v07.64(05/16/2018) no
"""
# Captured output from: install all nxos <image>
install_all_disruptive_success = """
Installer will perform compatibility check first. Please wait.
Installer is forced disruptive
Verifying image bootflash:/$IMAGE for boot variable "nxos".
[####################] 100% -- SUCCESS
Verifying image type.
[####################] 100% -- SUCCESS
Preparing "nxos" version info using image bootflash:/$IMAGE.
[####################] 100% -- SUCCESS
Preparing "bios" version info using image bootflash:/$IMAGE.
[####################] 100% -- SUCCESS
Performing module support checks.
[####################] 100% -- SUCCESS
Notifying services about system upgrade.
[####################] 100% -- SUCCESS
Compatibility check is done:
Module bootable Impact Install-type Reason
------ -------- -------------- ------------ ------
1 yes disruptive reset default upgrade is not hitless
Images will be upgraded according to following table:
Module Image Running-Version(pri:alt) New-Version Upg-Required
------ ---------- ---------------------------------------- -------------------- ------------
1 nxos $CVER $NVER $REQ
1 bios v07.64(05/16/2018):v07.06(03/02/2014) v07.64(05/16/2018) no
Install is in progress, please wait.
Performing runtime checks.
[####################] 100% -- SUCCESS
Setting boot variables.
[####################] 100% -- SUCCESS
Performing configuration copy.
[####################] 100% -- SUCCESS
Module 1: Refreshing compact flash and upgrading bios/loader/bootrom.
Warning: please do not remove or power off the module at this time.
[####################] 100% -- SUCCESS
Converting startup config.
[####################] 100% -- SUCCESS
Finishing the upgrade, switch will reboot in 10 seconds.
"""
|
softdelete/admin/forms.py | RedMoon32/django-softdelete | 242 | 12611961 | from django.forms import *
from softdelete.models import *
from softdelete.forms import *
import logging
class SoftDeleteObjectAdminForm(ModelForm):
deleted = BooleanField(required=False)
class Meta:
model = SoftDeleteObject
exclude = ('deleted_at',)
def __init__(self, *args, **kwargs):
super(SoftDeleteObjectAdminForm, self).__init__(*args, **kwargs)
instance = kwargs.get('instance')
if instance:
self.initial['deleted'] = instance.deleted
def clean(self, *args, **kwargs):
cleaned_data = super(SoftDeleteObjectAdminForm, self).clean(*args, **kwargs)
if 'undelete' in self.data:
self.instance.deleted = False
cleaned_data['deleted'] = False
return cleaned_data
def save(self, commit=True, *args, **kwargs):
model = super(SoftDeleteObjectAdminForm, self).save(commit=False,
*args, **kwargs)
model.deleted = self.cleaned_data['deleted']
if commit:
model.save()
return model
class ChangeSetAdminForm(ChangeSetForm):
pass
class SoftDeleteRecordAdminForm(ModelForm):
class Meta:
model = SoftDeleteRecord
readonly_fields = ('created', )
exclude = ('content_type', 'object_id', 'changeset')
|
nautobot/circuits/api/views.py | psmware-ltd/nautobot | 384 | 12611970 | <gh_stars>100-1000
from django.db.models import Prefetch
from rest_framework.routers import APIRootView
from nautobot.circuits import filters
from nautobot.circuits.models import Provider, CircuitTermination, CircuitType, Circuit
from nautobot.core.api.views import ModelViewSet
from nautobot.dcim.api.views import PathEndpointMixin
from nautobot.extras.api.views import CustomFieldModelViewSet, StatusViewSetMixin
from nautobot.utilities.utils import count_related
from . import serializers
class CircuitsRootView(APIRootView):
"""
Circuits API root view
"""
def get_view_name(self):
return "Circuits"
#
# Providers
#
class ProviderViewSet(CustomFieldModelViewSet):
queryset = Provider.objects.prefetch_related("tags").annotate(circuit_count=count_related(Circuit, "provider"))
serializer_class = serializers.ProviderSerializer
filterset_class = filters.ProviderFilterSet
#
# Circuit Types
#
class CircuitTypeViewSet(CustomFieldModelViewSet):
queryset = CircuitType.objects.annotate(circuit_count=count_related(Circuit, "type"))
serializer_class = serializers.CircuitTypeSerializer
filterset_class = filters.CircuitTypeFilterSet
#
# Circuits
#
class CircuitViewSet(StatusViewSetMixin, CustomFieldModelViewSet):
queryset = Circuit.objects.prefetch_related(
Prefetch("terminations", queryset=CircuitTermination.objects.prefetch_related("site")),
"status",
"type",
"tenant",
"provider",
).prefetch_related("tags")
serializer_class = serializers.CircuitSerializer
filterset_class = filters.CircuitFilterSet
#
# Circuit Terminations
#
class CircuitTerminationViewSet(PathEndpointMixin, ModelViewSet):
queryset = CircuitTermination.objects.prefetch_related("circuit", "site", "_path__destination", "cable")
serializer_class = serializers.CircuitTerminationSerializer
filterset_class = filters.CircuitTerminationFilterSet
brief_prefetch_fields = ["circuit"]
|
client/deploy_hdfs.py | jzmq/minos | 365 | 12611976 | <filename>client/deploy_hdfs.py
import deploy_utils
import parallel_deploy
import service_config
import subprocess
import sys
import time
from log import Log
ALL_JOBS = ["journalnode", "zkfc", "namenode", "datanode"]
SHELL_COMMAND_INFO = {
"dfs": ("org.apache.hadoop.fs.FsShell",
"run a filesystem command on the file systems supported in Hadoop"),
"dfsadmin": ("org.apache.hadoop.hdfs.tools.DFSAdmin",
"run a DFS admin client"),
"haadmin": ("org.apache.hadoop.hdfs.tools.DFSHAAdmin",
"run a DFS HA admin client"),
"fsck": ("org.apache.hadoop.hdfs.tools.DFSck",
"run a DFS filesystem checking utility"),
"balancer": ("org.apache.hadoop.hdfs.server.balancer.Balancer",
"run a cluster balancing utility"),
"jmxget": ("org.apache.hadoop.hdfs.tools.JMXGet",
"get JMX exported values from NameNode or DataNode"),
"oiv": ("org.apache.hadoop.hdfs.tools.offlineImageViewer.OfflineImageViewer",
"apply the offline fsimage viewer to an fsimage"),
"oev": ("org.apache.hadoop.hdfs.tools.offlineEditsViewer.OfflineEditsViewer",
"apply the offline edits viewer to an edits file"),
"fetchdt": ("org.apache.hadoop.hdfs.tools.DelegationTokenFetcher",
"fetch a delegation token from the NameNode"),
"getconf": ("org.apache.hadoop.hdfs.tools.GetConf",
"get config values from configuration"),
"groups": ("org.apache.hadoop.hdfs.tools.GetGroups",
"get the groups which users belong to"),
}
def generate_metrics_config(args, host, job_name, instance_id=-1):
job = args.hdfs_config.jobs[job_name]
supervisor_client = deploy_utils.get_supervisor_client(host,
"hdfs", args.hdfs_config.cluster.name, job_name, instance_id=instance_id)
ganglia_switch = "# "
if args.hdfs_config.cluster.ganglia_address:
ganglia_switch = ""
config_dict = {
"job_name": job_name,
"period": 10,
"data_dir": supervisor_client.get_log_dir(),
"ganglia_address": args.hdfs_config.cluster.ganglia_address,
"ganglia_switch": ganglia_switch,
}
local_path = "%s/hadoop-metrics2.properties.tmpl" % deploy_utils.get_template_dir()
template = deploy_utils.Template(open(local_path, "r").read())
return template.substitute(config_dict)
def generate_configs(args, host, job_name, instance_id):
core_site_xml = deploy_utils.generate_site_xml(args,
args.hdfs_config.configuration.generated_files["core-site.xml"])
hdfs_site_xml = deploy_utils.generate_site_xml(args,
args.hdfs_config.configuration.generated_files["hdfs-site.xml"])
hadoop_metrics2_properties = generate_metrics_config(args, host, job_name, instance_id)
config_files = {
"core-site.xml": core_site_xml,
"hdfs-site.xml": hdfs_site_xml,
"hadoop-metrics2.properties": hadoop_metrics2_properties,
}
config_files.update(args.hdfs_config.configuration.raw_files)
return config_files
def generate_run_scripts_params(args, host, job_name, host_id, instance_id):
job = args.hdfs_config.jobs[job_name]
supervisor_client = deploy_utils.get_supervisor_client(host,
"hdfs", args.hdfs_config.cluster.name, job_name, instance_id=instance_id)
artifact_and_version = "hadoop-" + args.hdfs_config.cluster.version
jar_dirs = ""
# must include both [dir]/ and [dir]/* as [dir]/* only import all jars under
# this dir but we also need access the webapps under this dir.
for component in ["common", "hdfs"]:
if jar_dirs: jar_dirs += ":"
component_dir = ("$package_dir/share/hadoop/%s" % component)
jar_dirs += "%s/:%s/lib/*:%s/*" % (
component_dir, component_dir, component_dir)
log_level = deploy_utils.get_service_log_level(args, args.hdfs_config)
params = job.get_arguments(args, args.hdfs_config.cluster, args.hdfs_config.jobs,
args.hdfs_config.arguments_dict, job_name, host_id, instance_id)
script_dict = {
"artifact": artifact_and_version,
"job_name": job_name,
"jar_dirs": jar_dirs,
"run_dir": supervisor_client.get_run_dir(),
"params": params,
}
return script_dict
def get_hdfs_service_config(args):
args.hdfs_config = deploy_utils.get_service_config(args)
if not args.hdfs_config.cluster.zk_cluster:
Log.print_critical(
"hdfs cluster must depends on a zookeeper clusters: %s" %
args.hdfs_config.cluster.name)
namenode_hosts = args.hdfs_config.jobs["namenode"].hosts
args.hdfs_config.jobs["zkfc"].hosts = namenode_hosts.copy()
args.skip_gen_config_files = False
def generate_bootstrap_script(args, host, job_name, host_id, instance_id, active):
option = str()
script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)
script_params['ha_status'] = 'standby'
if job_name == "zkfc":
if active:
option = "-formatZK"
script_params['ha_status'] = 'active'
elif job_name == "namenode":
if active:
option = "-format -nonInteractive"
else:
option = "-bootstrapStandby -skipSharedEditsCheck -nonInteractive"
script_params['params'] += " %s" % option
return deploy_utils.create_run_script(
'%s/bootstrap_hdfs.sh.tmpl' % deploy_utils.get_template_dir(),
script_params)
def generate_cleanup_script(args, host, job_name, host_id, instance_id, active):
script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)
script_params['params'] += " -clearZK"
if active:
script_params['ha_status'] = 'active'
else:
script_params['ha_status'] = 'standby'
return deploy_utils.create_run_script(
'%s/cleanup_hdfs.sh.tmpl' % deploy_utils.get_template_dir(),
script_params)
def generate_start_script(args, host, job_name, host_id, instance_id):
script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)
return deploy_utils.create_run_script(
'%s/start.sh.tmpl' % deploy_utils.get_template_dir(),
script_params)
def check_journalnode_all_started(args):
job = args.hdfs_config.jobs["journalnode"]
hosts = job.hosts
for host_id in hosts.iterkeys():
for instance_id in range(hosts[host_id].instance_num):
if not deploy_utils.check_service(hosts[host_id].ip,
service_config.get_base_port(job.base_port, instance_id)):
return False
return True
def get_data_dir_indexes(args, job_name, host, instance_id):
if job_name != "datanode":
return "0"
else:
supervisor_client = deploy_utils.get_supervisor_client(host,
"hdfs", args.hdfs_config.cluster.name, job_name, instance_id=instance_id)
data_dirs = supervisor_client.get_available_data_dirs()
return ",".join([str(i) for i in range(len(data_dirs))])
def install(args):
get_hdfs_service_config(args)
deploy_utils.install_service(args, "hdfs", args.hdfs_config, "hadoop")
def cleanup_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
cleanup_script = str()
if job_name == "zkfc":
cleanup_script = generate_cleanup_script(args, host, job_name, host_id, instance_id, active)
deploy_utils.cleanup_job("hdfs", args.hdfs_config,
host, job_name, instance_id, cleanup_token, cleanup_script)
def cleanup(args):
get_hdfs_service_config(args)
cleanup_token = deploy_utils.confirm_cleanup(args,
"hdfs", args.hdfs_config)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'cleanup', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(cleanup_job, task_list)
def bootstrap_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
if job_name == "namenode" and not active:
hosts = args.hdfs_config.jobs[job_name].hosts
while not deploy_utils.check_service(hosts[0].ip,
args.hdfs_config.jobs["namenode"].base_port):
Log.print_warning("Wait for active namenode starting")
time.sleep(2)
# parse the service_config according to the instance_id
args.hdfs_config.parse_generated_config_files(args, job_name, host_id, instance_id)
data_dir_indexes = get_data_dir_indexes(args, job_name, host, instance_id)
config_files = generate_configs(args, host, job_name, instance_id)
if job_name == "namenode" or job_name == "zkfc":
bootstrap_script = generate_bootstrap_script(args, host, job_name, host_id, instance_id, active)
deploy_utils.bootstrap_job(args, "hadoop", "hdfs", args.hdfs_config,
host, job_name, instance_id, cleanup_token, data_dir_indexes, bootstrap_script,
**config_files)
else:
deploy_utils.bootstrap_job(args, "hadoop", "hdfs", args.hdfs_config,
host, job_name, instance_id, cleanup_token, data_dir_indexes, '', **config_files)
# start job after bootstrapping
args.skip_gen_config_files = True
start_job(args, host, job_name, host_id, instance_id)
def bootstrap(args):
get_hdfs_service_config(args)
cleanup_token = deploy_utils.confirm_bootstrap("hdfs", args.hdfs_config)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
if job_name == "namenode":
while not check_journalnode_all_started(args):
Log.print_warning("Wait for journalnode starting")
time.sleep(2)
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'bootstrap', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(bootstrap_job, task_list)
def start_job(args, host, job_name, host_id, instance_id, is_wait=False):
if is_wait:
deploy_utils.wait_for_job_stopping("hdfs",
args.hdfs_config.cluster.name, job_name, host, instance_id)
# parse the service_config according to the instance_id
args.hdfs_config.parse_generated_config_files(args, job_name, host_id, instance_id)
start_script = generate_start_script(args, host, job_name, host_id, instance_id)
http_url = deploy_utils.get_http_service_uri(host,
args.hdfs_config.jobs[job_name].base_port, instance_id)
config_files = dict()
if not args.skip_gen_config_files:
config_files = generate_configs(args, host, job_name, instance_id)
deploy_utils.start_job(args, "hadoop", "hdfs", args.hdfs_config,
host, job_name, instance_id, start_script, http_url, **config_files)
def start(args):
if not args.skip_confirm:
deploy_utils.confirm_start(args)
get_hdfs_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'start')
parallel_deploy.start_deploy_threads(start_job, task_list)
def stop_job(args, host, job_name, instance_id):
deploy_utils.stop_job("hdfs", args.hdfs_config,
host, job_name, instance_id)
def stop(args):
if not args.skip_confirm:
deploy_utils.confirm_stop(args)
get_hdfs_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
def restart(args):
if not args.skip_confirm:
deploy_utils.confirm_restart(args)
get_hdfs_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'start', is_wait=True)
parallel_deploy.start_deploy_threads(start_job, task_list)
def show_job(args, host, job_name, instance_id):
deploy_utils.show_job("hdfs", args.hdfs_config, host, job_name, instance_id)
def show(args):
get_hdfs_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.hdfs_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'show')
parallel_deploy.start_deploy_threads(show_job, task_list)
def run_shell(args):
get_hdfs_service_config(args)
main_class, options = deploy_utils.parse_shell_command(
args, SHELL_COMMAND_INFO)
if not main_class:
return
# parse the service_config, suppose the instance_id is -1
args.hdfs_config.parse_generated_config_files(args)
core_site_dict = args.hdfs_config.configuration.generated_files["core-site.xml"]
hdfs_site_dict = args.hdfs_config.configuration.generated_files["hdfs-site.xml"]
hadoop_opts = list()
for key, value in core_site_dict.iteritems():
hadoop_opts.append("-D%s%s=%s" % (deploy_utils.HADOOP_PROPERTY_PREFIX,
key, value))
for key, value in hdfs_site_dict.iteritems():
hadoop_opts.append("-D%s%s=%s" % (deploy_utils.HADOOP_PROPERTY_PREFIX,
key, value))
package_root = deploy_utils.get_artifact_package_root(args,
args.hdfs_config.cluster, "hadoop")
lib_root = "%s/share/hadoop" % package_root
class_path = "%s/etc/hadoop" % package_root
for component in ["common", "hdfs"]:
component_dir = "%s/%s" % (lib_root, component)
class_path += ":%s/:%s/*:%s/lib/*" % (component_dir,
component_dir, component_dir)
if deploy_utils.is_security_enabled(args):
boot_class_path = "%s/common/lib/hadoop-security-%s.jar" % (lib_root,
args.hdfs_config.cluster.version)
hadoop_opts.append("-Xbootclasspath/p:%s" % boot_class_path)
hadoop_opts.append("-Dkerberos.instance=hadoop")
hadoop_opts.append(
"-Djava.security.krb5.conf=%s/krb5-hadoop.conf" %
deploy_utils.get_config_dir())
cmd = (["java", "-cp", class_path] + hadoop_opts +
[main_class] + options)
p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stderr)
p.wait()
def generate_client_config(args, artifact, version):
config_path = "%s/%s/%s-%s/etc/hadoop" % (args.package_root,
args.cluster, artifact, version)
deploy_utils.write_file("%s/core-site.xml" % config_path,
deploy_utils.generate_site_xml(args,
args.hdfs_config.configuration.generated_files["core-site.xml"]))
deploy_utils.write_file("%s/hdfs-site.xml" % config_path,
deploy_utils.generate_site_xml(args,
args.hdfs_config.configuration.generated_files["hdfs-site.xml"]))
deploy_utils.write_file("%s/hadoop-metrics2.properties" % config_path,
generate_metrics_config(args, args.hdfs_config.jobs["namenode"].hosts[0].ip,
"namenode"))
deploy_utils.write_file("%s/krb5.conf" % config_path,
args.hdfs_config.configuration.raw_files["krb5.conf"])
update_hadoop_env_sh(args, artifact, version, "HADOOP_OPTS")
def update_hadoop_env_sh(args, artifact, version, opts_name):
config_path = "%s/%s/%s-%s/etc/hadoop" % (args.package_root,
args.cluster, artifact, version)
hadoop_opts = "-Djava.security.krb5.conf=$HADOOP_CONF_DIR/krb5.conf"
deploy_utils.append_to_file("%s/hadoop-env.sh" % config_path,
'export %s="$%s %s"\n' % (opts_name, opts_name, hadoop_opts))
def pack(args):
get_hdfs_service_config(args)
args.hdfs_config.parse_generated_config_files(args)
version = args.hdfs_config.cluster.version
deploy_utils.make_package_dir(args, "hadoop", args.hdfs_config.cluster)
generate_client_config(args, "hadoop", version)
if not args.skip_tarball:
deploy_utils.pack_package(args, "hadoop", args.hdfs_config.cluster.version)
Log.print_success("Pack client utilities for hadoop success!\n")
def rolling_update(args):
if not args.job:
Log.print_critical("You must specify the job name to do rolling update")
get_hdfs_service_config(args)
job_name = args.job[0]
if not args.skip_confirm:
deploy_utils.confirm_action(args, "rolling_update")
Log.print_info("Rolling updating %s" % job_name)
hosts = args.hdfs_config.jobs[job_name].hosts
wait_time = 0
args.task_map = deploy_utils.parse_args_host_and_task(args, hosts)
for host_id in args.task_map.keys() or hosts.iterkeys():
for instance_id in args.task_map.get(host_id) or range(hosts[host_id].instance_num):
instance_id = -1 if not deploy_utils.is_multiple_instances(host_id, hosts) else instance_id
deploy_utils.confirm_rolling_update(host_id, instance_id, wait_time)
stop_job(args, hosts[host_id].ip, job_name, instance_id)
deploy_utils.wait_for_job_stopping("hdfs",
args.hdfs_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
start_job(args, hosts[host_id].ip, job_name, host_id, instance_id)
deploy_utils.wait_for_job_starting("hdfs",
args.hdfs_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
wait_time = args.time_interval
Log.print_success("Rolling updating %s success" % job_name)
if __name__ == '__main__':
test()
|
httpx/_compat.py | jleven/httpx | 8,523 | 12611984 | <reponame>jleven/httpx
"""
The _compat module is used for code which requires branching between different
Python environments. It is excluded from the code coverage checks.
"""
import ssl
import sys
# `contextlib.asynccontextmanager` exists from Python 3.7 onwards.
# For 3.6 we require the `async_generator` package for a backported version.
if sys.version_info >= (3, 7):
from contextlib import asynccontextmanager # type: ignore
else:
from async_generator import asynccontextmanager # type: ignore # noqa
# Brotli support is optional
# The C bindings in `brotli` are recommended for CPython.
# The CFFI bindings in `brotlicffi` are recommended for PyPy and everything else.
try:
import brotlicffi as brotli
except ImportError: # pragma: nocover
try:
import brotli
except ImportError:
brotli = None
if sys.version_info >= (3, 10) or (
sys.version_info >= (3, 7) and ssl.OPENSSL_VERSION_INFO >= (1, 1, 0, 7)
):
def set_minimum_tls_version_1_2(context: ssl.SSLContext) -> None:
# The OP_NO_SSL* and OP_NO_TLS* become deprecated in favor of
# 'SSLContext.minimum_version' from Python 3.7 onwards, however
# this attribute is not available unless the ssl module is compiled
# with OpenSSL 1.1.0g or newer.
# https://docs.python.org/3.10/library/ssl.html#ssl.SSLContext.minimum_version
# https://docs.python.org/3.7/library/ssl.html#ssl.SSLContext.minimum_version
context.minimum_version = ssl.TLSVersion.TLSv1_2
else:
def set_minimum_tls_version_1_2(context: ssl.SSLContext) -> None:
# If 'minimum_version' isn't available, we configure these options with
# the older deprecated variants.
context.options |= ssl.OP_NO_SSLv2
context.options |= ssl.OP_NO_SSLv3
context.options |= ssl.OP_NO_TLSv1
context.options |= ssl.OP_NO_TLSv1_1
|
chapter3/yield_psychologist.py | haru-256/ExpertPython3_Source | 112 | 12611997 | <gh_stars>100-1000
def psychologist():
print('Please tell me your problems')
while True:
answer = (yield)
if answer is not None:
if answer.endswith('?'):
print("Don't ask yourself too much questions")
elif 'good' in answer:
print("Ahh that's good, go on")
elif 'bad' in answer:
print("Don't be so negative")
elif answer in ('q', 'quit'):
print("Goodbye")
yield
return
else:
print("Please continue")
if __name__ == "__main__":
print("Starting psychologist session, type 'q' or 'quit' to end session")
freud = psychologist()
for phrase in freud:
problem = input("> ")
freud.send(problem) |
h2o-docs/src/booklets/v2_2015/source/GBM_Vignette_code_examples/gbm_examplerun.py | ahmedengu/h2o-3 | 6,098 | 12612000 | # Now, train the GBM model:
from h2o.estimators.gbm import H2OGradientBoostingEstimator
# Load the data and prepare for modeling
airlines_hex = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/airlines/allyears2k_headers.zip")
# Generate random numbers and create training, validation, testing splits
r = airlines_hex.runif() # Random UNIForm numbers, one per row
air_train_hex = airlines_hex[r < 0.6]
air_valid_hex = airlines_hex[(r >= 0.6) & (r < 0.9)]
air_test_hex = airlines_hex[r >= 0.9]
myX = ["DayofMonth", "DayOfWeek"]
air_model = H2OGradientBoostingEstimator(
distribution='bernoulli', ntrees=100,
max_depth=4, learn_rate=0.1)
air_model.train(x=myX, y="IsDepDelayed",
training_frame=air_train_hex) |
reverb/dataset_test.py | wookayin/reverb | 569 | 12612002 | # Lint as: python3
# Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for dataset."""
import socket
import threading
import time
from absl.testing import parameterized
import numpy as np
from reverb import client
from reverb import dataset as reverb_dataset
from reverb import errors
from reverb import item_selectors
from reverb import rate_limiters
from reverb import replay_sample
from reverb import server as reverb_server
import tensorflow.compat.v1 as tf
import tree
from tensorflow.python.framework import tensor_spec # pylint:disable=g-direct-tensorflow-import
def make_server():
return reverb_server.Server(
tables=[
reverb_server.Table(
'dist',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
reverb_server.Table(
'dist_queue',
sampler=item_selectors.Fifo(),
remover=item_selectors.Fifo(),
max_size=1000000,
max_times_sampled=1,
rate_limiter=rate_limiters.MinSize(1)),
reverb_server.Table(
'signatured',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1),
signature=tf.TensorSpec(dtype=tf.float32, shape=(None, None))),
reverb_server.Table(
'bounded_spec_signatured',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1),
# Currently only the `shape` and `dtype` of the bounded spec
# is considered during signature check.
# TODO(b/158033101): Check the boundaries as well.
signature=tensor_spec.BoundedTensorSpec(
dtype=tf.float32,
shape=(None, None),
minimum=(0.0, 0.0),
maximum=(10.0, 10.)),
),
],
port=None,
)
class LocalReplayDatasetTest(tf.test.TestCase, parameterized.TestCase):
USE_LOCALHOST = True
@classmethod
def setUpClass(cls):
super().setUpClass()
cls._server = make_server()
if cls.USE_LOCALHOST:
connect_to = 'localhost'
else:
connect_to = 'dns:///{}'.format(socket.gethostname())
cls._client = client.Client(f'{connect_to}:{cls._server.port}')
def setUp(self):
super().setUp()
self._num_prev_samples = {
table: self._get_total_num_samples(table)
for table in ('dist', 'dist_queue', 'signatured',
'bounded_spec_signatured')
}
def tearDown(self):
super().tearDown()
self._client.reset('dist')
self._client.reset('dist_queue')
self._client.reset('signatured')
self._client.reset('bounded_spec_signatured')
@classmethod
def tearDownClass(cls):
super().tearDownClass()
cls._server.stop()
def _populate_replay(self,
sequence_length=100,
max_time_steps=None,
max_items=1000):
max_time_steps = max_time_steps or sequence_length
num_items = 0
with self._client.writer(max_time_steps) as writer:
for i in range(1000):
writer.append([np.zeros((3, 3), dtype=np.float32)])
if i % min(5, sequence_length) == 0 and i >= sequence_length:
writer.create_item(
table='dist', num_timesteps=sequence_length, priority=1)
writer.create_item(
table='dist_queue', num_timesteps=sequence_length, priority=1)
writer.create_item(
table='signatured', num_timesteps=sequence_length, priority=1)
writer.create_item(
table='bounded_spec_signatured',
num_timesteps=sequence_length,
priority=1)
num_items += 1
if num_items >= max_items:
break
def _sample_from(self, dataset, num_samples):
iterator = dataset.make_initializable_iterator()
dataset_item = iterator.get_next()
self.evaluate(iterator.initializer)
return [self.evaluate(dataset_item) for _ in range(num_samples)]
def _get_total_num_samples(self, table: str) -> int:
table_info = self._client.server_info()[table]
return table_info.rate_limiter_info.sample_stats.completed
def _get_num_samples(self, table: str) -> int:
"""Gets the number of samples since the start of the test."""
return self._get_total_num_samples(table) - self._num_prev_samples[table]
@parameterized.named_parameters(
{
'testcase_name': 'default_values',
},
{
'testcase_name': 'num_workers_per_iterator_is_0',
'num_workers_per_iterator': 0,
'want_error': ValueError,
},
{
'testcase_name': 'num_workers_per_iterator_is_1',
'num_workers_per_iterator': 1,
},
{
'testcase_name': 'num_workers_per_iterator_is_minus_1',
'num_workers_per_iterator': -1,
},
{
'testcase_name': 'num_workers_per_iterator_is_minus_2',
'num_workers_per_iterator': -2,
'want_error': ValueError,
},
{
'testcase_name': 'max_samples_per_stream_is_0',
'max_samples_per_stream': 0,
'want_error': ValueError,
},
{
'testcase_name': 'max_samples_per_stream_is_1',
'max_samples_per_stream': 1,
},
{
'testcase_name': 'max_samples_per_stream_is_minus_1',
'max_samples_per_stream': -1,
},
{
'testcase_name': 'max_samples_per_stream_is_minus_2',
'num_workers_per_iterator': -2,
'want_error': ValueError,
},
{
'testcase_name': 'max_in_flight_samples_per_worker_is_0',
'max_in_flight_samples_per_worker': 0,
'want_error': ValueError,
},
{
'testcase_name': 'max_in_flight_samples_per_worker_is_1',
'max_in_flight_samples_per_worker': 1,
},
{
'testcase_name': 'max_in_flight_samples_per_worker_is_minus_1',
'max_in_flight_samples_per_worker': -1,
'want_error': ValueError,
},
)
def test_sampler_parameter_validation(self, **kwargs):
dtypes = (tf.float32,)
shapes = (tf.TensorShape([3, 3]),)
if 'max_in_flight_samples_per_worker' not in kwargs:
kwargs['max_in_flight_samples_per_worker'] = 100
if 'want_error' in kwargs:
error = kwargs.pop('want_error')
with self.assertRaises(error):
reverb_dataset.ReplayDataset(self._client.server_address, 'dist',
dtypes, shapes, **kwargs)
else:
reverb_dataset.ReplayDataset(self._client.server_address, 'dist', dtypes,
shapes, **kwargs)
def test_iterate(self):
self._populate_replay()
dataset = reverb_dataset.ReplayDataset(
tf.constant(self._client.server_address),
table=tf.constant('dist'),
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100)
got = self._sample_from(dataset, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
# A single sample is returned so the key should be a scalar int64.
self.assertIsInstance(sample.info.key, np.uint64)
np.testing.assert_array_equal(sample.data[0],
np.zeros((3, 3), dtype=np.float32))
def test_distribution_strategy(self):
self._populate_replay()
physical_devices = tf.config.list_physical_devices('CPU')
configs = tf.config.experimental.get_virtual_device_configuration(
physical_devices[0])
if configs is None:
virtual_devices = [tf.config.experimental.VirtualDeviceConfiguration()
for _ in range(4)]
tf.config.experimental.set_virtual_device_configuration(
physical_devices[0], virtual_devices)
strategy = tf.distribute.MirroredStrategy(['/cpu:%d' % i for i in range(4)])
def reverb_dataset_fn(i):
tf.print('Creating dataset for replica; index:', i)
return reverb_dataset.ReplayDataset(
self._client.server_address,
table=tf.constant('dist'),
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100).take(2)
def dataset_fn(_):
return tf.data.Dataset.range(4).flat_map(reverb_dataset_fn).take(2 * 4)
ds = strategy.experimental_distribute_datasets_from_function(dataset_fn)
def check_probabilities(_, v):
probability = v.info.probability
self.assertLen(probability.values, 4)
# Don't use any math ops since tensor values seem to contain
# unaligned tensors on some systems; but tf.print doesn't check alignment.
#
# This seems to be caused by a compatibility issue where DistStrat isn't
# well tested when eager mode is disabled. So instead of treating this
# as a true TF bug, we just work around it. We can remove this hack and
# convert it to e.g. tf.assert_greater type check if/when we enable eager
# execution for these tests.
tf.print('Probability values:', probability.values)
def get_next_value(v):
return tf.distribute.get_replica_context().merge_call(
check_probabilities, args=(v,))
@tf.function
def run_strategy(ds_):
i = tf.constant(0)
for v in ds_:
strategy.run(get_next_value, args=(v,))
i += 1
return i
rs = run_strategy(ds)
# Each iteration contains 4 items - one from each replica. We take 8 items
# total, so there should be 2 iterations.
self.assertEqual(2, self.evaluate(rs))
def test_timeout_invalid_arguments(self):
with self.assertRaisesRegex(ValueError, r'must be an integer >= -1'):
reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
rate_limiter_timeout_ms=-2,
max_in_flight_samples_per_worker=100)
def test_timeout(self):
dataset_0s = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist_queue',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
rate_limiter_timeout_ms=50, # Slightly above exactly 0.
max_in_flight_samples_per_worker=100)
dataset_1s = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist_queue',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
rate_limiter_timeout_ms=1000,
max_in_flight_samples_per_worker=100)
dataset_2s = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist_queue',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
rate_limiter_timeout_ms=2000,
max_in_flight_samples_per_worker=100)
start_time = time.time()
with self.assertRaisesWithPredicateMatch(tf.errors.OutOfRangeError,
r'End of sequence'):
self._sample_from(dataset_0s, 1)
duration = time.time() - start_time
self.assertGreaterEqual(duration, 0)
self.assertLess(duration, 5)
start_time = time.time()
with self.assertRaisesWithPredicateMatch(tf.errors.OutOfRangeError,
r'End of sequence'):
self._sample_from(dataset_1s, 1)
duration = time.time() - start_time
self.assertGreaterEqual(duration, 1)
self.assertLess(duration, 10)
start_time = time.time()
with self.assertRaisesWithPredicateMatch(tf.errors.OutOfRangeError,
r'End of sequence'):
self._sample_from(dataset_2s, 1)
duration = time.time() - start_time
self.assertGreaterEqual(duration, 2)
self.assertLess(duration, 10)
# If we insert some data, and the rate limiter doesn't force any waiting,
# then we can ask for a timeout of 0s and still get data back.
iterator = dataset_0s.make_initializable_iterator()
dataset_0s_item = iterator.get_next()
self.evaluate(iterator.initializer)
for _ in range(3):
self._populate_replay(max_items=2)
# Pull two items
for _ in range(2):
self.evaluate(dataset_0s_item)
# Wait for the time it would take a broken sampler to time out
# on next iteration.
time.sleep(0.5)
@parameterized.parameters(['signatured'], ['bounded_spec_signatured'])
def test_inconsistent_signature_size(self, table_name):
self._populate_replay()
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32, tf.float64),
shapes=(tf.TensorShape([3, 3]), tf.TensorShape([])),
max_in_flight_samples_per_worker=100)
with self.assertRaisesWithPredicateMatch(
tf.errors.InvalidArgumentError,
r'Inconsistent number of tensors requested from table \'{}\'. '
r'Requested 6 tensors, but table signature shows 5 tensors.'.format(
table_name)):
self._sample_from(dataset, 10)
@parameterized.parameters(['signatured'], ['bounded_spec_signatured'])
def test_incompatible_signature_dtype(self, table_name):
self._populate_replay()
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.int64,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100)
with self.assertRaisesWithPredicateMatch(
tf.errors.InvalidArgumentError,
r'Requested incompatible tensor at flattened index 4 from table '
r'\'{}\'. Requested \(dtype, shape\): \(int64, \[3,3\]\). '
r'Signature \(dtype, shape\): \(float, \[\?,\?\]\)'.format(table_name)):
self._sample_from(dataset, 10)
dataset_emit_sequences = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.int64,),
shapes=(tf.TensorShape([None, 3, 3]),),
emit_timesteps=False,
max_in_flight_samples_per_worker=100)
with self.assertRaisesWithPredicateMatch(
tf.errors.InvalidArgumentError,
r'Requested incompatible tensor at flattened index 4 from table '
r'\'{}\'. Requested \(dtype, shape\): \(int64, \[3,3\]\). '
r'Signature \(dtype, shape\): \(float, \[\?,\?\]\)'.format(table_name)):
self._sample_from(dataset_emit_sequences, 10)
@parameterized.parameters(['signatured'], ['bounded_spec_signatured'])
def test_incompatible_signature_shape(self, table_name):
self._populate_replay()
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3]),),
max_in_flight_samples_per_worker=100)
with self.assertRaisesWithPredicateMatch(
tf.errors.InvalidArgumentError,
r'Requested incompatible tensor at flattened index 4 from table '
r'\'{}\'. Requested \(dtype, shape\): \(float, \[3\]\). '
r'Signature \(dtype, shape\): \(float, \[\?,\?\]\)'.format(table_name)):
self._sample_from(dataset, 10)
dataset_emit_sequences = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([None, 3]),),
emit_timesteps=False,
max_in_flight_samples_per_worker=100)
with self.assertRaisesWithPredicateMatch(
tf.errors.InvalidArgumentError,
r'Requested incompatible tensor at flattened index 4 from table '
r'\'{}\'. Requested \(dtype, shape\): \(float, \[3\]\). '
r'Signature \(dtype, shape\): \(float, \[\?,\?\]\)'.format(table_name)):
self._sample_from(dataset_emit_sequences, 10)
@parameterized.parameters([1], [3], [10])
def test_incompatible_shape_when_using_sequence_length(self, sequence_length):
with self.assertRaises(ValueError):
reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([sequence_length + 1, 3, 3]),),
emit_timesteps=False,
sequence_length=sequence_length,
max_in_flight_samples_per_worker=100)
def test_incompatible_dataset_shapes_and_types_without_signature(self):
self._populate_replay()
ds_wrong_shape = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([]),),
max_in_flight_samples_per_worker=100)
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
r'Specification has \(dtype, shape\): \(float, \[\]\). '
r'Tensor has \(dtype, shape\): \(float, \[3,3\]\).'):
self._sample_from(ds_wrong_shape, 1)
ds_full_sequences_wrong_shape = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([None]),),
emit_timesteps=False,
max_in_flight_samples_per_worker=100)
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
r'Specification has \(dtype, shape\): \(float, \[\]\). '
r'Tensor has \(dtype, shape\): \(float, \[3,3\]\).'):
self._sample_from(ds_full_sequences_wrong_shape, 1)
@parameterized.parameters(
('dist', 1, 1),
('dist', 1, 3),
('dist', 3, 3),
('dist', 3, 5),
('dist', 10, 10),
('dist', 10, 11),
('signatured', 1, 1),
('signatured', 3, 3),
('signatured', 3, 5),
('signatured', 10, 10),
('bounded_spec_signatured', 1, 1),
('bounded_spec_signatured', 3, 3),
('bounded_spec_signatured', 3, 5),
('bounded_spec_signatured', 10, 10),
)
def test_iterate_with_sequence_length(self, table_name, sequence_length,
max_time_steps):
# Also ensure we get sequence_length-shaped outputs when
# writers' max_time_steps != sequence_length.
self._populate_replay(sequence_length, max_time_steps=max_time_steps)
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([sequence_length, 3, 3]),),
emit_timesteps=False,
sequence_length=sequence_length,
max_in_flight_samples_per_worker=100)
got = self._sample_from(dataset, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
# The keys and data should be batched up by the sequence length.
self.assertEqual(sample.info.key.shape, (sequence_length,))
np.testing.assert_array_equal(
sample.data[0], np.zeros((sequence_length, 3, 3), dtype=np.float32))
@parameterized.parameters(
('dist', 1),
('dist', 3),
('dist', 10),
('signatured', 1),
('signatured', 3),
('signatured', 10),
('bounded_spec_signatured', 1),
('bounded_spec_signatured', 3),
('bounded_spec_signatured', 10),
)
def test_iterate_with_unknown_sequence_length(self, table_name,
sequence_length):
self._populate_replay(sequence_length)
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([None, 3, 3]),),
emit_timesteps=False,
sequence_length=None,
max_in_flight_samples_per_worker=100)
# Check the shape of the items.
iterator = dataset.make_initializable_iterator()
dataset_item = iterator.get_next()
self.assertIsNone(dataset_item.info.key.shape.as_list()[0], None)
self.assertIsNone(dataset_item.data[0].shape.as_list()[0], None)
# Verify that once evaluated, the samples has the expected length.
got = self._sample_from(dataset, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
# The keys and data should be batched up by the sequence length.
self.assertEqual(sample.info.key.shape, (sequence_length,))
np.testing.assert_array_equal(
sample.data[0], np.zeros((sequence_length, 3, 3), dtype=np.float32))
@parameterized.parameters(
('dist', 1, 2),
('dist', 2, 1),
('signatured', 1, 2),
('signatured', 2, 1),
('bounded_spec_signatured', 1, 2),
('bounded_spec_signatured', 2, 1),
)
def test_checks_sequence_length_when_timesteps_emitted(
self, table_name, actual_sequence_length, provided_sequence_length):
self._populate_replay(actual_sequence_length)
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([provided_sequence_length, 3, 3]),),
emit_timesteps=True,
sequence_length=provided_sequence_length,
max_in_flight_samples_per_worker=100)
with self.assertRaises(tf.errors.InvalidArgumentError):
self._sample_from(dataset, 10)
@parameterized.named_parameters(
dict(testcase_name='TableDist', table_name='dist'),
dict(testcase_name='TableSignatured', table_name='signatured'),
dict(
testcase_name='TableBoundedSpecSignatured',
table_name='bounded_spec_signatured'))
def test_iterate_batched(self, table_name):
self._populate_replay()
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table=table_name,
dtypes=(tf.float32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100)
dataset = dataset.batch(2, True)
got = self._sample_from(dataset, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
# The keys should be batched up like the data.
self.assertEqual(sample.info.key.shape, (2,))
np.testing.assert_array_equal(sample.data[0],
np.zeros((2, 3, 3), dtype=np.float32))
def test_iterate_nested_and_batched(self):
with self._client.writer(100) as writer:
for i in range(1000):
writer.append({
'observation': {
'data': np.zeros((3, 3), dtype=np.float32),
'extras': [
np.int64(10),
np.ones([1], dtype=np.int32),
],
},
'reward': np.zeros((10, 10), dtype=np.float32),
})
if i % 5 == 0 and i >= 100:
writer.create_item(
table='dist', num_timesteps=100, priority=1)
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(((tf.float32), (tf.int64, tf.int32)), tf.float32),
shapes=((tf.TensorShape([3, 3]), (tf.TensorShape(None),
tf.TensorShape([1]))),
tf.TensorShape([10, 10])),
max_in_flight_samples_per_worker=100)
dataset = dataset.batch(3)
structure = {
'observation': {
'data':
tf.TensorSpec([3, 3], tf.float32),
'extras': [
tf.TensorSpec([], tf.int64),
tf.TensorSpec([1], tf.int32),
],
},
'reward': tf.TensorSpec([], tf.int64),
}
got = self._sample_from(dataset, 10)
self.assertLen(got, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
transition = tree.unflatten_as(structure, tree.flatten(sample.data))
np.testing.assert_array_equal(transition['observation']['data'],
np.zeros([3, 3, 3], dtype=np.float32))
np.testing.assert_array_equal(transition['observation']['extras'][0],
np.ones([3], dtype=np.int64) * 10)
np.testing.assert_array_equal(transition['observation']['extras'][1],
np.ones([3, 1], dtype=np.int32))
np.testing.assert_array_equal(transition['reward'],
np.zeros([3, 10, 10], dtype=np.float32))
def test_multiple_iterators(self):
with self._client.writer(100) as writer:
for i in range(10):
writer.append([np.ones((81, 81), dtype=np.float32) * i])
writer.create_item(table='dist', num_timesteps=10, priority=1)
trajectory_length = 5
batch_size = 3
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.float32,),
shapes=(tf.TensorShape([81, 81]),),
max_in_flight_samples_per_worker=100)
dataset = dataset.batch(trajectory_length)
iterators = [
dataset.make_initializable_iterator() for _ in range(batch_size)
]
items = tf.stack(
[tf.squeeze(iterator.get_next().data) for iterator in iterators])
with self.session() as session:
session.run([iterator.initializer for iterator in iterators])
got = session.run(items)
self.assertEqual(got.shape, (batch_size, trajectory_length, 81, 81))
want = np.array(
[[np.ones([81, 81]) * i for i in range(trajectory_length)]] *
batch_size)
np.testing.assert_array_equal(got, want)
def test_iterate_over_blobs(self):
for _ in range(10):
self._client.insert((np.ones([3, 3], dtype=np.int32)), {'dist': 1})
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.int32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100)
got = self._sample_from(dataset, 20)
self.assertLen(got, 20)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
self.assertIsInstance(sample.info.key, np.uint64)
self.assertIsInstance(sample.info.probability, np.float64)
np.testing.assert_array_equal(sample.data[0],
np.ones((3, 3), dtype=np.int32))
@parameterized.parameters(1, 3, 7)
def test_respects_max_in_flight_samples_per_worker(
self, max_in_flight_samples_per_worker):
if not self.USE_LOCALHOST:
self.skipTest('TODO(b/190761815): test broken in Nonlocal case')
for _ in range(10):
self._client.insert((np.ones([3, 3], dtype=np.int32)), {'dist': 1})
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.int32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=max_in_flight_samples_per_worker)
iterator = dataset.make_initializable_iterator()
dataset_item = iterator.get_next()
self.evaluate(iterator.initializer)
for _ in range(100):
self.evaluate(dataset_item)
# Check that the buffer is incremented by steps of
# max_in_flight_samples_per_worker.
self.assertEqual(
self._get_num_samples('dist') % max_in_flight_samples_per_worker, 0)
def test_iterate_over_batched_blobs(self):
for _ in range(10):
self._client.insert((np.ones([3, 3], dtype=np.int32)), {'dist': 1})
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=(tf.int32,),
shapes=(tf.TensorShape([3, 3]),),
max_in_flight_samples_per_worker=100)
dataset = dataset.batch(5)
got = self._sample_from(dataset, 20)
self.assertLen(got, 20)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
self.assertEqual(sample.info.key.shape, (5,))
np.testing.assert_array_equal(sample.data[0],
np.ones((5, 3, 3), dtype=np.int32))
def test_converts_spec_lists_into_tuples(self):
for _ in range(10):
data = [
(np.ones([1, 1], dtype=np.int32),),
[
np.ones([3, 3], dtype=np.int8),
(np.ones([2, 2], dtype=np.float64),)
],
]
self._client.insert(data, {'dist': 1})
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=[
(tf.int32,),
[
tf.int8,
(tf.float64,),
],
],
shapes=[
(tf.TensorShape([1, 1]),),
[
tf.TensorShape([3, 3]),
(tf.TensorShape([2, 2]),),
],
],
max_in_flight_samples_per_worker=100)
got = self._sample_from(dataset, 10)
for sample in got:
self.assertIsInstance(sample, replay_sample.ReplaySample)
self.assertIsInstance(sample.info.key, np.uint64)
tree.assert_same_structure(sample.data, (
(None,),
(
None,
(None,),
),
))
def test_session_is_closed_while_op_pending(self):
dataset = reverb_dataset.ReplayDataset(
self._client.server_address,
table='dist',
dtypes=tf.float32,
shapes=tf.TensorShape([]),
max_in_flight_samples_per_worker=100)
iterator = dataset.make_initializable_iterator()
item = iterator.get_next()
def _session_closer(sess, wait_time_secs):
def _fn():
time.sleep(wait_time_secs)
sess.close()
return _fn
with self.session() as sess:
sess.run(iterator.initializer)
thread = threading.Thread(target=_session_closer(sess, 3))
thread.start()
with self.assertRaises(tf.errors.CancelledError):
sess.run(item)
class NonlocalReplayDatasetTest(LocalReplayDatasetTest):
"""Test with non-localhost connection to server."""
USE_LOCALHOST = False
class FromTableSignatureTest(tf.test.TestCase):
def test_table_not_found(self):
server = reverb_server.Server([
reverb_server.Table.queue('table_a', 10),
reverb_server.Table.queue('table_c', 10),
reverb_server.Table.queue('table_b', 10),
])
address = f'localhost:{server.port}'
with self.assertRaisesWithPredicateMatch(
ValueError,
f'Server at {address} does not contain any table named not_found. '
f'Found: table_a, table_b, table_c.'):
reverb_dataset.ReplayDataset.from_table_signature(
address, 'not_found', 100)
def test_server_not_found(self):
with self.assertRaises(errors.DeadlineExceededError):
reverb_dataset.ReplayDataset.from_table_signature(
'localhost:1234', 'not_found', 100, get_signature_timeout_secs=1)
def test_table_does_not_have_signature(self):
server = make_server()
address = f'localhost:{server.port}'
with self.assertRaisesWithPredicateMatch(
ValueError, f'Table dist at {address} does not have a signature.'):
reverb_dataset.ReplayDataset.from_table_signature(
address, 'dist', 100)
def test_sets_dtypes_from_signature(self):
signature = {
'a': {
'b': tf.TensorSpec([3, 3], tf.float32),
'c': tf.TensorSpec([], tf.int64),
},
'x': tf.TensorSpec([None], tf.uint64),
}
server = reverb_server.Server(
[reverb_server.Table.queue('queue', 10, signature=signature)])
dataset = reverb_dataset.ReplayDataset.from_table_signature(
f'localhost:{server.port}', 'queue', 100)
self.assertDictEqual(dataset.element_spec.data, signature)
def test_sets_dtypes_from_bounded_spec_signature(self):
bounded_spec_signature = {
'a': {
'b': tensor_spec.BoundedTensorSpec([3, 3], tf.float32, 0, 3),
'c': tensor_spec.BoundedTensorSpec([], tf.int64, 0, 5),
},
}
server = reverb_server.Server([
reverb_server.Table.queue(
'queue', 10, signature=bounded_spec_signature)
])
dataset = reverb_dataset.ReplayDataset.from_table_signature(
f'localhost:{server.port}', 'queue', 100)
self.assertDictEqual(
dataset.element_spec.data, {
'a': {
'b': tf.TensorSpec([3, 3], tf.float32),
'c': tf.TensorSpec([], tf.int64),
},
})
def test_combines_sequence_length_with_signature_if_not_emit_timestamps(self):
server = reverb_server.Server([
reverb_server.Table.queue(
'queue',
10,
signature={
'a': {
'b': tf.TensorSpec([3, 3], tf.float32),
'c': tf.TensorSpec([], tf.int64),
},
})
])
dataset = reverb_dataset.ReplayDataset.from_table_signature(
f'localhost:{server.port}',
'queue',
100,
emit_timesteps=False,
sequence_length=5)
self.assertDictEqual(
dataset.element_spec.data, {
'a': {
'b': tf.TensorSpec([5, 3, 3], tf.float32),
'c': tf.TensorSpec([5], tf.int64),
},
})
if __name__ == '__main__':
tf.disable_eager_execution()
tf.test.main()
|
files/managers/upload_module.py | wkma/bk-sops | 881 | 12612025 | <filename>files/managers/upload_module.py
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2021 TH<NAME>, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
from .base import Manager
from ..models import UploadModuleFileTag
from ..exceptions import InvalidOperationError
from ..env import BKAPP_FILE_MGR_SOURCE_ACCOUNT
class UploadModuleManager(Manager):
type = "upload_module"
def __init__(self):
super().__init__(None)
def save(self, name, content, shims=None, max_length=None, **kwargs):
tag = UploadModuleFileTag.objects.create(
source_ip=kwargs["source_ip"], file_path=kwargs["file_path"], file_name=name
)
return {"type": "upload_module", "tags": {"tag_id": tag.id}}
def push_files_to_ips(
self, esb_client, bk_biz_id, file_tags, target_path, ips, account, callback_url=None, timeout=None
):
if not all([tag["type"] == "upload_module" for tag in file_tags]):
raise InvalidOperationError("can not do files push operation on different types files")
tag_ids = [tag["tags"]["tag_id"] for tag in file_tags]
tag_models = UploadModuleFileTag.objects.filter(id__in=tag_ids)
file_source = [
{
"files": ["{}/{}".format(tag.file_path, tag.file_name)],
"account": BKAPP_FILE_MGR_SOURCE_ACCOUNT,
"ip_list": [{"bk_cloud_id": 0, "ip": tag.source_ip}],
}
for tag in tag_models
]
job_kwargs = {
"bk_biz_id": bk_biz_id,
"account": account,
"file_target_path": target_path,
"file_source": file_source,
"ip_list": ips,
}
if timeout is not None:
job_kwargs["timeout"] = int(timeout)
if callback_url:
job_kwargs["bk_callback_url"] = callback_url
job_result = esb_client.job.fast_push_file(job_kwargs)
if not job_result["result"]:
return {
"result": job_result["result"],
"message": job_result["message"],
"response": job_result,
"kwargs": job_kwargs,
"job_api": "job.fast_push_file",
}
return {"result": job_result["result"], "data": {"job_id": job_result["data"]["job_instance_id"]}}
def get_push_job_state(self, esb_client, job_id):
raise NotImplementedError()
|
torchsde/_core/adjoint_sde.py | emaballarin/torchsde | 984 | 12612046 | # Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from . import base_sde
from . import misc
from ..settings import NOISE_TYPES, SDE_TYPES
from ..types import Sequence, TensorOrTensors
class AdjointSDE(base_sde.BaseSDE):
def __init__(self,
forward_sde: base_sde.ForwardSDE,
params: TensorOrTensors,
shapes: Sequence[torch.Size]):
# There's a mapping from the noise type of the forward SDE to the noise type of the adjoint.
# Usually, these two aren't the same, e.g. when the forward SDE has additive noise, the adjoint SDE's diffusion
# is a linear function of the adjoint variable, so it is not of additive noise.
sde_type = forward_sde.sde_type
noise_type = {
NOISE_TYPES.general: NOISE_TYPES.general,
NOISE_TYPES.additive: NOISE_TYPES.general,
NOISE_TYPES.scalar: NOISE_TYPES.scalar,
NOISE_TYPES.diagonal: NOISE_TYPES.diagonal,
}.get(forward_sde.noise_type)
super(AdjointSDE, self).__init__(sde_type=sde_type, noise_type=noise_type)
self.forward_sde = forward_sde
self.params = params
self._shapes = shapes
# Register the core functions. This avoids polluting the codebase with if-statements and achieves speed-ups
# by making sure it's a one-time cost. The `sde_type` and `noise_type` of the forward SDE determines the
# registered functions.
self.f = {
SDE_TYPES.ito: {
NOISE_TYPES.diagonal: self.f_corrected_diagonal,
NOISE_TYPES.additive: self.f_uncorrected,
NOISE_TYPES.scalar: self.f_corrected_default,
NOISE_TYPES.general: self.f_corrected_default
}.get(forward_sde.noise_type),
SDE_TYPES.stratonovich: self.f_uncorrected
}.get(forward_sde.sde_type)
self.f_and_g_prod = {
SDE_TYPES.ito: {
NOISE_TYPES.diagonal: self.f_and_g_prod_corrected_diagonal,
NOISE_TYPES.additive: self.f_and_g_prod_uncorrected,
NOISE_TYPES.scalar: self.f_and_g_prod_corrected_default,
NOISE_TYPES.general: self.f_and_g_prod_corrected_default
}.get(forward_sde.noise_type),
SDE_TYPES.stratonovich: self.f_and_g_prod_uncorrected
}.get(forward_sde.sde_type)
self.g_prod_and_gdg_prod = {
NOISE_TYPES.diagonal: self.g_prod_and_gdg_prod_diagonal,
}.get(forward_sde.noise_type, self.g_prod_and_gdg_prod_default)
########################################
# Helper functions #
########################################
def get_state(self, t, y_aug, v=None, extra_states=False):
"""Unpacks y_aug, whilst enforcing the necessary checks so that we can calculate derivatives wrt state."""
# These leaf checks are very important.
# get_state is used where we want to compute:
# ```
# with torch.enable_grad():
# s = some_function(y)
# torch.autograd.grad(s, [y] + params, ...)
# ```
# where `some_function` implicitly depends on `params`.
# However if y has history of its own then in principle it could _also_ depend upon `params`, and this call to
# `grad` will go all the way back to that. To avoid this, we require that every input tensor be a leaf tensor.
#
# This is also the reason for the `y0.detach()` in adjoint.py::_SdeintAdjointMethod.forward. If we don't detach,
# then y0 may have a history and these checks will fail. This is a spurious failure as
# `torch.autograd.Function.forward` has an implicit `torch.no_grad()` guard, i.e. we definitely don't want to
# use its history there.
assert t.is_leaf, "Internal error: please report a bug to torchsde"
assert y_aug.is_leaf, "Internal error: please report a bug to torchsde"
if v is not None:
assert v.is_leaf, "Internal error: please report a bug to torchsde"
requires_grad = torch.is_grad_enabled()
if extra_states:
shapes = self._shapes
else:
shapes = self._shapes[:2]
numel = sum(shape.numel() for shape in shapes)
y, adj_y, *extra_states = misc.flat_to_shape(y_aug.squeeze(0)[:numel], shapes)
# To support the later differentiation wrt y, we set it to require_grad if it doesn't already.
if not y.requires_grad:
y = y.detach().requires_grad_()
return y, adj_y, extra_states, requires_grad
def _f_uncorrected(self, f, y, adj_y, requires_grad):
vjp_y_and_params = misc.vjp(
outputs=f,
inputs=[y] + self.params,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
if not requires_grad:
# We had to build a computational graph to be able to compute the above vjp.
# However, if we don't require_grad then we don't need to backprop through this function, so we should
# delete the computational graph to avoid a memory leak. (Which for example would keep the local
# variable `y` in memory: f->grad_fn->...->AccumulatedGrad->y.)
# Note: `requires_grad` might not be equal to what `torch.is_grad_enabled` returns here.
f = f.detach()
return misc.flatten((-f, *vjp_y_and_params)).unsqueeze(0)
def _f_corrected_default(self, f, g, y, adj_y, requires_grad):
g_columns = [g_column.squeeze(dim=-1) for g_column in g.split(1, dim=-1)]
dg_g_jvp = sum([
misc.jvp(
outputs=g_column,
inputs=y,
grad_inputs=g_column,
allow_unused=True,
create_graph=True
)[0] for g_column in g_columns
])
# Double Stratonovich correction.
f = f - dg_g_jvp
vjp_y_and_params = misc.vjp(
outputs=f,
inputs=[y] + self.params,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
# Convert the adjoint Stratonovich SDE to Itô form.
extra_vjp_y_and_params = []
for g_column in g_columns:
a_dg_vjp, = misc.vjp(
outputs=g_column,
inputs=y,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
extra_vjp_y_and_params_column = misc.vjp(
outputs=g_column,
inputs=[y] + self.params,
grad_outputs=a_dg_vjp,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
extra_vjp_y_and_params.append(extra_vjp_y_and_params_column)
vjp_y_and_params = misc.seq_add(vjp_y_and_params, *extra_vjp_y_and_params)
if not requires_grad:
# See corresponding note in _f_uncorrected.
f = f.detach()
return misc.flatten((-f, *vjp_y_and_params)).unsqueeze(0)
def _f_corrected_diagonal(self, f, g, y, adj_y, requires_grad):
g_dg_vjp, = misc.vjp(
outputs=g,
inputs=y,
grad_outputs=g,
allow_unused=True,
create_graph=True
)
# Double Stratonovich correction.
f = f - g_dg_vjp
vjp_y_and_params = misc.vjp(
outputs=f,
inputs=[y] + self.params,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
# Convert the adjoint Stratonovich SDE to Itô form.
a_dg_vjp, = misc.vjp(
outputs=g,
inputs=y,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
extra_vjp_y_and_params = misc.vjp(
outputs=g,
inputs=[y] + self.params,
grad_outputs=a_dg_vjp,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
vjp_y_and_params = misc.seq_add(vjp_y_and_params, extra_vjp_y_and_params)
if not requires_grad:
# See corresponding note in _f_uncorrected.
f = f.detach()
return misc.flatten((-f, *vjp_y_and_params)).unsqueeze(0)
def _g_prod(self, g_prod, y, adj_y, requires_grad):
vjp_y_and_params = misc.vjp(
outputs=g_prod,
inputs=[y] + self.params,
grad_outputs=adj_y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
if not requires_grad:
# See corresponding note in _f_uncorrected.
g_prod = g_prod.detach()
return misc.flatten((-g_prod, *vjp_y_and_params)).unsqueeze(0)
########################################
# f #
########################################
def f_uncorrected(self, t, y_aug): # For Ito additive and Stratonovich.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f = self.forward_sde.f(-t, y)
return self._f_uncorrected(f, y, adj_y, requires_grad)
def f_corrected_default(self, t, y_aug): # For Ito general/scalar.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f, g = self.forward_sde.f_and_g(-t, y)
return self._f_corrected_default(f, g, y, adj_y, requires_grad)
def f_corrected_diagonal(self, t, y_aug): # For Ito diagonal.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f, g = self.forward_sde.f_and_g(-t, y)
return self._f_corrected_diagonal(f, g, y, adj_y, requires_grad)
########################################
# g #
########################################
def g(self, t, y):
# We don't want to define it, it's super inefficient to compute.
# In theory every part of the code which _could_ call it either does something else, or has some more
# informative error message to tell the user what went wrong.
# This is here as a fallback option.
raise RuntimeError("Adjoint `g` not defined. Please report a bug to torchsde.")
########################################
# f_and_g #
########################################
def f_and_g(self, t, y):
# Like g above, this is inefficient to compute.
raise RuntimeError("Adjoint `f_and_g` not defined. Please report a bug to torchsde.")
########################################
# prod #
########################################
def prod(self, g, v):
# We could define this just fine, but we don't expect to ever be able to compute the input `g`, so we should
# never get here.
raise RuntimeError("Adjoint `prod` not defined. Please report a bug to torchsde.")
########################################
# g_prod #
########################################
def g_prod(self, t, y_aug, v):
y, adj_y, _, requires_grad = self.get_state(t, y_aug, v)
with torch.enable_grad():
g_prod = self.forward_sde.g_prod(-t, y, v)
return self._g_prod(g_prod, y, adj_y, requires_grad)
########################################
# f_and_g_prod #
########################################
def f_and_g_prod_uncorrected(self, t, y_aug, v): # For Ito additive and Stratonovich.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f, g_prod = self.forward_sde.f_and_g_prod(-t, y, v)
f_out = self._f_uncorrected(f, y, adj_y, requires_grad)
g_prod_out = self._g_prod(g_prod, y, adj_y, requires_grad)
return f_out, g_prod_out
def f_and_g_prod_corrected_default(self, t, y_aug, v): # For Ito general/scalar.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f, g = self.forward_sde.f_and_g(-t, y)
g_prod = self.forward_sde.prod(g, v)
f_out = self._f_corrected_default(f, g, y, adj_y, requires_grad)
g_prod_out = self._g_prod(g_prod, y, adj_y, requires_grad)
return f_out, g_prod_out
def f_and_g_prod_corrected_diagonal(self, t, y_aug, v): # For Ito diagonal.
y, adj_y, _, requires_grad = self.get_state(t, y_aug)
with torch.enable_grad():
f, g = self.forward_sde.f_and_g(-t, y)
g_prod = self.forward_sde.prod(g, v)
f_out = self._f_corrected_diagonal(f, g, y, adj_y, requires_grad)
g_prod_out = self._g_prod(g_prod, y, adj_y, requires_grad)
return f_out, g_prod_out
########################################
# gdg_prod #
########################################
def g_prod_and_gdg_prod_default(self, t, y, v1, v2): # For Ito/Stratonovich general/additive/scalar.
raise NotImplementedError
def g_prod_and_gdg_prod_diagonal(self, t, y_aug, v1, v2): # For Ito/Stratonovich diagonal.
y, adj_y, _, requires_grad = self.get_state(t, y_aug, v2)
with torch.enable_grad():
g = self.forward_sde.g(-t, y)
g_prod = self.forward_sde.prod(g, v1)
vg_dg_vjp, = misc.vjp(
outputs=g,
inputs=y,
grad_outputs=v2 * g,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
dgdy, = misc.vjp(
outputs=g.sum(),
inputs=y,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
prod_partials_adj_y_and_params = misc.vjp(
outputs=g,
inputs=[y] + self.params,
grad_outputs=adj_y * v2 * dgdy,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
avg_dg_vjp, = misc.vjp(
outputs=g,
inputs=y,
grad_outputs=(adj_y * v2 * g).detach(),
allow_unused=True,
create_graph=True
)
mixed_partials_adj_y_and_params = misc.vjp(
outputs=avg_dg_vjp.sum(),
inputs=[y] + self.params,
allow_unused=True,
retain_graph=True,
create_graph=requires_grad
)
vjp_y_and_params = misc.seq_sub(prod_partials_adj_y_and_params, mixed_partials_adj_y_and_params)
return self._g_prod(g_prod, y, adj_y, requires_grad), misc.flatten((vg_dg_vjp,
*vjp_y_and_params)).unsqueeze(0)
|
Python3/1288.py | rakhi2001/ecom7 | 854 | 12612056 | __________________________________________________________________________________________________
sample 84 ms submission
class Solution:
def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:
if not intervals: return 0
intervals.sort()
L, R = intervals[0]
for i in range(1, remaining := len(intervals)):
l, r = intervals[i]
if L <= l and R >= r: remaining -= 1
else: L, R = l, r
return remaining
__________________________________________________________________________________________________
sample 88 ms submission
class Solution:
def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:
intervals.sort()
remain_lst = []
cur_left, cur_right = intervals[0]
count = 1
for interval in intervals:
if cur_left == interval[0]:
cur_right = interval[1]
else:
if cur_right >= interval[1]:
continue
else:
cur_left, cur_right = interval
count += 1
return count
__________________________________________________________________________________________________
|
examples/textbook/gravity_gravity.py | rknop/amuse | 131 | 12612060 | #from __future__ import print_function
import numpy
from amuse.units import units
from amuse.units import quantities
from amuse.units import constants
from amuse.units import nbody_system
from amuse.ext.bridge import bridge
from amuse.community.phigrape.interface import PhiGRAPE
from amuse.community.ph4.interface import ph4
from amuse.community.fi.interface import Fi
from amuse.community.bhtree.interface import BHTree
from amuse.community.gadget2.interface import Gadget2
from matplotlib import pyplot
from amuse.ic.kingmodel import new_king_model
from amuse.ext.galactics_model import new_galactics_model
from amuse.lab import new_powerlaw_mass_distribution
def make_king_model_cluster(nbodycode, N, W0, Mcluster,
Rcluster, parameters = []):
converter=nbody_system.nbody_to_si(Mcluster,Rcluster)
bodies=new_king_model(N,W0,convert_nbody=converter)
code=nbodycode(converter)
for name,value in parameters:
setattr(code.parameters, name, value)
code.particles.add_particles(bodies)
return code
from prepare_figure import single_frame
from distinct_colours import get_distinct
from matplotlib.colors import LogNorm
def plot_galaxy_and_stars(galaxy, stars):
colors = get_distinct(3)
single_frame('X [pc]', 'Y [pc]')
xlim = 60
pyplot.xlim(-xlim, xlim)
pyplot.ylim(-xlim, xlim)
ax = pyplot.gca()
import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
p = galaxy.select(lambda x: x<60|units.parsec,["x"])
p = p.select(lambda x: x>-60|units.parsec,["x"])
p = p.select(lambda y: y<60|units.parsec,["y"])
p = p.select(lambda y: y>-60|units.parsec,["y"])
x = p.x.value_in(units.parsec)
y = p.y.value_in(units.parsec)
sns.kdeplot(x, y, ax=ax)
m = 100*numpy.sqrt(stars.mass/stars.mass.max())
pyplot.scatter(stars.x.value_in(units.parsec), stars.y.value_in(units.parsec), c=colors[0], s=m, lw=0)
# pyplot.show()
pyplot.savefig("Fujii_Comparison_Figure")
from amuse.lab import new_plummer_model
def evolve_cluster_in_galaxy(N, W0, Rinit, tend, timestep, M, R):
R_galaxy=0.1 | units.kpc
M_galaxy=1.6e10 | units.MSun
converter=nbody_system.nbody_to_si(M_galaxy, R_galaxy)
galaxy=new_plummer_model(10000,convert_nbody=converter)
print("com:", galaxy.center_of_mass().in_(units.kpc))
print("comv:", galaxy.center_of_mass_velocity().in_(units.kms))
print(len(galaxy))
galaxy_code = BHTree(converter, number_of_workers=2)
galaxy_code.parameters.epsilon_squared = (0.01 | units.kpc)**2
channe_to_galaxy = galaxy_code.particles.new_channel_to(galaxy)
channe_to_galaxy.copy()
galaxy_code.particles.add_particles(galaxy)
inner_stars = galaxy.select(lambda r: r.length()<Rinit,["position"])
Minner = inner_stars.mass.sum()
print("Minner=", Minner.in_(units.MSun))
print("Ninner=", len(inner_stars))
vc_inner = (constants.G*Minner/Rinit).sqrt()
converter=nbody_system.nbody_to_si(Mcluster,Rcluster)
stars=new_king_model(N,W0,convert_nbody=converter)
masses = new_powerlaw_mass_distribution(N, 0.1|units.MSun, 100|units.MSun, -2.35)
stars.mass = masses
stars.scale_to_standard(converter)
stars.x += Rinit
stars.vy += 0.8*vc_inner
cluster_code=ph4(converter, number_of_workers=2)
cluster_code.particles.add_particles(stars)
channel_to_stars=cluster_code.particles.new_channel_to(stars)
system=bridge(verbose=False)
system.add_system(cluster_code, (galaxy_code,))
system.add_system(galaxy_code, (cluster_code,))
system.timestep = 0.1*timestep
times = quantities.arange(0|units.Myr, tend, timestep)
for i,t in enumerate(times):
print("Time=", t.in_(units.Myr))
channe_to_galaxy.copy()
channel_to_stars.copy()
inner_stars = galaxy.select(lambda r: r.length()<Rinit,["position"])
print("Minner=", inner_stars.mass.sum().in_(units.MSun))
system.evolve_model(t,timestep=timestep)
plot_galaxy_and_stars(galaxy, stars)
galaxy_code.stop()
cluster_code.stop()
if __name__ == "__main__":
N=1024
W0 = 3
Rinit=50. | units.parsec
timestep = 0.1 | units.Myr
# endtime = 1.8| units.Myr
# endtime = 1.4| units.Myr
endtime = 2.35| units.Myr
Mcluster = 5.e4 | units.MSun
Rcluster = 0.8 | units.parsec
evolve_cluster_in_galaxy(N, W0, Rinit, endtime, timestep, Mcluster, Rcluster)
|
FAS_challenge_CVPRW2020/Track1 Multi-modal/model1_2_pytorch/models/CDCNs.py | Turing311/CDCN | 463 | 12612072 | <filename>FAS_challenge_CVPRW2020/Track1 Multi-modal/model1_2_pytorch/models/CDCNs.py
import math
import torch
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import Parameter
import pdb
import numpy as np
class Conv2d_cd(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, dilation=1, groups=1, bias=False, theta=0.7):
super(Conv2d_cd, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
self.theta = theta
def forward(self, x):
out_normal = self.conv(x)
if math.fabs(self.theta - 0.0) < 1e-8:
return out_normal
else:
#pdb.set_trace()
[C_out,C_in, kernel_size,kernel_size] = self.conv.weight.shape
kernel_diff = self.conv.weight.sum(2).sum(2)
kernel_diff = kernel_diff[:, :, None, None]
out_diff = F.conv2d(input=x, weight=kernel_diff, bias=self.conv.bias, stride=self.conv.stride, padding=0, dilation=self.conv.dilation, groups=self.conv.groups)
return out_normal - self.theta * out_diff
class SpatialAttention(nn.Module):
def __init__(self, kernel = 3):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size=kernel, padding=kernel//2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return self.sigmoid(x)
class CDCNpp(nn.Module):
def __init__(self, basic_conv=Conv2d_cd, theta=0.7 ):
super(CDCNpp, self).__init__()
self.conv1 = nn.Sequential(
basic_conv(3, 80, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(80),
nn.ReLU(),
)
self.Block1 = nn.Sequential(
basic_conv(80, 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
basic_conv(160, int(160*1.6), kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(int(160*1.6)),
nn.ReLU(),
basic_conv(int(160*1.6), 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block2 = nn.Sequential(
basic_conv(160, int(160*1.2), kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(int(160*1.2)),
nn.ReLU(),
basic_conv(int(160*1.2), 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
basic_conv(160, int(160*1.4), kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(int(160*1.4)),
nn.ReLU(),
basic_conv(int(160*1.4), 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block3 = nn.Sequential(
basic_conv(160, 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
basic_conv(160, int(160*1.2), kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(int(160*1.2)),
nn.ReLU(),
basic_conv(int(160*1.2), 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
# Original
self.lastconv1 = nn.Sequential(
basic_conv(160*3, 160, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(160),
nn.ReLU(),
basic_conv(160, 1, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.ReLU(),
)
self.sa1 = SpatialAttention(kernel = 7)
self.sa2 = SpatialAttention(kernel = 5)
self.sa3 = SpatialAttention(kernel = 3)
self.downsample32x32 = nn.Upsample(size=(32, 32), mode='bilinear')
def forward(self, x): # x [3, 256, 256]
x_input = x
x = self.conv1(x)
x_Block1 = self.Block1(x)
attention1 = self.sa1(x_Block1)
x_Block1_SA = attention1 * x_Block1
x_Block1_32x32 = self.downsample32x32(x_Block1_SA)
x_Block2 = self.Block2(x_Block1)
attention2 = self.sa2(x_Block2)
x_Block2_SA = attention2 * x_Block2
x_Block2_32x32 = self.downsample32x32(x_Block2_SA)
x_Block3 = self.Block3(x_Block2)
attention3 = self.sa3(x_Block3)
x_Block3_SA = attention3 * x_Block3
x_Block3_32x32 = self.downsample32x32(x_Block3_SA)
x_concat = torch.cat((x_Block1_32x32,x_Block2_32x32,x_Block3_32x32), dim=1)
#pdb.set_trace()
map_x = self.lastconv1(x_concat)
map_x = map_x.squeeze(1)
return map_x, x_concat, attention1, attention2, attention3, x_input
############################################
# Multi-modal
############################################
class CDCN_3modality2(nn.Module):
def __init__(self, basic_conv=Conv2d_cd, theta=0.7):
super(CDCN_3modality2, self).__init__()
self.conv1_M1 = nn.Sequential(
basic_conv(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1_M1 = nn.Sequential(
basic_conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block2_M1 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block3_M1 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.conv1_M2 = nn.Sequential(
basic_conv(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1_M2 = nn.Sequential(
basic_conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block2_M2 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block3_M2 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.conv1_M3 = nn.Sequential(
basic_conv(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1_M3 = nn.Sequential(
basic_conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block2_M3 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block3_M3 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.lastconv1_M1 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv1_M2 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv1_M3 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv2 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv3 = nn.Sequential(
basic_conv(128, 1, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.ReLU(),
)
self.downsample32x32 = nn.Upsample(size=(32, 32), mode='bilinear')
def forward(self, x1, x2, x3):
# RGB
x_input = x1
x = self.conv1_M1(x1)
x_Block1_M1 = self.Block1_M1(x)
x_Block1_32x32_M1 = self.downsample32x32(x_Block1_M1)
x_Block2_M1 = self.Block2_M1(x_Block1_M1)
x_Block2_32x32_M1 = self.downsample32x32(x_Block2_M1)
x_Block3_M1 = self.Block3_M1(x_Block2_M1)
x_Block3_32x32_M1 = self.downsample32x32(x_Block3_M1)
x_concat_M1 = torch.cat((x_Block1_32x32_M1,x_Block2_32x32_M1,x_Block3_32x32_M1), dim=1)
# IR
x = self.conv1_M2(x2)
x_Block1_M2 = self.Block1_M2(x)
x_Block1_32x32_M2 = self.downsample32x32(x_Block1_M2)
x_Block2_M2 = self.Block2_M2(x_Block1_M2)
x_Block2_32x32_M2 = self.downsample32x32(x_Block2_M2)
x_Block3_M2 = self.Block3_M2(x_Block2_M2)
x_Block3_32x32_M2 = self.downsample32x32(x_Block3_M2)
x_concat_M2 = torch.cat((x_Block1_32x32_M2,x_Block2_32x32_M2,x_Block3_32x32_M2), dim=1)
# Depth
x = self.conv1_M3(x3)
x_Block1_M3 = self.Block1_M3(x)
x_Block1_32x32_M3 = self.downsample32x32(x_Block1_M3)
x_Block2_M3 = self.Block2_M3(x_Block1_M3)
x_Block2_32x32_M3 = self.downsample32x32(x_Block2_M1)
x_Block3_M3 = self.Block3_M3(x_Block2_M3)
x_Block3_32x32_M3 = self.downsample32x32(x_Block3_M3)
x_concat_M3 = torch.cat((x_Block1_32x32_M3,x_Block2_32x32_M3,x_Block3_32x32_M3), dim=1)
x_M1 = self.lastconv1_M1(x_concat_M1)
x_M2 = self.lastconv1_M2(x_concat_M2)
x_M3 = self.lastconv1_M3(x_concat_M3)
x = torch.cat((x_M1,x_M2,x_M3), dim=1)
x = self.lastconv2(x)
x = self.lastconv3(x)
map_x = x.squeeze(1)
return map_x, x_concat_M1, x_Block1_M1, x_Block2_M1, x_Block3_M1, x_input
|
tests/unit/command/test_get_url.py | lucasalavapena/dvc | 9,136 | 12612073 | <filename>tests/unit/command/test_get_url.py
from dvc.cli import parse_args
from dvc.command.get_url import CmdGetUrl
def test_get_url(mocker):
cli_args = parse_args(["get-url", "src", "out", "-j", "5"])
assert cli_args.func == CmdGetUrl
cmd = cli_args.func(cli_args)
m = mocker.patch("dvc.repo.Repo.get_url")
assert cmd.run() == 0
m.assert_called_once_with("src", out="out", jobs=5)
|
loss_fn/base_criteria.py | apple/ml-cvnets | 209 | 12612092 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch import nn, Tensor
import argparse
from typing import Any
class BaseCriteria(nn.Module):
def __init__(self, *args, **kwargs):
super(BaseCriteria, self).__init__()
self.eps = 1e-7
@classmethod
def add_arguments(cls, parser: argparse.ArgumentParser):
return parser
def forward(
self, input_sample: Any, prediction: Any, target: Any, *args, **kwargs
) -> Tensor:
raise NotImplementedError
@staticmethod
def _class_weights(target: Tensor, n_classes: int, norm_val: float = 1.1) -> Tensor:
class_hist: Tensor = torch.histc(
target.float(), bins=n_classes, min=0, max=n_classes - 1
)
mask_indices = class_hist == 0
# normalize between 0 and 1 by dividing by the sum
norm_hist = torch.div(class_hist, class_hist.sum())
norm_hist = torch.add(norm_hist, norm_val)
# compute class weights..
# samples with more frequency will have less weight and vice-versa
class_wts = torch.div(torch.ones_like(class_hist), torch.log(norm_hist))
# mask the classes which do not have samples in the current batch
class_wts[mask_indices] = 0.0
return class_wts.to(device=target.device)
def __repr__(self):
return "{}()".format(self.__class__.__name__)
|
snippets/06 - Reshaping data6.py | joshuagottardogalvani/pandas-tutorial | 183 | 12612116 | colnames = ['date'] + [item for pair in zip(hours, ['flag']*24) for item in pair]
data = pd.read_csv("data/BETR8010000800100hour.1-1-1990.31-12-2012",
sep='\t', header=None, na_values=[-999, -9999], names=colnames) |
chromium/tools/telemetry/telemetry/internal/browser/tab.py | wedataintelligence/vivaldi-source | 925 | 12612121 | <gh_stars>100-1000
# Copyright 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from telemetry.internal.browser import web_contents
from telemetry.internal.image_processing import video
DEFAULT_TAB_TIMEOUT = 60
class Tab(web_contents.WebContents):
"""Represents a tab in the browser
The important parts of the Tab object are in the runtime and page objects.
E.g.:
# Navigates the tab to a given url.
tab.Navigate('http://www.google.com/')
# Evaluates 1+1 in the tab's JavaScript context.
tab.Evaluate('1+1')
"""
def __init__(self, inspector_backend, tab_list_backend, browser):
super(Tab, self).__init__(inspector_backend)
self._tab_list_backend = tab_list_backend
self._browser = browser
@property
def browser(self):
"""The browser in which this tab resides."""
return self._browser
@property
def url(self):
"""Returns the URL of the tab, as reported by devtools.
Raises:
devtools_http.DevToolsClientConnectionError
"""
return self._inspector_backend.url
@property
def dom_stats(self):
"""A dictionary populated with measured DOM statistics.
Currently this dictionary contains:
{
'document_count': integer,
'node_count': integer,
'event_listener_count': integer
}
Raises:
inspector_memory.InspectorMemoryException
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
dom_counters = self._inspector_backend.GetDOMStats(
timeout=DEFAULT_TAB_TIMEOUT)
assert (len(dom_counters) == 3 and
all([x in dom_counters for x in ['document_count', 'node_count',
'event_listener_count']]))
return dom_counters
def Activate(self):
"""Brings this tab to the foreground asynchronously.
Not all browsers or browser versions support this method.
Be sure to check browser.supports_tab_control.
Please note: this is asynchronous. There is a delay between this call
and the page's documentVisibilityState becoming 'visible', and yet more
delay until the actual tab is visible to the user. None of these delays
are included in this call.
Raises:
devtools_http.DevToolsClientConnectionError
devtools_client_backend.TabNotFoundError
tab_list_backend.TabUnexpectedResponseException
"""
self._tab_list_backend.ActivateTab(self.id)
def Close(self):
"""Closes this tab.
Not all browsers or browser versions support this method.
Be sure to check browser.supports_tab_control.
Raises:
devtools_http.DevToolsClientConnectionError
devtools_client_backend.TabNotFoundError
tab_list_backend.TabUnexpectedResponseException
exceptions.TimeoutException
"""
self._tab_list_backend.CloseTab(self.id)
@property
def screenshot_supported(self):
"""True if the browser instance is capable of capturing screenshots."""
return self._inspector_backend.screenshot_supported
def Screenshot(self, timeout=DEFAULT_TAB_TIMEOUT):
"""Capture a screenshot of the tab's contents.
Returns:
A telemetry.core.Bitmap.
Raises:
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
return self._inspector_backend.Screenshot(timeout)
@property
def video_capture_supported(self):
"""True if the browser instance is capable of capturing video."""
return self.browser.platform.CanCaptureVideo()
def Highlight(self, color):
"""Synchronously highlights entire tab contents with the given RgbaColor.
TODO(tonyg): It is possible that the z-index hack here might not work for
all pages. If this happens, DevTools also provides a method for this.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
self.ExecuteJavaScript("""
(function() {
var screen = document.createElement('div');
screen.style.background = 'rgba(%d, %d, %d, %d)';
screen.style.position = 'fixed';
screen.style.top = '0';
screen.style.left = '0';
screen.style.width = '100%%';
screen.style.height = '100%%';
screen.style.zIndex = '2147483638';
document.body.appendChild(screen);
requestAnimationFrame(function() {
requestAnimationFrame(function() {
window.__telemetry_screen_%d = screen;
});
});
})();
""" % (color.r, color.g, color.b, color.a, int(color)))
self.WaitForJavaScriptExpression(
'!!window.__telemetry_screen_%d' % int(color), 5)
def ClearHighlight(self, color):
"""Clears a highlight of the given bitmap.RgbaColor.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
self.ExecuteJavaScript("""
(function() {
document.body.removeChild(window.__telemetry_screen_%d);
requestAnimationFrame(function() {
requestAnimationFrame(function() {
window.__telemetry_screen_%d = null;
console.time('__ClearHighlight.video_capture_start');
console.timeEnd('__ClearHighlight.video_capture_start');
});
});
})();
""" % (int(color), int(color)))
self.WaitForJavaScriptExpression(
'!window.__telemetry_screen_%d' % int(color), 5)
def StartVideoCapture(self, min_bitrate_mbps,
highlight_bitmap=video.HIGHLIGHT_ORANGE_FRAME):
"""Starts capturing video of the tab's contents.
This works by flashing the entire tab contents to a arbitrary color and then
starting video recording. When the frames are processed, we can look for
that flash as the content bounds.
Args:
min_bitrate_mbps: The minimum caputre bitrate in MegaBits Per Second.
The platform is free to deliver a higher bitrate if it can do so
without increasing overhead.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
ValueError: If the required |min_bitrate_mbps| can't be achieved.
"""
self.Highlight(highlight_bitmap)
self.browser.platform.StartVideoCapture(min_bitrate_mbps)
self.ClearHighlight(highlight_bitmap)
@property
def is_video_capture_running(self):
return self.browser.platform.is_video_capture_running
def StopVideoCapture(self):
"""Stops recording video of the tab's contents.
This looks for the initial color flash in the first frame to establish the
tab content boundaries and then omits all frames displaying the flash.
Returns:
video: A video object which is a telemetry.core.Video
"""
return self.browser.platform.StopVideoCapture()
def GetCookieByName(self, name, timeout=DEFAULT_TAB_TIMEOUT):
"""Returns the value of the cookie by the given |name|.
Raises:
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
return self._inspector_backend.GetCookieByName(name, timeout)
def CollectGarbage(self):
"""Forces a garbage collection.
Raises:
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
"""
self._inspector_backend.CollectGarbage()
def ClearCache(self, force):
"""Clears the browser's networking related disk, memory and other caches.
Args:
force: Iff true, navigates to about:blank which destroys the previous
renderer, ensuring that even "live" resources in the memory cache are
cleared.
Raises:
exceptions.EvaluateException
exceptions.WebSocketDisconnected
exceptions.TimeoutException
exceptions.DevtoolsTargetCrashException
errors.DeviceUnresponsiveError
"""
self.browser.platform.FlushDnsCache()
self.ExecuteJavaScript("""
if (window.chrome && chrome.benchmarking &&
chrome.benchmarking.clearCache) {
chrome.benchmarking.clearCache();
chrome.benchmarking.clearPredictorCache();
chrome.benchmarking.clearHostResolverCache();
}
""")
if force:
self.Navigate('about:blank')
|
firefighter/update/common/buildbot/builder.py | tingshao/catapult | 2,151 | 12612136 | <filename>firefighter/update/common/buildbot/builder.py
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import urllib
from common.buildbot import builds
from common.buildbot import network
def Builders(master_name):
builder_data = network.FetchData(network.BuildUrl(
master_name, 'json/builders'))
return sorted(Builder(master_name, builder_name, builder_info)
for builder_name, builder_info in builder_data.iteritems())
class Builder(object):
def __init__(self, master_name, name, data):
self._master_name = master_name
self._name = name
self._url = network.BuildUrl(
master_name, 'builders/%s' % urllib.quote(self.name))
self._builds = builds.Builds(master_name, name, self._url)
self.Update(data)
def __lt__(self, other):
return self.name < other.name
def __str__(self):
return self.name
def Update(self, data=None):
if not data:
data = network.FetchData(network.BuildUrl(
self.master_name, 'json/builders/%s' % urllib.quote(self.name)))
self._state = data['state']
self._pending_build_count = data['pendingBuilds']
self._current_builds = frozenset(data['currentBuilds'])
self._cached_builds = frozenset(data['cachedBuilds'])
self._slaves = frozenset(data['slaves'])
@property
def master_name(self):
return self._master_name
@property
def name(self):
return self._name
@property
def url(self):
return self._url
@property
def state(self):
return self._state
@property
def builds(self):
return self._builds
@property
def pending_build_count(self):
return self._pending_build_count
@property
def current_builds(self):
"""Set of build numbers currently building.
There may be multiple entries if there are multiple build slaves.
"""
return self._current_builds
@property
def cached_builds(self):
"""Set of builds whose data are visible on the master in increasing order.
More builds may be available than this.
"""
return self._cached_builds
@property
def available_builds(self):
return self.cached_builds - self.current_builds
@property
def last_build(self):
"""Last completed build."""
return max(self.available_builds)
@property
def slaves(self):
return self._slaves
|
disabled-challenges/SBTP/poller/for-release/machine.py | pingjuiliao/cb-multios | 473 | 12612149 | #!/usr/bin/env python
#
# Copyright (C) 2014
# <NAME> <<EMAIL>>
# Narf Industries <<EMAIL>>
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
import string
import sys
import os
from generator.actions import Actions
from random import choice,randint
from struct import *
from os.path import dirname, abspath, join
from commands import getoutput
from re import escape
from collections import OrderedDict
cb_base = dirname(dirname(dirname(abspath(__file__))))
cb = join(cb_base, "build/release/bin/NRFIN_00071")
req = {
"MKDIR":0,
"LIST":1,
"PUT":2,
"GET":3,
"RM":4,
"RMDIR":5,
"QUIT":6,
}
resp = {
"MKDIR_OK":0,
"MKDIR_FAIL":1,
"LIST_OK":2,
"LIST_FAIL":3,
"PUT_OK":4,
"PUT_FAIL":5,
"GET_OK":6,
"GET_FAIL":7,
"RM_OK":8,
"RM_FAIL":9,
"RMDIR_OK":10,
"RMDIR_FAIL":11,
"QUIT_OK":12
}
README = "This is a sample file! Feel free to add your own!"
def random_alpha(a, b):
return ''.join(choice(string.letters) for _ in range(randint(a, b)))
x,y,z,w = 0,0,0,0
def getshifty():
global x, y, z, w
tmp = x ^ ((x<<12)&0xffffffff)
x = y
y = z
z = w
w = w ^ (w>>18) ^ tmp ^ (tmp >> 9)
return w
def xor(x,y):
return x^y
def chunks(l, n):
for i in xrange(0, len(l), n):
yield l[i:i+n]
class SBTP(Actions):
def start(self):
#self.delay(100)
#init prng
global x, y, z, w
x = reduce(xor, unpack("<II",self.magic_page[:8]))
y = reduce(xor,unpack("<II",self.magic_page[8:16]))
z = reduce(xor,unpack("<II",self.magic_page[16:24]))
w = reduce(xor,unpack("<II",self.magic_page[24:32]))
#set up our config
self.version = randint(0xc0ff33,0xffffffff)
self.min_ver = randint(0,0xc0ff32)
#we make a point to always "encrypt"
#and see if competitors discover/choose to use plaintext
self.encrypt = 0
self.allow_reconnect = 1
self.buf_size = 0
self.files = OrderedDict([("#README",README)])
self.subdir = None
def do_session(self):
#want to ensure teams can't just get rid of reconnects
#but don't want to make polls take *overly* long
if randint(0,50) == 42:
self.doreconn()
options = [
self.add_rootfile,
self.add_subdir,
self.add_subfile,
self.list_root,
self.list_root,
self.list_subdir,
self.get_all_files,
self.rm_subdir_file,
self.rm_subdir,
self.rm_all_files,
self.add_subdir,
self.add_subfile,
self.get_all_files,
]
for i in range(randint(10,50)):
choice(options)()
def verify(self):
#ensure cb is built
if not os.path.exists(cb):
os.system("make -C %s build" % cb_base)
readcgcef = getoutput("readcgcef -a %s" % cb)
for line in readcgcef.split("\n"):
parts = line.split()
if len(parts) != 7:
continue
if parts[6] == "go":
start = int(parts[1],16)-0x8048000
if parts[6] == "longjmp":
end = int(parts[1],16)-0x8048000
csum = getshifty();
magic = 0x1337b33f ^ reduce(xor, unpack("<II",self.magic_page[:8]))
cbdata = open(cb,"r").read()
for i in range(start,end,4):
csum ^= unpack("<I",cbdata[i:i+4])[0]
magic = pack(">I",magic).encode("hex").upper()
csum = pack(">I",csum).encode("hex").upper()
#leaving debug magic in on the off chance anyone manages to brute force it
self.read(length=8, expect="("+magic+"|"+csum+")", expect_format="pcre")
self.write(pack("<I",1))
def read_seed(self):
global x, y, z, w
if self.encrypt:
x = xor(*unpack("<II",self.magic_page[32:40]))
y = xor(*unpack("<II",self.magic_page[40:48]))
z = xor(*unpack("<II",self.magic_page[48:56]))
w = xor(*unpack("<II",self.magic_page[56:64]))
else:
x,y,z,w = 0,0,0,0
self.read(length=16, expect=pack("<IIII",x,y,z,w))
def send_conf(self):
self.write(pack("<IIIII",self.version, self.min_ver, self.encrypt, self.allow_reconnect, self.buf_size))
self.read(length=4, expect=pack("<I",1))
def doreconn(self):
self.delay(3000)
self.write(pack("<IIIII",self.version, self.min_ver, self.encrypt, self.allow_reconnect, self.buf_size))
self.write(pack("<I",0))
self.doread(pack("<I1024s", resp['GET_OK'], README))
def _enc(self,s):
if (len(s) % 4) != 0:
raise Exception("Must be 4 byte aligned.")
res = []
for dword in chunks(s,4):
tmp = getshifty()
res.append(pack("<I",tmp^unpack("<I",dword)[0]))
return "".join(res)
def dowrite(self,s):
if (self.encrypt):
s = self._enc(s)
self.write(s)
def doread(self,s):
if (self.encrypt):
s = self._enc(s)
self.read(length=len(s), expect=s)
def get_file(self, fn, data):
self.dowrite(pack("<I1024s", req['GET'], fn))
self.doread(pack("<I1024s", resp['GET_OK'], data))
def get_file_fail(self,fn):
self.dowrite(pack("<I1024s", req['GET'], fn))
self.doread(pack("<I1024s", resp['GET_FAIL'], ""))
def get_readme(self):
self.get_file("#README", README)
def put_file(self, fn, data):
self.dowrite(pack("<I1024s", req['PUT'],fn+'\x00'+data))
self.doread(pack("<I1024s", resp['PUT_OK'], ''))
self.files[fn] = data
def add_rootfile(self):
for i in range(randint(0,5)):
fn = "#"+random_alpha(4,20)
if fn not in self.files:
data = random_alpha(1,900)
self.put_file(fn, data)
def ls_dir(self, path, files=[]):
files = [f.split("#")[-1] for f in files]
self.dowrite(pack("<I1024s", req['LIST'],path+'\x00'))
self.doread(pack("<I1024s", resp['LIST_OK'],":".join(files)))
def list_root(self):
self.ls_dir("#", [fn for fn in self.files if fn.count("#") == 1])
def list_subdir(self):
if not self.subdir:
self.add_subdir()
self.ls_dir(self.subdir, [fn for fn in self.files if fn.startswith(self.subdir+"#")])
def add_subdir(self):
if self.subdir:
self.rm_subdir()
self.subdir = "#"+random_alpha(4,20)
self.dowrite(pack("<I1024s", req['MKDIR'], self.subdir))
self.doread(pack("<I1024s", resp['MKDIR_OK'], ''))
def add_subfile(self):
if not self.subdir:
self.add_subdir()
for i in range(randint(0,5)):
fn = self.subdir+"#"+random_alpha(4,20)
if fn not in self.files:
data = random_alpha(1,900)
self.put_file(fn, data)
def get_all_files(self):
for fn,data in self.files.items():
self.get_file(fn,data)
def rm(self, path):
self.dowrite(pack("<I1024s", req['RM'], path))
self.doread(pack("<I1024s", resp['RM_OK'], ''))
#confirm deletion
self.get_file_fail(path)
del(self.files[path])
def rm_dir(self, path):
self.dowrite(pack("<I1024s", req['RMDIR'], path))
self.doread(pack("<I1024s", resp['RMDIR_OK'], ''))
deleted = [fn for fn in self.files if fn.startswith(path+"#")]
#confirm deletion
for fn in deleted:
self.get_file_fail(fn)
del(self.files[fn])
def rm_all_files(self):
for fn in self.files:
self.rm(fn)
self.files = OrderedDict()
def rm_subdir_file(self):
if not self.subdir:
self.add_subdir()
sfiles = [fn for fn in self.files if fn.startswith(self.subdir+"#")]
if len(sfiles) != 0:
self.rm(choice(sfiles))
def rm_subdir(self):
if not self.subdir:
self.add_subdir()
self.rm_dir(self.subdir)
self.subdir = None
def quit(self):
self.dowrite(pack("<I1024s", req['QUIT'],""))
|
alipay/aop/api/domain/TuitionCertificate.py | antopen/alipay-sdk-python-all | 213 | 12612157 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.TuitionUserName import TuitionUserName
class TuitionCertificate(object):
def __init__(self):
self._certificate_no = None
self._certificate_type = None
self._effective_date = None
self._expire_date = None
self._holder_name = None
@property
def certificate_no(self):
return self._certificate_no
@certificate_no.setter
def certificate_no(self, value):
self._certificate_no = value
@property
def certificate_type(self):
return self._certificate_type
@certificate_type.setter
def certificate_type(self, value):
self._certificate_type = value
@property
def effective_date(self):
return self._effective_date
@effective_date.setter
def effective_date(self, value):
self._effective_date = value
@property
def expire_date(self):
return self._expire_date
@expire_date.setter
def expire_date(self, value):
self._expire_date = value
@property
def holder_name(self):
return self._holder_name
@holder_name.setter
def holder_name(self, value):
if isinstance(value, TuitionUserName):
self._holder_name = value
else:
self._holder_name = TuitionUserName.from_alipay_dict(value)
def to_alipay_dict(self):
params = dict()
if self.certificate_no:
if hasattr(self.certificate_no, 'to_alipay_dict'):
params['certificate_no'] = self.certificate_no.to_alipay_dict()
else:
params['certificate_no'] = self.certificate_no
if self.certificate_type:
if hasattr(self.certificate_type, 'to_alipay_dict'):
params['certificate_type'] = self.certificate_type.to_alipay_dict()
else:
params['certificate_type'] = self.certificate_type
if self.effective_date:
if hasattr(self.effective_date, 'to_alipay_dict'):
params['effective_date'] = self.effective_date.to_alipay_dict()
else:
params['effective_date'] = self.effective_date
if self.expire_date:
if hasattr(self.expire_date, 'to_alipay_dict'):
params['expire_date'] = self.expire_date.to_alipay_dict()
else:
params['expire_date'] = self.expire_date
if self.holder_name:
if hasattr(self.holder_name, 'to_alipay_dict'):
params['holder_name'] = self.holder_name.to_alipay_dict()
else:
params['holder_name'] = self.holder_name
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = TuitionCertificate()
if 'certificate_no' in d:
o.certificate_no = d['certificate_no']
if 'certificate_type' in d:
o.certificate_type = d['certificate_type']
if 'effective_date' in d:
o.effective_date = d['effective_date']
if 'expire_date' in d:
o.expire_date = d['expire_date']
if 'holder_name' in d:
o.holder_name = d['holder_name']
return o
|
juriscraper/opinions/united_states_backscrapers/federal_special/cit_2003.py | EvandoBlanco/juriscraper | 228 | 12612172 | <filename>juriscraper/opinions/united_states_backscrapers/federal_special/cit_2003.py
# Scraper for the United States Court of International Trade
# CourtID: cit
# Court Short Name: Ct. Int'l Trade
# Neutral Citation Format: Ct. Int'l Trade No. 12-1
from juriscraper.OpinionSite import OpinionSite
import re
import time
from datetime import date
from lxml import html
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
# This is a special backscraper to deal with problems on the 2003 page.
self.url = "http://www.cit.uscourts.gov/SlipOpinions/SlipOps-2003.html"
self.court_id = self.__module__
def _get_download_urls(self):
return [
t
for t in self.html.xpath(
"//table[3]//tr[position() > 1]/td//font//a/@href"
)
]
def _get_neutral_citations(self):
neutral_citations = []
for e in self.html.xpath(
"//table[3]//tr[position() > 1]/td[1]//font//a"
):
s = html.tostring(e, method="text", encoding="unicode").strip()
if s == "03-59":
continue
else:
neutral_citations.append(s)
return neutral_citations
def _get_case_names(self):
case_names = []
for e in self.html.xpath("//table[3]//tr[position() > 1]/td[2]/*"):
s = html.tostring(e, method="text", encoding="unicode").strip()
# We strip "erratum: mm/dd/yyyy" from the case names of errata docs.
if "errat" in s:
case_names.append(s.strip()[:-20])
elif s == "VACATED":
continue
else:
case_names.append(s.strip())
return case_names
def _get_precedential_statuses(self):
return ["Published"] * len(self.case_names)
def _get_case_dates(self):
case_dates = []
for e in self.html.xpath("//table[3]//tr[position() > 1]/td[3]//font"):
s = html.tostring(e, method="text", encoding="unicode").strip()
if s == "11/18/2003":
continue
elif s == "08//08/2003":
case_dates.append(
date.fromtimestamp(
time.mktime(time.strptime("08/08/2003", "%m/%d/%Y"))
)
)
else:
case_dates.append(
date.fromtimestamp(
time.mktime(time.strptime(s.strip(), "%m/%d/%Y"))
)
)
return case_dates
# Because there can be multiple docket numbers we have to replace some newlines.
def _get_docket_numbers(self):
docket_numbers = []
for e in self.html.xpath("//table[3]//tr[position() > 1]/td[4]//font"):
s = html.tostring(e, method="text", encoding="unicode").strip()
if s == "01-00745":
continue
else:
docket_numbers.append(s.replace("\r\n", " &"))
return docket_numbers
|
docs/settings.py | simonkern/django-crispy-forms | 2,347 | 12612182 | <reponame>simonkern/django-crispy-forms
import os
SITE_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)))
TEMPLATE_LOADERS = (
"django.template.loaders.filesystem.Loader",
"django.template.loaders.app_directories.Loader",
)
TEMPLATE_DIRS = os.path.join(SITE_ROOT, "templates")
INSTALLED_APPS = "crispy_forms"
SECRET_KEY = "secretkey"
|
detection/RetinaFace/rcnn/utils/save_model.py | dwhite54/insightface | 12,377 | 12612191 | <filename>detection/RetinaFace/rcnn/utils/save_model.py<gh_stars>1000+
import mxnet as mx
def save_checkpoint(prefix, epoch, arg_params, aux_params):
"""Checkpoint the model data into file.
:param prefix: Prefix of model name.
:param epoch: The epoch number of the model.
:param arg_params: dict of str to NDArray
Model parameter, dict of name to NDArray of net's weights.
:param aux_params: dict of str to NDArray
Model parameter, dict of name to NDArray of net's auxiliary states.
:return: None
prefix-epoch.params will be saved for parameters.
"""
save_dict = {('arg:%s' % k): v for k, v in arg_params.items()}
save_dict.update({('aux:%s' % k): v for k, v in aux_params.items()})
param_name = '%s-%04d.params' % (prefix, epoch)
mx.nd.save(param_name, save_dict)
|
checkov/kubernetes/checks/ApiServerAuthorizationModeNode.py | niradler/checkov | 4,013 | 12612220 | from checkov.common.models.enums import CheckCategories, CheckResult
from checkov.kubernetes.base_spec_check import BaseK8Check
class ApiServerAuthorizationModeNode(BaseK8Check):
def __init__(self):
id = "CKV_K8S_75"
name = "Ensure that the --authorization-mode argument includes Node"
categories = [CheckCategories.KUBERNETES]
supported_entities = ['containers']
super().__init__(name=name, id=id, categories=categories, supported_entities=supported_entities)
def get_resource_id(self, conf):
return f'{conf["parent"]} - {conf["name"]}' if conf.get('name') else conf["parent"]
def scan_spec_conf(self, conf):
if conf.get("command") is not None:
if "kube-apiserver" in conf["command"]:
hasNodeAuthorizationMode = False
for command in conf["command"]:
if command.startswith("--authorization-mode"):
modes = command.split("=")[1]
if "Node" in modes.split(","):
hasNodeAuthorizationMode = True
return CheckResult.PASSED if hasNodeAuthorizationMode else CheckResult.FAILED
return CheckResult.PASSED
check = ApiServerAuthorizationModeNode()
|
homeassistant/components/airnow/__init__.py | MrDelik/core | 30,023 | 12612221 | """The AirNow integration."""
import datetime
import logging
from aiohttp.client_exceptions import ClientConnectorError
from pyairnow import WebServiceAPI
from pyairnow.conv import aqi_to_concentration
from pyairnow.errors import AirNowError
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import (
CONF_API_KEY,
CONF_LATITUDE,
CONF_LONGITUDE,
CONF_RADIUS,
Platform,
)
from homeassistant.core import HomeAssistant
from homeassistant.helpers.aiohttp_client import async_get_clientsession
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
from .const import (
ATTR_API_AQI,
ATTR_API_AQI_DESCRIPTION,
ATTR_API_AQI_LEVEL,
ATTR_API_AQI_PARAM,
ATTR_API_CAT_DESCRIPTION,
ATTR_API_CAT_LEVEL,
ATTR_API_CATEGORY,
ATTR_API_PM25,
ATTR_API_POLLUTANT,
ATTR_API_REPORT_DATE,
ATTR_API_REPORT_HOUR,
ATTR_API_STATE,
ATTR_API_STATION,
ATTR_API_STATION_LATITUDE,
ATTR_API_STATION_LONGITUDE,
DOMAIN,
)
_LOGGER = logging.getLogger(__name__)
PLATFORMS = [Platform.SENSOR]
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up AirNow from a config entry."""
api_key = entry.data[CONF_API_KEY]
latitude = entry.data[CONF_LATITUDE]
longitude = entry.data[CONF_LONGITUDE]
distance = entry.data[CONF_RADIUS]
# Reports are published hourly but update twice per hour
update_interval = datetime.timedelta(minutes=30)
# Setup the Coordinator
session = async_get_clientsession(hass)
coordinator = AirNowDataUpdateCoordinator(
hass, session, api_key, latitude, longitude, distance, update_interval
)
# Sync with Coordinator
await coordinator.async_config_entry_first_refresh()
# Store Entity and Initialize Platforms
hass.data.setdefault(DOMAIN, {})
hass.data[DOMAIN][entry.entry_id] = coordinator
hass.config_entries.async_setup_platforms(entry, PLATFORMS)
return True
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Unload a config entry."""
unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
if unload_ok:
hass.data[DOMAIN].pop(entry.entry_id)
return unload_ok
class AirNowDataUpdateCoordinator(DataUpdateCoordinator):
"""Define an object to hold Airly data."""
def __init__(
self, hass, session, api_key, latitude, longitude, distance, update_interval
):
"""Initialize."""
self.latitude = latitude
self.longitude = longitude
self.distance = distance
self.airnow = WebServiceAPI(api_key, session=session)
super().__init__(hass, _LOGGER, name=DOMAIN, update_interval=update_interval)
async def _async_update_data(self):
"""Update data via library."""
data = {}
try:
obs = await self.airnow.observations.latLong(
self.latitude,
self.longitude,
distance=self.distance,
)
except (AirNowError, ClientConnectorError) as error:
raise UpdateFailed(error) from error
if not obs:
raise UpdateFailed("No data was returned from AirNow")
max_aqi = 0
max_aqi_level = 0
max_aqi_desc = ""
max_aqi_poll = ""
for obv in obs:
# Convert AQIs to Concentration
pollutant = obv[ATTR_API_AQI_PARAM]
concentration = aqi_to_concentration(obv[ATTR_API_AQI], pollutant)
data[obv[ATTR_API_AQI_PARAM]] = concentration
# Overall AQI is the max of all pollutant AQIs
if obv[ATTR_API_AQI] > max_aqi:
max_aqi = obv[ATTR_API_AQI]
max_aqi_level = obv[ATTR_API_CATEGORY][ATTR_API_CAT_LEVEL]
max_aqi_desc = obv[ATTR_API_CATEGORY][ATTR_API_CAT_DESCRIPTION]
max_aqi_poll = pollutant
# Copy other data from PM2.5 Value
if obv[ATTR_API_AQI_PARAM] == ATTR_API_PM25:
# Copy Report Details
data[ATTR_API_REPORT_DATE] = obv[ATTR_API_REPORT_DATE]
data[ATTR_API_REPORT_HOUR] = obv[ATTR_API_REPORT_HOUR]
# Copy Station Details
data[ATTR_API_STATE] = obv[ATTR_API_STATE]
data[ATTR_API_STATION] = obv[ATTR_API_STATION]
data[ATTR_API_STATION_LATITUDE] = obv[ATTR_API_STATION_LATITUDE]
data[ATTR_API_STATION_LONGITUDE] = obv[ATTR_API_STATION_LONGITUDE]
# Store Overall AQI
data[ATTR_API_AQI] = max_aqi
data[ATTR_API_AQI_LEVEL] = max_aqi_level
data[ATTR_API_AQI_DESCRIPTION] = max_aqi_desc
data[ATTR_API_POLLUTANT] = max_aqi_poll
return data
|
{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}/settings/test.py | thorgate/django-project-template | 123 | 12612227 | from settings.local import *
SEND_EMAILS = False
DATABASES["default"]["TEST"] = {
"NAME": "{{ cookiecutter.repo_name }}_test",
}
EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"
# - {%- if cookiecutter.frontend_style == SPA %}
# Use session in tests to make api login easier
REST_FRAMEWORK["DEFAULT_AUTHENTICATION_CLASSES"] = (
"rest_framework.authentication.SessionAuthentication",
)
# - {% endif %}
|
moldesign/_tests/test_geometry.py | Autodesk/molecular-design-toolkit | 147 | 12612241 | """ Tests geometry routines
"""
from builtins import range
import random
import itertools
import pytest
import numpy as np
import moldesign as mdt
from moldesign import units as u
from . import helpers
registered_types = {}
__PYTEST_MARK__ = 'internal' # mark all tests in this module with this label (see ./conftest.py)
# TODO: automated method testing based on its metadata - i.e. test to make sure parameters are
# honored, test that it calcultes what it says it does, test that properties have the right
# units and array shapes, etc.
# step for numerical derivative testing
def typedfixture(*types, **kwargs):
"""This is a decorator that lets us associate fixtures with one or more arbitrary types.
We'll later use this type to determine what tests to run on the result"""
def fixture_wrapper(func):
for t in types:
registered_types.setdefault(t, []).append(func.__name__)
return pytest.fixture(**kwargs)(func)
return fixture_wrapper
def _make_mol_with_n_hydrogens(n):
return mdt.Molecule([mdt.Atom('H') for i in range(n)])
def _apply_random_offsets(mol, idim):
mol.positions[:, idim] += (random.random()-0.5)*100.0*u.angstrom
@typedfixture('atomcontainer', scope='function')
def three_particle_right_angle():
mol = _make_mol_with_n_hydrogens(3)
mol.atoms[0].x = 1.0 * u.angstrom
mol.atoms[2].y = 1.0 * u.angstrom
for idim in range(3):
_apply_random_offsets(mol, idim)
return mol
@typedfixture('atomcontainer', scope='function')
def four_particle_45_twist():
mol = _make_mol_with_n_hydrogens(4)
mol.positions = u.nm*[[0.1, 0.0, -0.5],
[0.0, 0.0, -0.5],
[0.0, 0.0, 0.5],
[0.2, -0.2, 0.5]]
for idim in range(3):
_apply_random_offsets(mol, idim)
for iatom in range(3):
mol.atoms[iatom].bond_to(mol.atoms[iatom+1], 1)
return mol
########################
# Dihedrals #
########################
def test_dihedral_measure(four_particle_45_twist):
mol = four_particle_45_twist
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
45.0,
decimal=8)
def test_dihedral_two_atom_selection(four_particle_45_twist):
mol = four_particle_45_twist
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms[1:3]).value_in(u.degrees),
45.0,
decimal=8)
with pytest.raises(ValueError): # raises exception because it's not part of a dihedral
mdt.dihedral(mol.atoms[0], mol.atoms[1])
def test_set_dihedral(four_particle_45_twist):
mol = four_particle_45_twist
mdt.set_dihedral(mol.atoms[0], mol.atoms[1], mol.atoms[2], mol.atoms[3], 10.0 * u.degrees)
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
10.0,
decimal=8)
@pytest.mark.screening
def test_set_dihedral_sign_convention(four_particle_45_twist):
mol = four_particle_45_twist
mdt.set_dihedral(mol.atoms[0], mol.atoms[1], mol.atoms[2], mol.atoms[3], -23.0 * u.degrees)
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
337.0,
decimal=8)
def test_set_dihedral_two_atom_selection(four_particle_45_twist):
mol = four_particle_45_twist
mdt.set_dihedral(mol.atoms[1], mol.atoms[2], 10.0 * u.degrees)
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
10.0,
decimal=8)
with pytest.raises(ValueError): # raises exception because it's not part of a dihedral
mdt.set_dihedral(mol.atoms[0], mol.atoms[1], 5.0 * u.degrees)
def test_set_dihedral_bond_no_adjust(four_particle_45_twist):
mol = four_particle_45_twist
bond = mdt.Bond(mol.atoms[1], mol.atoms[2])
mdt.set_dihedral(bond, 10.0 * u.degrees, adjustmol=False)
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
10.0,
decimal=8)
def test_dihedral_sign_convention(four_particle_45_twist):
mol = four_particle_45_twist
mol.atoms[-1].y += 0.4 * u.nm
np.testing.assert_almost_equal(mdt.dihedral(*mol.atoms).value_in(u.degrees),
315.0,
decimal=8)
# TODO: test behavior at discontinuities (180, -180)
@pytest.mark.screening
def test_dihedral_gradient(four_particle_45_twist):
mol = four_particle_45_twist
dihe = mdt.DihedralMonitor(*mol.atoms)
calc_grad = dihe.gradient()
num_grad = helpers.num_grad(mol, lambda: dihe.value)
np.testing.assert_allclose(calc_grad.defunits_value(),
num_grad.defunits_value(),
atol=5.0*helpers.DEFSTEP.defunits_value())
def test_dihedral_gradient_sign_convention(four_particle_45_twist):
mol = four_particle_45_twist
mol.atoms[-1].y += 0.4 * u.nm
dihe = mdt.DihedralMonitor(*mol.atoms)
calc_grad = dihe.gradient()
num_grad = helpers.num_grad(mol, lambda: dihe.value)
np.testing.assert_allclose(calc_grad,
num_grad,
atol=5.0*helpers.DEFSTEP.defunits_value())
########################
# Angles #
########################
def test_angle_measure(three_particle_right_angle):
mol = three_particle_right_angle
np.testing.assert_almost_equal(mdt.angle(*mol.atoms).value_in(u.degrees),
90.0,
decimal=8)
@pytest.mark.screening
def test_angle_gradient(three_particle_right_angle):
mol = three_particle_right_angle
ang = mdt.AngleMonitor(*mol.atoms)
assert abs(ang.value.value_in(u.degrees) - 90.0) <= 1.0e-8
calc_grad = ang.gradient()
num_grad = helpers.num_grad(mol, lambda:ang.value)
np.testing.assert_allclose(calc_grad.defunits_value(),
num_grad.defunits_value(),
atol=5.0*helpers.DEFSTEP.defunits_value())
def test_set_angle_with_monitor(three_particle_right_angle):
mol = three_particle_right_angle
ang = mdt.AngleMonitor(*mol.atoms)
ang.value = 45 * u.degrees
assert abs(mdt.angle(*mol.atoms) - (45 * u.degrees)) < 0.1 * u.degrees
def test_set_angle_noadjust(four_particle_45_twist):
mol = four_particle_45_twist
assert mdt.angle(*mol.atoms[:3]) == 90.0 * u.degrees
final = 45 * u.degrees
origpos = mol.positions.copy()
mdt.set_angle(mol.atoms[0], mol.atoms[1], mol.atoms[2], final, adjustmol=False)
assert abs(mdt.angle(*mol.atoms[:3]) - final) < 0.1 * u.degrees
assert (mol.positions[-1] == origpos[-1]).all()
########################
# Distances #
########################
def test_distance_array(three_particle_right_angle):
mol = three_particle_right_angle
desired_distance_array = u.angstrom*[[0.0, 1.0, np.sqrt(2)],
[1.0, 0.0, 1.0],
[np.sqrt(2), 1.0, 0.0]]
distance_array = mol.calc_distance_array()
np.testing.assert_allclose(distance_array,
desired_distance_array,
atol=1e-8)
def test_set_distance_and_adjust(four_particle_45_twist):
mol = four_particle_45_twist
origpos = mol.positions.copy()
distance = mdt.DistanceMonitor(mol.atoms[1], mol.atoms[2])
olddist = distance.value
distance.value *= 2.0
displacement = np.sqrt(((origpos[0] - mol.positions[0])**2).sum()) + \
np.sqrt(((origpos[-1] - mol.positions[-1])**2).sum())
assert abs(mdt.distance(mol.atoms[1], mol.atoms[2]) - 2.0*olddist) <= 1e-9 * u.angstrom
assert abs(displacement - olddist) < 1.0e-9 * u.angstrom
def test_set_distance_noadjust(four_particle_45_twist):
mol = four_particle_45_twist
origpos = mol.positions.copy()
olddist = mdt.distance(mol.atoms[1], mol.atoms[2])
mdt.set_distance(mol.atoms[1], mol.atoms[2], 2.0 * olddist, adjustmol=False)
assert abs(mdt.distance(mol.atoms[1], mol.atoms[2]) - 2.0*olddist) <= 1e-9 * u.angstrom
assert (origpos[0] == mol.positions[0]).all() and (origpos[-1] == mol.positions[-1]).all()
@pytest.mark.parametrize('objkey', registered_types['atomcontainer'])
def test_atomic_distance_measures_are_consistent(objkey, request):
mol = request.getfixturevalue(objkey)
distance_array = mol.calc_distance_array()
for i, j in itertools.product(range(3), range(3)):
ai, aj = mol.atoms[i], mol.atoms[j]
assert ai.distance(aj) == distance_array[i, j]
assert mdt.distance(ai, aj) == distance_array[i, j]
np.testing.assert_almost_equal(np.sum((ai.position - aj.position)**2).defunits_value(),
(distance_array[i, j]**2).defunits_value(),
decimal=10)
def test_center_of_mass(four_particle_45_twist):
mol = four_particle_45_twist
mol.positions = u.nm*[[0.1, 0.0, -0.5],
[0.0, 0.0, -0.5],
[0.0, 0.0, 0.5],
[0.2, -0.2, 0.5]]
desired_com_angstroms = np.array([0.1+0.2, -0.2, 0.0]) * 10.0 / 4.0
np.testing.assert_almost_equal(mol.center_of_mass.defunits_value(),
desired_com_angstroms)
mol.atoms[0].mass = 5.0 * u.ureg.kilograms
mol.atoms[1].mass = 10.0 * u.ureg.kilograms
mol.atoms[2].mass = 5.0 * u.ureg.kilograms
mol.atoms[3].mass = 10.0 * u.ureg.kilograms
desired_com_angstroms = np.array([0.1+0.4, -0.4, 0.0]) * 10.0 / 6.0
np.testing.assert_almost_equal(mol.center_of_mass.defunits_value(),
desired_com_angstroms)
def test_set_center_of_mass(four_particle_45_twist):
# reset COM
four_particle_45_twist.com = [0, 0, 0] * u.angstrom
np.testing.assert_almost_equal(four_particle_45_twist.com.value_in(u.angstrom),
([0, 0, 0] * u.angstrom).value_in(u.angstrom))
# set it to its current position
four_particle_45_twist.com = [0, 0, 0] * u.angstrom
np.testing.assert_almost_equal(four_particle_45_twist.com.value_in(u.angstrom),
([0, 0, 0] * u.angstrom).value_in(u.angstrom))
# move COM elsewhere
four_particle_45_twist.com = [10, 0, -10] * u.angstrom
np.testing.assert_almost_equal(four_particle_45_twist.com.value_in(u.angstrom),
([10, 0, -10] * u.angstrom).value_in(u.angstrom))
def test_distance_gradient(three_particle_right_angle):
mol = three_particle_right_angle
dist = mdt.DistanceMonitor(*mol.atoms[:2])
assert dist.value == mol.atoms[0].distance(mol.atoms[1])
calc_grad = dist.gradient()
num_grad = helpers.num_grad(mol, lambda:dist.value)
np.testing.assert_allclose(calc_grad.defunits_value(),
num_grad.defunits_value(),
atol=5.0*helpers.DEFSTEP.defunits_value())
#########################
# Utilities #
#########################
def test_sub_angles():
from moldesign.geom import sub_angles
np.testing.assert_allclose(sub_angles(np.pi*u.radian, np.pi/2.0*u.radian),
np.pi/2.0 * u.radian)
np.testing.assert_allclose(sub_angles(360*u.degrees, 179*u.degrees),
-179*u.degrees)
np.testing.assert_allclose(sub_angles(360*u.degrees, 3*u.degrees),
-3*u.degrees)
np.testing.assert_allclose(sub_angles(720*u.degrees, -360*u.degrees),
0*u.degrees)
np.testing.assert_allclose(sub_angles(180*u.degrees, 270*u.degrees),
-90*u.degrees)
np.testing.assert_allclose(sub_angles(270*u.degrees, 0*u.degrees),
-90*u.degrees)
|
whatlies/language/_convert_lang.py | nth-attempt/whatlies | 325 | 12612264 | <filename>whatlies/language/_convert_lang.py
from typing import Union, List
import tensorflow_text # noqa: F401
import tensorflow as tf
import tensorflow_hub as tfhub
from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet
from whatlies.language._common import SklearnTransformerMixin, HiddenPrints
class ConveRTLanguage(SklearnTransformerMixin):
"""
This object is used to fetch [Embedding][whatlies.embedding.Embedding]s or
[EmbeddingSet][whatlies.embeddingset.EmbeddingSet]s from a
[ConveRT](https://github.com/PolyAI-LDN/polyai-models) model.
This object is meant for retreival, not plotting.
Important:
This object will automatically download a large file if it is not cached yet.
This language model does not contain a vocabulary, so it cannot be used
to retreive similar tokens. Use an `EmbeddingSet` instead.
This language backend might require you to manually install extra dependencies
unless you installed via either;
```
pip install whatlies[tfhub]
pip install whatlies[all]
```
Arguments:
model_id: identifier used for loading the corresponding TFHub module, we currently only allow `'convert'`.
**Usage**:
```python
> from whatlies.language import ConveRTLanguage
> lang = ConveRTLanguage()
> lang['bank']
```
"""
MODEL_URL = {
"convert": "https://github.com/connorbrinton/polyai-models/releases/download/v1.0/model.tar.gz",
}
MODEL_SIGNATURES = [
"default",
"encode_context",
"encode_response",
"encode_sequence",
]
def __init__(self, model_id: str = "convert", signature: str = "default") -> None:
if model_id not in self.MODEL_URL:
raise ValueError(
f"The `model_id` value should be one of {list(self.MODEL_URL.keys())}"
)
if signature not in self.MODEL_SIGNATURES:
raise ValueError(
f"The `signature` value should be one of {self.MODEL_SIGNATURES}"
)
if signature == "encode_context" and model_id in [
"convert-multi-context",
"convert-ubuntu",
]:
raise NotImplementedError(
"Currently 'encode_context' signature is not support with multi-context and ubuntu models."
)
self.model_id = model_id
self.signature = signature
with HiddenPrints():
self.module = tfhub.load(self.MODEL_URL[self.model_id])
self.model = self.module.signatures[self.signature]
def __getitem__(
self, query: Union[str, List[str]]
) -> Union[Embedding, EmbeddingSet]:
"""
Retreive a single embedding or a set of embeddings.
Arguments:
query: single string or list of strings
**Usage**
```python
> from whatlies.language import ConveRTLanguage
> lang = ConveRTLanguage()
> lang['bank']
> lang = ConveRTLanguage()
> lang[['bank of the river', 'money on the bank', 'bank']]
```
"""
if isinstance(query, str):
query_tensor = tf.convert_to_tensor([query])
encoding = self.model(query_tensor)
if self.signature == "encode_sequence":
vec = encoding["sequence_encoding"].numpy().sum(axis=1)[0]
else:
vec = encoding["default"].numpy()[0]
return Embedding(query, vec)
return EmbeddingSet(*[self[tok] for tok in query])
|
src/qrcode/pyqart/art/target.py | lapinozz/ArtCoder | 525 | 12612269 | # Added at : 2016.8.3
# Author : 7sDream
# Usage : Target point(contain point and image pixel info).
__all__ = ['Target']
class Target(object):
def __init__(self, y, x, fill, contrast, point):
self._y = y
self._x = x
self._fill = fill
self._contrast = contrast
self._point = point
self._hard_zero = False
@property
def fill(self):
return self._fill
@property
def contrast(self):
return self._contrast
@property
def y(self):
return self._y
@property
def x(self):
return self._x
@property
def point(self):
return self._point
def set_hard_zero(self):
self._hard_zero = True
def is_hard_zero(self):
return self._hard_zero
def __str__(self):
return "Target({fill}, {contrast:.3f})".format(
fill=self.fill, contrast=self.contrast
)
|
saleor/graphql/attribute/bulk_mutations.py | fairhopeweb/saleor | 15,337 | 12612293 | <filename>saleor/graphql/attribute/bulk_mutations.py
import graphene
from ...attribute import models
from ...core.permissions import PageTypePermissions
from ..core.mutations import ModelBulkDeleteMutation
from ..core.types.common import AttributeError
class AttributeBulkDelete(ModelBulkDeleteMutation):
class Arguments:
ids = graphene.List(
graphene.ID, required=True, description="List of attribute IDs to delete."
)
class Meta:
description = "Deletes attributes."
model = models.Attribute
permissions = (PageTypePermissions.MANAGE_PAGE_TYPES_AND_ATTRIBUTES,)
error_type_class = AttributeError
error_type_field = "attribute_errors"
class AttributeValueBulkDelete(ModelBulkDeleteMutation):
class Arguments:
ids = graphene.List(
graphene.ID,
required=True,
description="List of attribute value IDs to delete.",
)
class Meta:
description = "Deletes values of attributes."
model = models.AttributeValue
permissions = (PageTypePermissions.MANAGE_PAGE_TYPES_AND_ATTRIBUTES,)
error_type_class = AttributeError
error_type_field = "attribute_errors"
|
local/make_vctk_wav.py | ishine/pytorch-kaldi-neural-speaker-embeddings | 141 | 12612294 | import os
import sys
path
|
Python3/1161.py | rakhi2001/ecom7 | 854 | 12612299 | __________________________________________________________________________________________________
Runtime: 388 ms
Memory Usage: 18.5 MB
class Solution:
def maxLevelSum(self, root: TreeNode) -> int:
mapping = {}
self.helper(mapping, root, 1)
max_val, max_level = -9999999, 0
for level, val in mapping.items():
if val > max_val:
max_val = val
max_level = level
return max_level
def helper(self, mapping, root, level):
if not root:
return
mapping[level] = mapping.get(level, 0) + root.val
self.helper(mapping, root.left, level + 1)
self.helper(mapping, root.right, level + 1)
__________________________________________________________________________________________________
__________________________________________________________________________________________________
|
lldb/test/API/commands/expression/namespace_local_var_same_name_obj_c/TestNamespaceLocalVarSameNameObjC.py | mkinsner/llvm | 2,338 | 12612332 | <filename>lldb/test/API/commands/expression/namespace_local_var_same_name_obj_c/TestNamespaceLocalVarSameNameObjC.py
import lldb
from lldbsuite.test.decorators import *
from lldbsuite.test.lldbtest import *
from lldbsuite.test import lldbutil
class TestNamespaceLocalVarSameNameObjC(TestBase):
mydir = TestBase.compute_mydir(__file__)
@add_test_categories(["gmodules", "objc"])
def test_namespace_local_var_same_name_obj_c(self):
self.build()
(self.target, self.process, _, bkpt) = lldbutil.run_to_source_breakpoint(self, '// break here',
lldb.SBFileSpec("util.mm", False))
self.expect("expr error",
substrs=['(NSError *) $0 ='])
lldbutil.continue_to_breakpoint(self.process, bkpt)
self.expect("expr error",
substrs=['(NSError *) $1 ='])
|
src/examples/freeswitch/inbound/inbound_concurrent_dialer_server.py | mknecht/plivoframework | 151 | 12612348 | <filename>src/examples/freeswitch/inbound/inbound_concurrent_dialer_server.py
# -*- coding: utf-8 -*-
# Copyright (c) 2011 Plivo Team. See LICENSE for details.
from plivo.core.freeswitch.inboundsocket import InboundEventSocket
from plivo.core.errors import ConnectError
from plivo.utils.logger import StdoutLogger
import gevent
from gevent import wsgi
CONTACTS = (
'{originate_timeout=20}user/1000 &playback(/usr/local/freeswitch/sounds/en/us/callie/base256/8000/liberty.wav)',
'{originate_timeout=20}user/1000 &playback(/usr/local/freeswitch/sounds/en/us/callie/base256/8000/liberty.wav)',
'{originate_timeout=20}user/1000 &playback(/usr/local/freeswitch/sounds/en/us/callie/base256/8000/liberty.wav)',
)
class MyEventSocket(InboundEventSocket):
def __init__(self, host, port, password, filter="ALL", log=None):
InboundEventSocket.__init__(self, host, port, password, filter)
self.log = log
def on_background_job(self, ev):
'''
Receives callbacks for BACKGROUND_JOB event.
'''
job_uuid = ev['Job-UUID']
job_cmd = ev['Job-Command']
job_arg = ev['Job-Command-Arg']
self.log.debug("BackGround JOB Recieved" )
self.log.debug("%s %s, args %s \n\n" % (job_uuid, job_cmd, job_arg))
def on_channel_hangup(self, ev):
'''
Receives callbacks for BACKGROUND_JOB event.
'''
job_uuid = ev['Job-UUID']
job_cmd = ev['Job-Command']
job_arg = ev['Job-Command-Arg']
self.log.debug("Channel Hangup" )
self.log.debug("%s %s, args %s \n\n " % (job_uuid, job_cmd, job_arg))
def spawn_originate(inbound_event_listener, contact, log):
log.info("Originate command")
fs_bg_api_string = \
"originate %s &playback(/usr/local/freeswitch/sounds/en/us/callie/base256/8000/liberty.wav)" \
% contact
bg_api_response = inbound_event_listener.bgapi(fs_bg_api_string)
log.info(str(bg_api_response))
job_uuid = bg_api_response.get_job_uuid()
if not job_uuid:
log.error("bgapi %s: job uuid not found \n\n" % fs_bg_api_string)
return
log.info("bgapi %s => Job-UUID %s \n\n" % (fs_bg_api_string, job_uuid))
def dispatch_requests(env, start_response):
if env['PATH_INFO'] == '/':
if CONTACTS:
start_response('200 OK', [('Content-Type', 'text/html')])
#Put logic to handle the request each time
pool = gevent.pool.Pool(len(CONTACTS))
jobs = [pool.spawn(spawn_originate, inbound_event_listener, contact, log) for contact in CONTACTS]
gevent.joinall(jobs)
log.debug("All originate commands done")
return ["<b>Executed Request</b>"]
start_response('404 Not Found', [('Content-Type', 'text/html')])
return ['<h1>Wrong Usage - Command Not found</h1>']
if __name__ == '__main__':
log = StdoutLogger()
#Connect to freeswitch ESL in inbound mode
inbound_event_listener = MyEventSocket('127.0.0.1', 8021, 'ClueCon', filter="ALL", log=log)
try:
inbound_event_listener.connect()
except ConnectError, e:
log.error("connect failed: %s" % str(e))
raise SystemExit('exit')
#Connect to freeswitch ESL in inbound mode
wsgi.WSGIServer(('', 8088), dispatch_requests).serve_forever()
|
modules/nltk_contrib/refexpr/constraint.py | h4ck3rm1k3/NLP-project | 123 | 12612352 | #!/usr/bin/python
#
# Copyright 2005 <NAME> <<EMAIL>>
# Originally licensed under the GNU General Public License
#
# Relicensed with permission under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance with the
# License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
@var Unassigned: Helper object instance representing unassigned values
@sort: Problem, Variable, Domain
@group Solvers: Solver,
BacktrackingSolver,
RecursiveBacktrackingSolver,
MinConflictsSolver
@group Constraints: Constraint,
FunctionConstraint,
AllDifferentConstraint,
AllEqualConstraint,
MaxSumConstraint,
ExactSumConstraint,
MinSumConstraint,
InSetConstraint,
NotInSetConstraint,
SomeInSetConstraint,
SomeNotInSetConstraint
"""
import random
import copy
__all__ = ["Problem", "Variable", "Domain", "Unassigned",
"Solver", "BacktrackingSolver", "RecursiveBacktrackingSolver",
"MinConflictsSolver", "Constraint", "FunctionConstraint",
"AllDifferentConstraint", "AllEqualConstraint", "MaxSumConstraint",
"ExactSumConstraint", "MinSumConstraint", "InSetConstraint",
"NotInSetConstraint", "SomeInSetConstraint",
"SomeNotInSetConstraint"]
class Problem(object):
"""
Class used to define a problem and retrieve solutions
"""
def __init__(self, solver=None):
"""
@param solver: Problem solver used to find solutions
(default is L{BacktrackingSolver})
@type solver: instance of a L{Solver} subclass
"""
self._solver = solver or BacktrackingSolver()
self._constraints = []
self._variables = {}
def reset(self):
"""
Reset the current problem definition
Example:
>>> problem = Problem()
>>> problem.addVariable("a", [1, 2])
>>> problem.reset()
>>> problem.getSolution()
>>>
"""
del self._constraints[:]
self._variables.clear()
def setSolver(self, solver):
"""
Change the problem solver currently in use
Example:
>>> solver = BacktrackingSolver()
>>> problem = Problem(solver)
>>> problem.getSolver() is solver
True
@param solver: New problem solver
@type solver: instance of a C{Solver} subclass
"""
self._solver = solver
def getSolver(self):
"""
Obtain the problem solver currently in use
Example:
>>> solver = BacktrackingSolver()
>>> problem = Problem(solver)
>>> problem.getSolver() is solver
True
@return: Solver currently in use
@rtype: instance of a L{Solver} subclass
"""
return self._solver
def addVariable(self, variable, domain):
"""
Add a variable to the problem
Example:
>>> problem = Problem()
>>> problem.addVariable("a", [1, 2])
>>> problem.getSolution() in ({'a': 1}, {'a': 2})
True
@param variable: Object representing a problem variable
@type variable: hashable object
@param domain: Set of items defining the possible values that
the given variable may assume
@type domain: list, tuple, or instance of C{Domain}
"""
if variable in self._variables:
raise ValueError, "Tried to insert duplicated variable %s" % \
repr(variable)
if type(domain) in (list, tuple):
domain = Domain(domain)
elif isinstance(domain, Domain):
domain = copy.copy(domain)
else:
raise TypeError, "Domains must be instances of subclasses of "\
"the Domain class"
if not domain:
raise ValueError, "Domain is empty"
self._variables[variable] = domain
def addVariables(self, variables, domain):
"""
Add one or more variables to the problem
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> solutions = problem.getSolutions()
>>> len(solutions)
9
>>> {'a': 3, 'b': 1} in solutions
True
@param variables: Any object containing a sequence of objects
represeting problem variables
@type variables: sequence of hashable objects
@param domain: Set of items defining the possible values that
the given variables may assume
@type domain: list, tuple, or instance of C{Domain}
"""
for variable in variables:
self.addVariable(variable, domain)
def addConstraint(self, constraint, variables=None):
"""
Add a constraint to the problem
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> problem.addConstraint(lambda a, b: b == a+1, ["a", "b"])
>>> solutions = problem.getSolutions()
>>>
@param constraint: Constraint to be included in the problem
@type constraint: instance a L{Constraint} subclass or a
function to be wrapped by L{FunctionConstraint}
@param variables: Variables affected by the constraint (default to
all variables). Depending on the constraint type
the order may be important.
@type variables: set or sequence of variables
"""
if not isinstance(constraint, Constraint):
if callable(constraint):
constraint = FunctionConstraint(constraint)
else:
raise ValueError, "Constraints must be instances of "\
"subclasses of the Constraint class"
self._constraints.append((constraint, variables))
def getSolution(self):
"""
Find and return a solution to the problem
Example:
>>> problem = Problem()
>>> problem.getSolution() is None
True
>>> problem.addVariables(["a"], [42])
>>> problem.getSolution()
{'a': 42}
@return: Solution for the problem
@rtype: dictionary mapping variables to values
"""
domains, constraints, vconstraints = self._getArgs()
if not domains:
return None
return self._solver.getSolution(domains, constraints, vconstraints)
def getSolutions(self):
"""
Find and return all solutions to the problem
Example:
>>> problem = Problem()
>>> problem.getSolutions() == []
True
>>> problem.addVariables(["a"], [42])
>>> problem.getSolutions()
[{'a': 42}]
@return: All solutions for the problem
@rtype: list of dictionaries mapping variables to values
"""
domains, constraints, vconstraints = self._getArgs()
if not domains:
return []
return self._solver.getSolutions(domains, constraints, vconstraints)
def getSolutionIter(self):
"""
Return an iterator to the solutions of the problem
Example:
>>> problem = Problem()
>>> list(problem.getSolutionIter()) == []
True
>>> problem.addVariables(["a"], [42])
>>> iter = problem.getSolutionIter()
>>> iter.next()
{'a': 42}
>>> iter.next()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
"""
domains, constraints, vconstraints = self._getArgs()
if not domains:
return iter(())
return self._solver.getSolutionIter(domains, constraints,
vconstraints)
def _getArgs(self):
domains = self._variables.copy()
allvariables = domains.keys()
constraints = []
for constraint, variables in self._constraints:
if not variables:
variables = allvariables
constraints.append((constraint, variables))
vconstraints = {}
for variable in domains:
vconstraints[variable] = []
for constraint, variables in constraints:
for variable in variables:
vconstraints[variable].append((constraint, variables))
for constraint, variables in constraints[:]:
constraint.preProcess(variables, domains,
constraints, vconstraints)
for domain in domains.values():
domain.resetState()
if not domain:
return None, None, None
#doArc8(getArcs(domains, constraints), domains, {})
return domains, constraints, vconstraints
# ----------------------------------------------------------------------
# Solvers
# ----------------------------------------------------------------------
def getArcs(domains, constraints):
"""
Return a dictionary mapping pairs (arcs) of constrained variables
@attention: Currently unused.
"""
arcs = {}
for x in constraints:
constraint, variables = x
if len(variables) == 2:
variable1, variable2 = variables
arcs.setdefault(variable1, {})\
.setdefault(variable2, [])\
.append(x)
arcs.setdefault(variable2, {})\
.setdefault(variable1, [])\
.append(x)
return arcs
def doArc8(arcs, domains, assignments):
"""
Perform the ARC-8 arc checking algorithm and prune domains
@attention: Currently unused.
"""
check = dict.fromkeys(domains, True)
while check:
variable, _ = check.popitem()
if variable not in arcs or variable in assignments:
continue
domain = domains[variable]
arcsvariable = arcs[variable]
for othervariable in arcsvariable:
arcconstraints = arcsvariable[othervariable]
if othervariable in assignments:
otherdomain = [assignments[othervariable]]
else:
otherdomain = domains[othervariable]
if domain:
changed = False
for value in domain[:]:
assignments[variable] = value
if otherdomain:
for othervalue in otherdomain:
assignments[othervariable] = othervalue
for constraint, variables in arcconstraints:
if not constraint(variables, domains,
assignments, True):
break
else:
# All constraints passed. Value is safe.
break
else:
# All othervalues failed. Kill value.
domain.hideValue(value)
changed = True
del assignments[othervariable]
del assignments[variable]
#if changed:
# check.update(dict.fromkeys(arcsvariable))
if not domain:
return False
return True
class Solver(object):
"""
Abstract base class for solvers
@sort: getSolution, getSolutions, getSolutionIter
"""
def getSolution(self, domains, constraints, vconstraints):
"""
Return one solution for the given problem
@param domains: Dictionary mapping variables to their domains
@type domains: dict
@param constraints: List of pairs of (constraint, variables)
@type constraints: list
@param vconstraints: Dictionary mapping variables to a list of
constraints affecting the given variables.
@type vconstraints: dict
"""
raise NotImplementedError, \
"%s is an abstract class" % self.__class__.__name__
def getSolutions(self, domains, constraints, vconstraints):
"""
Return all solutions for the given problem
@param domains: Dictionary mapping variables to domains
@type domains: dict
@param constraints: List of pairs of (constraint, variables)
@type constraints: list
@param vconstraints: Dictionary mapping variables to a list of
constraints affecting the given variables.
@type vconstraints: dict
"""
raise NotImplementedError, \
"%s provides only a single solution" % self.__class__.__name__
def getSolutionIter(self, domains, constraints, vconstraints):
"""
Return an iterator for the solutions of the given problem
@param domains: Dictionary mapping variables to domains
@type domains: dict
@param constraints: List of pairs of (constraint, variables)
@type constraints: list
@param vconstraints: Dictionary mapping variables to a list of
constraints affecting the given variables.
@type vconstraints: dict
"""
raise NotImplementedError, \
"%s doesn't provide iteration" % self.__class__.__name__
class BacktrackingSolver(Solver):
"""
Problem solver with backtracking capabilities
Examples:
>>> result = [[('a', 1), ('b', 2)],
... [('a', 1), ('b', 3)],
... [('a', 2), ('b', 3)]]
>>> problem = Problem(BacktrackingSolver())
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> problem.addConstraint(lambda a, b: b > a, ["a", "b"])
>>> solution = problem.getSolution()
>>> sorted(solution.items()) in result
True
>>> for solution in problem.getSolutionIter():
... sorted(solution.items()) in result
True
True
True
>>> for solution in problem.getSolutions():
... sorted(solution.items()) in result
True
True
True
"""#"""
def __init__(self, forwardcheck=True):
"""
@param forwardcheck: If false forward checking will not be requested
to constraints while looking for solutions
(default is true)
@type forwardcheck: bool
"""
self._forwardcheck = forwardcheck
def getSolutionIter(self, domains, constraints, vconstraints):
forwardcheck = self._forwardcheck
assignments = {}
queue = []
while True:
# Mix the Degree and Minimum Remaing Values (MRV) heuristics
lst = [(-len(vconstraints[variable]),
len(domains[variable]), variable) for variable in domains]
lst.sort()
for item in lst:
if item[-1] not in assignments:
# Found unassigned variable
variable = item[-1]
values = domains[variable][:]
if forwardcheck:
pushdomains = [domains[x] for x in domains
if x not in assignments and
x != variable]
else:
pushdomains = None
break
else:
# No unassigned variables. We've got a solution. Go back
# to last variable, if there's one.
yield assignments.copy()
if not queue:
return
variable, values, pushdomains = queue.pop()
if pushdomains:
for domain in pushdomains:
domain.popState()
while True:
# We have a variable. Do we have any values left?
if not values:
# No. Go back to last variable, if there's one.
del assignments[variable]
while queue:
variable, values, pushdomains = queue.pop()
if pushdomains:
for domain in pushdomains:
domain.popState()
if values:
break
del assignments[variable]
else:
return
# Got a value. Check it.
assignments[variable] = values.pop()
if pushdomains:
for domain in pushdomains:
domain.pushState()
for constraint, variables in vconstraints[variable]:
if not constraint(variables, domains, assignments,
pushdomains):
# Value is not good.
break
else:
break
if pushdomains:
for domain in pushdomains:
domain.popState()
# Push state before looking for next variable.
queue.append((variable, values, pushdomains))
raise RuntimeError, "Can't happen"
def getSolution(self, domains, constraints, vconstraints):
iter = self.getSolutionIter(domains, constraints, vconstraints)
try:
return iter.next()
except StopIteration:
return None
def getSolutions(self, domains, constraints, vconstraints):
return list(self.getSolutionIter(domains, constraints, vconstraints))
class RecursiveBacktrackingSolver(Solver):
"""
Recursive problem solver with backtracking capabilities
Examples:
>>> result = [[('a', 1), ('b', 2)],
... [('a', 1), ('b', 3)],
... [('a', 2), ('b', 3)]]
>>> problem = Problem(RecursiveBacktrackingSolver())
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> problem.addConstraint(lambda a, b: b > a, ["a", "b"])
>>> solution = problem.getSolution()
>>> sorted(solution.items()) in result
True
>>> for solution in problem.getSolutions():
... sorted(solution.items()) in result
True
True
True
>>> problem.getSolutionIter()
Traceback (most recent call last):
...
NotImplementedError: RecursiveBacktrackingSolver doesn't provide iteration
"""#"""
def __init__(self, forwardcheck=True):
"""
@param forwardcheck: If false forward checking will not be requested
to constraints while looking for solutions
(default is true)
@type forwardcheck: bool
"""
self._forwardcheck = forwardcheck
def recursiveBacktracking(self, solutions, domains, vconstraints,
assignments, single):
# Mix the Degree and Minimum Remaing Values (MRV) heuristics
lst = [(-len(vconstraints[variable]),
len(domains[variable]), variable) for variable in domains]
lst.sort()
for item in lst:
if item[-1] not in assignments:
# Found an unassigned variable. Let's go.
break
else:
# No unassigned variables. We've got a solution.
solutions.append(assignments.copy())
return solutions
variable = item[-1]
assignments[variable] = None
forwardcheck = self._forwardcheck
if forwardcheck:
pushdomains = [domains[x] for x in domains if x not in assignments]
else:
pushdomains = None
for value in domains[variable]:
assignments[variable] = value
if pushdomains:
for domain in pushdomains:
domain.pushState()
for constraint, variables in vconstraints[variable]:
if not constraint(variables, domains, assignments,
pushdomains):
# Value is not good.
break
else:
# Value is good. Recurse and get next variable.
self.recursiveBacktracking(solutions, domains, vconstraints,
assignments, single)
if solutions and single:
return solutions
if pushdomains:
for domain in pushdomains:
domain.popState()
del assignments[variable]
return solutions
def getSolution(self, domains, constraints, vconstraints):
solutions = self.recursiveBacktracking([], domains, vconstraints,
{}, True)
return solutions and solutions[0] or None
def getSolutions(self, domains, constraints, vconstraints):
return self.recursiveBacktracking([], domains, vconstraints,
{}, False)
class MinConflictsSolver(Solver):
"""
Problem solver based on the minimum conflicts theory
Examples:
>>> result = [[('a', 1), ('b', 2)],
... [('a', 1), ('b', 3)],
... [('a', 2), ('b', 3)]]
>>> problem = Problem(MinConflictsSolver())
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> problem.addConstraint(lambda a, b: b > a, ["a", "b"])
>>> solution = problem.getSolution()
>>> sorted(solution.items()) in result
True
>>> problem.getSolutions()
Traceback (most recent call last):
...
NotImplementedError: MinConflictsSolver provides only a single solution
>>> problem.getSolutionIter()
Traceback (most recent call last):
...
NotImplementedError: MinConflictsSolver doesn't provide iteration
"""#"""
def __init__(self, steps=1000):
"""
@param steps: Maximum number of steps to perform before giving up
when looking for a solution (default is 1000)
@type steps: int
"""
self._steps = steps
def getSolution(self, domains, constraints, vconstraints):
assignments = {}
# Initial assignment
for variable in domains:
assignments[variable] = random.choice(domains[variable])
for _ in xrange(self._steps):
conflicted = False
lst = domains.keys()
random.shuffle(lst)
for variable in lst:
# Check if variable is not in conflict
for constraint, variables in vconstraints[variable]:
if not constraint(variables, domains, assignments):
break
else:
continue
# Variable has conflicts. Find values with less conflicts.
mincount = len(vconstraints[variable])
minvalues = []
for value in domains[variable]:
assignments[variable] = value
count = 0
for constraint, variables in vconstraints[variable]:
if not constraint(variables, domains, assignments):
count += 1
if count == mincount:
minvalues.append(value)
elif count < mincount:
mincount = count
del minvalues[:]
minvalues.append(value)
# Pick a random one from these values.
assignments[variable] = random.choice(minvalues)
conflicted = True
if not conflicted:
return assignments
return None
# ----------------------------------------------------------------------
# Variables
# ----------------------------------------------------------------------
class Variable(object):
"""
Helper class for variable definition
Using this class is optional, since any hashable object,
including plain strings and integers, may be used as variables.
"""
def __init__(self, name):
"""
@param name: Generic variable name for problem-specific purposes
@type name: string
"""
self.name = name
def __repr__(self):
return self.name
Unassigned = Variable("Unassigned")
# ----------------------------------------------------------------------
# Domains
# ----------------------------------------------------------------------
class Domain(list):
"""
Class used to control possible values for variables
When list or tuples are used as domains, they are automatically
converted to an instance of that class.
"""
def __init__(self, set):
"""
@param set: Set of values that the given variables may assume
@type set: set of objects comparable by equality
"""
list.__init__(self, set)
self._hidden = []
self._states = []
def resetState(self):
"""
Reset to the original domain state, including all possible values
"""
self.extend(self._hidden)
del self._hidden[:]
del self._states[:]
def pushState(self):
"""
Save current domain state
Variables hidden after that call are restored when that state
is popped from the stack.
"""
self._states.append(len(self))
def popState(self):
"""
Restore domain state from the top of the stack
Variables hidden since the last popped state are then available
again.
"""
diff = self._states.pop()-len(self)
if diff:
self.extend(self._hidden[-diff:])
del self._hidden[-diff:]
def hideValue(self, value):
"""
Hide the given value from the domain
After that call the given value won't be seen as a possible value
on that domain anymore. The hidden value will be restored when the
previous saved state is popped.
@param value: Object currently available in the domain
"""
list.remove(self, value)
self._hidden.append(value)
# ----------------------------------------------------------------------
# Constraints
# ----------------------------------------------------------------------
class Constraint(object):
"""
Abstract base class for constraints
"""
def __call__(self, variables, domains, assignments, forwardcheck=False):
"""
Perform the constraint checking
If the forwardcheck parameter is not false, besides telling if
the constraint is currently broken or not, the constraint
implementation may choose to hide values from the domains of
unassigned variables to prevent them from being used, and thus
prune the search space.
@param variables: Variables affected by that constraint, in the
same order provided by the user
@type variables: sequence
@param domains: Dictionary mapping variables to their domains
@type domains: dict
@param assignments: Dictionary mapping assigned variables to their
current assumed value
@type assignments: dict
@param forwardcheck: Boolean value stating whether forward checking
should be performed or not
@return: Boolean value stating if this constraint is currently
broken or not
@rtype: bool
"""#"""
return True
def preProcess(self, variables, domains, constraints, vconstraints):
"""
Preprocess variable domains
This method is called before starting to look for solutions,
and is used to prune domains with specific constraint logic
when possible. For instance, any constraints with a single
variable may be applied on all possible values and removed,
since they may act on individual values even without further
knowledge about other assignments.
@param variables: Variables affected by that constraint, in the
same order provided by the user
@type variables: sequence
@param domains: Dictionary mapping variables to their domains
@type domains: dict
@param constraints: List of pairs of (constraint, variables)
@type constraints: list
@param vconstraints: Dictionary mapping variables to a list of
constraints affecting the given variables.
@type vconstraints: dict
"""#"""
if len(variables) == 1:
variable = variables[0]
domain = domains[variable]
for value in domain[:]:
if not self(variables, domains, {variable: value}):
domain.remove(value)
constraints.remove((self, variables))
vconstraints[variable].remove((self, variables))
def forwardCheck(self, variables, domains, assignments,
_unassigned=Unassigned):
"""
Helper method for generic forward checking
Currently, this method acts only when there's a single
unassigned variable.
@param variables: Variables affected by that constraint, in the
same order provided by the user
@type variables: sequence
@param domains: Dictionary mapping variables to their domains
@type domains: dict
@param assignments: Dictionary mapping assigned variables to their
current assumed value
@type assignments: dict
@return: Boolean value stating if this constraint is currently
broken or not
@rtype: bool
"""#"""
unassignedvariable = _unassigned
for variable in variables:
if variable not in assignments:
if unassignedvariable is _unassigned:
unassignedvariable = variable
else:
break
else:
if unassignedvariable is not _unassigned:
# Remove from the unassigned variable domain's all
# values which break our variable's constraints.
domain = domains[unassignedvariable]
if domain:
for value in domain[:]:
assignments[unassignedvariable] = value
if not self(variables, domains, assignments):
domain.hideValue(value)
del assignments[unassignedvariable]
if not domain:
return False
return True
class FunctionConstraint(Constraint):
"""
Constraint which wraps a function defining the constraint logic
Examples:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> def func(a, b):
... return b > a
>>> problem.addConstraint(func, ["a", "b"])
>>> problem.getSolution()
{'a': 1, 'b': 2}
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> def func(a, b):
... return b > a
>>> problem.addConstraint(FunctionConstraint(func), ["a", "b"])
>>> problem.getSolution()
{'a': 1, 'b': 2}
"""#"""
def __init__(self, func, assigned=True):
"""
@param func: Function wrapped and queried for constraint logic
@type func: callable object
@param assigned: Whether the function may receive unassigned
variables or not
@type assigned: bool
"""
self._func = func
self._assigned = assigned
def __call__(self, variables, domains, assignments, forwardcheck=False,
_unassigned=Unassigned):
parms = [assignments.get(x, _unassigned) for x in variables]
missing = parms.count(_unassigned)
if missing:
return ((self._assigned or self._func(*parms)) and
(not forwardcheck or missing != 1 or
self.forwardCheck(variables, domains, assignments)))
return self._func(*parms)
class AllDifferentConstraint(Constraint):
"""
Constraint enforcing that values of all given variables are different
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(AllDifferentConstraint())
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 2)], [('a', 2), ('b', 1)]]
"""#"""
def __call__(self, variables, domains, assignments, forwardcheck=False,
_unassigned=Unassigned):
seen = {}
for variable in variables:
value = assignments.get(variable, _unassigned)
if value is not _unassigned:
if value in seen:
return False
seen[value] = True
if forwardcheck:
for variable in variables:
if variable not in assignments:
domain = domains[variable]
for value in seen:
if value in domain:
domain.hideValue(value)
if not domain:
return False
return True
class AllEqualConstraint(Constraint):
"""
Constraint enforcing that values of all given variables are equal
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(AllEqualConstraint())
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 1)], [('a', 2), ('b', 2)]]
"""#"""
def __call__(self, variables, domains, assignments, forwardcheck=False,
_unassigned=Unassigned):
singlevalue = _unassigned
for value in assignments.values():
if singlevalue is _unassigned:
singlevalue = value
elif value != singlevalue:
return False
if forwardcheck and singlevalue is not _unassigned:
for variable in variables:
if variable not in assignments:
domain = domains[variable]
if singlevalue not in domain:
return False
for value in domain[:]:
if value != singlevalue:
domain.hideValue(value)
return True
class MaxSumConstraint(Constraint):
"""
Constraint enforcing that values of given variables sum up to
a given amount
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(MaxSumConstraint(3))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 1)], [('a', 1), ('b', 2)], [('a', 2), ('b', 1)]]
"""#"""
def __init__(self, maxsum, multipliers=None):
"""
@param maxsum: Value to be considered as the maximum sum
@type maxsum: number
@param multipliers: If given, variable values will be multiplied by
the given factors before being summed to be checked
@type multipliers: sequence of numbers
"""
self._maxsum = maxsum
self._multipliers = multipliers
def preProcess(self, variables, domains, constraints, vconstraints):
Constraint.preProcess(self, variables, domains,
constraints, vconstraints)
multipliers = self._multipliers
maxsum = self._maxsum
if multipliers:
for variable, multiplier in zip(variables, multipliers):
domain = domains[variable]
for value in domain[:]:
if value*multiplier > maxsum:
domain.remove(value)
else:
for variable in variables:
domain = domains[variable]
for value in domain[:]:
if value > maxsum:
domain.remove(value)
def __call__(self, variables, domains, assignments, forwardcheck=False):
multipliers = self._multipliers
maxsum = self._maxsum
sum = 0
if multipliers:
for variable, multiplier in zip(variables, multipliers):
if variable in assignments:
sum += assignments[variable]*multiplier
if type(sum) is float:
sum = round(sum, 10)
if sum > maxsum:
return False
if forwardcheck:
for variable, multiplier in zip(variables, multipliers):
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if sum+value*multiplier > maxsum:
domain.hideValue(value)
if not domain:
return False
else:
for variable in variables:
if variable in assignments:
sum += assignments[variable]
if type(sum) is float:
sum = round(sum, 10)
if sum > maxsum:
return False
if forwardcheck:
for variable in variables:
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if sum+value > maxsum:
domain.hideValue(value)
if not domain:
return False
return True
class ExactSumConstraint(Constraint):
"""
Constraint enforcing that values of given variables sum exactly
to a given amount
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(ExactSumConstraint(3))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 2)], [('a', 2), ('b', 1)]]
"""#"""
def __init__(self, exactsum, multipliers=None):
"""
@param exactsum: Value to be considered as the exact sum
@type exactsum: number
@param multipliers: If given, variable values will be multiplied by
the given factors before being summed to be checked
@type multipliers: sequence of numbers
"""
self._exactsum = exactsum
self._multipliers = multipliers
def preProcess(self, variables, domains, constraints, vconstraints):
Constraint.preProcess(self, variables, domains,
constraints, vconstraints)
multipliers = self._multipliers
exactsum = self._exactsum
if multipliers:
for variable, multiplier in zip(variables, multipliers):
domain = domains[variable]
for value in domain[:]:
if value*multiplier > exactsum:
domain.remove(value)
else:
for variable in variables:
domain = domains[variable]
for value in domain[:]:
if value > exactsum:
domain.remove(value)
def __call__(self, variables, domains, assignments, forwardcheck=False):
multipliers = self._multipliers
exactsum = self._exactsum
sum = 0
missing = False
if multipliers:
for variable, multiplier in zip(variables, multipliers):
if variable in assignments:
sum += assignments[variable]*multiplier
else:
missing = True
if type(sum) is float:
sum = round(sum, 10)
if sum > exactsum:
return False
if forwardcheck and missing:
for variable, multiplier in zip(variables, multipliers):
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if sum+value*multiplier > exactsum:
domain.hideValue(value)
if not domain:
return False
else:
for variable in variables:
if variable in assignments:
sum += assignments[variable]
else:
missing = True
if type(sum) is float:
sum = round(sum, 10)
if sum > exactsum:
return False
if forwardcheck and missing:
for variable in variables:
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if sum+value > exactsum:
domain.hideValue(value)
if not domain:
return False
if missing:
return sum <= exactsum
else:
return sum == exactsum
class MinSumConstraint(Constraint):
"""
Constraint enforcing that values of given variables sum at least
to a given amount
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(MinSumConstraint(3))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 2)], [('a', 2), ('b', 1)], [('a', 2), ('b', 2)]]
"""#"""
def __init__(self, minsum, multipliers=None):
"""
@param minsum: Value to be considered as the minimum sum
@type minsum: number
@param multipliers: If given, variable values will be multiplied by
the given factors before being summed to be checked
@type multipliers: sequence of numbers
"""
self._minsum = minsum
self._multipliers = multipliers
def __call__(self, variables, domains, assignments, forwardcheck=False):
for variable in variables:
if variable not in assignments:
return True
else:
multipliers = self._multipliers
minsum = self._minsum
sum = 0
if multipliers:
for variable, multiplier in zip(variables, multipliers):
sum += assignments[variable]*multiplier
else:
for variable in variables:
sum += assignments[variable]
if type(sum) is float:
sum = round(sum, 10)
return sum >= minsum
class InSetConstraint(Constraint):
"""
Constraint enforcing that values of given variables are present in
the given set
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(InSetConstraint([1]))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 1)]]
"""#"""
def __init__(self, set):
"""
@param set: Set of allowed values
@type set: set
"""
self._set = set
def __call__(self, variables, domains, assignments, forwardcheck=False):
# preProcess() will remove it.
raise RuntimeError, "Can't happen"
def preProcess(self, variables, domains, constraints, vconstraints):
set = self._set
for variable in variables:
domain = domains[variable]
for value in domain[:]:
if value not in set:
domain.remove(value)
vconstraints[variable].remove((self, variables))
constraints.remove((self, variables))
class NotInSetConstraint(Constraint):
"""
Constraint enforcing that values of given variables are not present in
the given set
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(NotInSetConstraint([1]))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 2), ('b', 2)]]
"""#"""
def __init__(self, set):
"""
@param set: Set of disallowed values
@type set: set
"""
self._set = set
def __call__(self, variables, domains, assignments, forwardcheck=False):
# preProcess() will remove it.
raise RuntimeError, "Can't happen"
def preProcess(self, variables, domains, constraints, vconstraints):
set = self._set
for variable in variables:
domain = domains[variable]
for value in domain[:]:
if value in set:
domain.remove(value)
vconstraints[variable].remove((self, variables))
constraints.remove((self, variables))
class SomeInSetConstraint(Constraint):
"""
Constraint enforcing that at least some of the values of given
variables must be present in a given set
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(SomeInSetConstraint([1]))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 1)], [('a', 1), ('b', 2)], [('a', 2), ('b', 1)]]
"""#"""
def __init__(self, set, n=1, exact=False):
"""
@param set: Set of values to be checked
@type set: set
@param n: Minimum number of assigned values that should be present
in set (default is 1)
@type n: int
@param exact: Whether the number of assigned values which are
present in set must be exactly C{n}
@type exact: bool
"""
self._set = set
self._n = n
self._exact = exact
def __call__(self, variables, domains, assignments, forwardcheck=False):
set = self._set
missing = 0
found = 0
for variable in variables:
if variable in assignments:
found += assignments[variable] in set
else:
missing += 1
if missing:
if self._exact:
if not (found <= self._n <= missing+found):
return False
else:
if self._n > missing+found:
return False
if forwardcheck and self._n-found == missing:
# All unassigned variables must be assigned to
# values in the set.
for variable in variables:
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if value not in set:
domain.hideValue(value)
if not domain:
return False
else:
if self._exact:
if found != self._n:
return False
else:
if found < self._n:
return False
return True
class SomeNotInSetConstraint(Constraint):
"""
Constraint enforcing that at least some of the values of given
variables must not be present in a given set
Example:
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2])
>>> problem.addConstraint(SomeNotInSetConstraint([1]))
>>> sorted(sorted(x.items()) for x in problem.getSolutions())
[[('a', 1), ('b', 2)], [('a', 2), ('b', 1)], [('a', 2), ('b', 2)]]
"""#"""
def __init__(self, set, n=1, exact=False):
"""
@param set: Set of values to be checked
@type set: set
@param n: Minimum number of assigned values that should not be present
in set (default is 1)
@type n: int
@param exact: Whether the number of assigned values which are
not present in set must be exactly C{n}
@type exact: bool
"""
self._set = set
self._n = n
self._exact = exact
def __call__(self, variables, domains, assignments, forwardcheck=False):
set = self._set
missing = 0
found = 0
for variable in variables:
if variable in assignments:
found += assignments[variable] not in set
else:
missing += 1
if missing:
if self._exact:
if not (found <= self._n <= missing+found):
return False
else:
if self._n > missing+found:
return False
if forwardcheck and self._n-found == missing:
# All unassigned variables must be assigned to
# values not in the set.
for variable in variables:
if variable not in assignments:
domain = domains[variable]
for value in domain[:]:
if value in set:
domain.hideValue(value)
if not domain:
return False
else:
if self._exact:
if found != self._n:
return False
else:
if found < self._n:
return False
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
|
samples/classification.py | dimdano/flops-counter.pytorch | 1,932 | 12612362 | import argparse
import sys
import torch
import torchvision.models as models
from ptflops import get_model_complexity_info
pt_models = {'resnet18': models.resnet18,
'resnet50': models.resnet50,
'alexnet': models.alexnet,
'vgg16': models.vgg16,
'squeezenet': models.squeezenet1_0,
'densenet': models.densenet161,
'inception': models.inception_v3}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ptflops sample script')
parser.add_argument('--device', type=int, default=0,
help='Device to store the model.')
parser.add_argument('--model', choices=list(pt_models.keys()),
type=str, default='resnet18')
parser.add_argument('--result', type=str, default=None)
args = parser.parse_args()
if args.result is None:
ost = sys.stdout
else:
ost = open(args.result, 'w')
net = pt_models[args.model]()
if torch.cuda.is_available():
net.cuda(device=args.device)
macs, params = get_model_complexity_info(net, (3, 224, 224),
as_strings=True,
print_per_layer_stat=True,
ost=ost)
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
|
gpytorch/lazy/identity_lazy_tensor.py | llguo95/gpytorch | 188 | 12612363 | <gh_stars>100-1000
#!/usr/bin/env python3
from typing import Optional, Tuple
import torch
from torch import Tensor
from ..utils.broadcasting import _mul_broadcast_shape
from ..utils.getitem import _compute_getitem_size, _is_noop_index
from ..utils.memoize import cached
from .diag_lazy_tensor import ConstantDiagLazyTensor
from .lazy_tensor import LazyTensor
from .zero_lazy_tensor import ZeroLazyTensor
class IdentityLazyTensor(ConstantDiagLazyTensor):
def __init__(self, diag_shape, batch_shape=torch.Size([]), dtype=None, device=None):
"""
Identity matrix lazy tensor. Supports arbitrary batch sizes.
Args:
:attr:`diag` (Tensor):
A `b1 x ... x bk x n` Tensor, representing a `b1 x ... x bk`-sized batch
of `n x n` identity matrices
"""
one = torch.tensor(1.0, dtype=dtype, device=device)
LazyTensor.__init__(self, diag_shape=diag_shape, batch_shape=batch_shape, dtype=dtype, device=device)
self.diag_values = one.expand(torch.Size([*batch_shape, 1]))
self.diag_shape = diag_shape
self._batch_shape = batch_shape
self._dtype = dtype
self._device = device
@property
def batch_shape(self):
"""
Returns the shape over which the tensor is batched.
"""
return self._batch_shape
@property
def dtype(self):
return self._dtype
@property
def device(self):
return self._device
def _maybe_reshape_rhs(self, rhs):
if self._batch_shape != rhs.shape[:-2]:
batch_shape = _mul_broadcast_shape(rhs.shape[:-2], self._batch_shape)
return rhs.expand(*batch_shape, *rhs.shape[-2:])
else:
return rhs
@cached(name="cholesky", ignore_args=True)
def _cholesky(self, upper=False):
return self
def _cholesky_solve(self, rhs):
return self._maybe_reshape_rhs(rhs)
def _expand_batch(self, batch_shape):
return IdentityLazyTensor(
diag_shape=self.diag_shape, batch_shape=batch_shape, dtype=self.dtype, device=self.device
)
def _getitem(self, row_index, col_index, *batch_indices):
# Special case: if both row and col are not indexed, then we are done
if _is_noop_index(row_index) and _is_noop_index(col_index):
if len(batch_indices):
new_batch_shape = _compute_getitem_size(self, (*batch_indices, row_index, col_index))[:-2]
res = IdentityLazyTensor(
diag_shape=self.diag_shape, batch_shape=new_batch_shape, dtype=self._dtype, device=self._device
)
return res
else:
return self
return super()._getitem(row_index, col_index, *batch_indices)
def _matmul(self, rhs):
return self._maybe_reshape_rhs(rhs)
def _mul_constant(self, constant):
return ConstantDiagLazyTensor(self.diag_values * constant, diag_shape=self.diag_shape)
def _mul_matrix(self, other):
return other
def _permute_batch(self, *dims):
batch_shape = self.diag_values.permute(*dims, -1).shape[:-1]
return IdentityLazyTensor(
diag_shape=self.diag_shape, batch_shape=batch_shape, dtype=self._dtype, device=self._device
)
def _prod_batch(self, dim):
batch_shape = list(self.batch_shape)
del batch_shape[dim]
return IdentityLazyTensor(
diag_shape=self.diag_shape, batch_shape=torch.Size(batch_shape), dtype=self.dtype, device=self.device
)
def _root_decomposition(self):
return self.sqrt()
def _root_inv_decomposition(self, initial_vectors=None):
return self.inverse().sqrt()
def _size(self):
return torch.Size([*self._batch_shape, self.diag_shape, self.diag_shape])
def _t_matmul(self, rhs):
return self._maybe_reshape_rhs(rhs)
def _transpose_nonbatch(self):
return self
def abs(self):
return self
def exp(self):
return self
def inverse(self):
return self
def inv_matmul(self, right_tensor, left_tensor=None):
res = self._maybe_reshape_rhs(right_tensor)
if left_tensor is not None:
res = left_tensor @ res
return res
def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True):
# TODO: Use proper batching for inv_quad_rhs (prepand to shape rathern than append)
if inv_quad_rhs is None:
inv_quad_term = torch.empty(0, dtype=self.dtype, device=self.device)
else:
rhs_batch_shape = inv_quad_rhs.shape[1 + self.batch_dim :]
inv_quad_term = inv_quad_rhs.mul(inv_quad_rhs).sum(-(1 + len(rhs_batch_shape)))
if reduce_inv_quad:
inv_quad_term = inv_quad_term.sum(-1)
if logdet:
logdet_term = torch.zeros(self.batch_shape, dtype=self.dtype, device=self.device)
else:
logdet_term = torch.empty(0, dtype=self.dtype, device=self.device)
return inv_quad_term, logdet_term
def log(self):
return ZeroLazyTensor(
*self._batch_shape, self.diag_shape, self.diag_shape, dtype=self._dtype, device=self._device
)
def matmul(self, other):
is_vec = False
if other.dim() == 1:
is_vec = True
other = other.unsqueeze(-1)
res = self._maybe_reshape_rhs(other)
if is_vec:
res = res.squeeze(-1)
return res
def sqrt(self):
return self
def sqrt_inv_matmul(self, rhs, lhs=None):
if lhs is None:
return self._maybe_reshape_rhs(rhs)
else:
sqrt_inv_matmul = lhs @ rhs
inv_quad = lhs.pow(2).sum(dim=-1)
return sqrt_inv_matmul, inv_quad
def type(self, dtype):
"""
This method operates similarly to :func:`torch.Tensor.type`.
"""
return IdentityLazyTensor(
diag_shape=self.diag_shape, batch_shape=self.batch_shape, dtype=dtype, device=self.device
)
def zero_mean_mvn_samples(self, num_samples):
base_samples = torch.randn(num_samples, *self.shape[:-1], dtype=self.dtype, device=self.device)
return base_samples
@cached(name="svd")
def _svd(self) -> Tuple[LazyTensor, Tensor, LazyTensor]:
return self, self._diag, self
def _symeig(self, eigenvectors: bool = False) -> Tuple[Tensor, Optional[LazyTensor]]:
return self._diag, self
|
zerver/webhooks/transifex/view.py | BillyMagarali/zulip | 17,004 | 12612369 | # Webhooks for external integrations.
from typing import Optional
from django.http import HttpRequest, HttpResponse
from zerver.decorator import webhook_view
from zerver.lib.exceptions import UnsupportedWebhookEventType
from zerver.lib.request import REQ, has_request_variables
from zerver.lib.response import json_success
from zerver.lib.validator import check_int
from zerver.lib.webhooks.common import check_send_webhook_message
from zerver.models import UserProfile
All_EVENT_TYPES = ["translated", "review"]
@webhook_view("Transifex", notify_bot_owner_on_invalid_json=False, all_event_types=All_EVENT_TYPES)
@has_request_variables
def api_transifex_webhook(
request: HttpRequest,
user_profile: UserProfile,
project: str = REQ(),
resource: str = REQ(),
language: str = REQ(),
translated: Optional[int] = REQ(json_validator=check_int, default=None),
reviewed: Optional[int] = REQ(json_validator=check_int, default=None),
) -> HttpResponse:
subject = f"{project} in {language}"
if translated:
event = "translated"
body = f"Resource {resource} fully translated."
elif reviewed:
event = "review"
body = f"Resource {resource} fully reviewed."
else:
raise UnsupportedWebhookEventType("Unknown Event Type")
check_send_webhook_message(request, user_profile, subject, body, event)
return json_success()
|
smtbx/ab_initio/development/electron_density_distribution.py | dperl-sol/cctbx_project | 155 | 12612371 | from __future__ import absolute_import, division, print_function
from scitbx.array_family import flex
def find_delta(rho_map, tol):
""" Find delta as hinted on fig. 1 of ref. [1] in module charge_flipping """
rho = rho_map.real_map_unpadded().as_1d()
max_rho = flex.max(rho)
rho /= max_rho
sorting = flex.sort_permutation(rho)
sorted_rho = rho.select(sorting)
n = len(sorted_rho)
p,q = n//4, 3*n//4
indexes = flex.double_range(p,q)
values = sorted_rho[p:q]
c = flex.linear_correlation(indexes, values)
assert c.is_well_defined() and c.coefficient() > 0.99
r = flex.linear_regression(indexes, values)
a,b = r.y_intercept(), r.slope()
deviation = flex.abs(a + b*flex.double_range(n) - sorted_rho)
non_linear_sel = deviation > tol
low = flex.first_index(non_linear_sel, False)
high = flex.last_index(non_linear_sel, False)
assert non_linear_sel[low:high].count(False)/(high-low+1) > 0.99
assert sorted_rho[low] < 0 and sorted_rho[high] > 0
return min(sorted_rho[high], -sorted_rho[low]), max_rho
def write_sorted_moduli_as_mathematica_plot(f, filename):
""" To obtain fig. 1 in ref [2] in module charge_flipping """
abs_f = flex.abs(f.data())
sorted = abs_f.select(flex.sort_permutation(abs_f))
sorted /= flex.max(sorted)
mf = open(os.path.expanduser(filename), 'w')
print('fp1 = {', file=mf)
for f in sorted:
print("%f, " % f, file=mf)
print("1 };", file=mf)
print("ListPlot[fp1]", file=mf)
mf.close()
|
tests/transport/test_tcp.py | g-r-a-n-t/py-libp2p | 315 | 12612383 | from multiaddr import Multiaddr
import pytest
import trio
from libp2p.network.connection.raw_connection import RawConnection
from libp2p.tools.constants import LISTEN_MADDR
from libp2p.transport.exceptions import OpenConnectionError
from libp2p.transport.tcp.tcp import TCP
@pytest.mark.trio
async def test_tcp_listener(nursery):
transport = TCP()
async def handler(tcp_stream):
pass
listener = transport.create_listener(handler)
assert len(listener.get_addrs()) == 0
await listener.listen(LISTEN_MADDR, nursery)
assert len(listener.get_addrs()) == 1
await listener.listen(LISTEN_MADDR, nursery)
assert len(listener.get_addrs()) == 2
@pytest.mark.trio
async def test_tcp_dial(nursery):
transport = TCP()
raw_conn_other_side = None
event = trio.Event()
async def handler(tcp_stream):
nonlocal raw_conn_other_side
raw_conn_other_side = RawConnection(tcp_stream, False)
event.set()
await trio.sleep_forever()
# Test: `OpenConnectionError` is raised when trying to dial to a port which
# no one is not listening to.
with pytest.raises(OpenConnectionError):
await transport.dial(Multiaddr("/ip4/127.0.0.1/tcp/1"))
listener = transport.create_listener(handler)
await listener.listen(LISTEN_MADDR, nursery)
addrs = listener.get_addrs()
assert len(addrs) == 1
listen_addr = addrs[0]
raw_conn = await transport.dial(listen_addr)
await event.wait()
data = b"123"
await raw_conn_other_side.write(data)
assert (await raw_conn.read(len(data))) == data
|
ui/webui/resources/js/cr/ui/compiled_resources2.gyp | google-ar/chromium | 777 | 12612385 | <filename>ui/webui/resources/js/cr/ui/compiled_resources2.gyp
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'targets': [
{
'target_name': 'alert_overlay',
'dependencies': [
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:util',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'array_data_model',
'dependencies': [
'../../compiled_resources2.gyp:cr',
'../compiled_resources2.gyp:event_target',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'command',
'dependencies': [
'../../compiled_resources2.gyp:cr',
'../compiled_resources2.gyp:ui',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'drag_wrapper',
'dependencies': [
'../../compiled_resources2.gyp:assert',
'../../compiled_resources2.gyp:cr',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'focus_grid',
'dependencies': [
'../../compiled_resources2.gyp:assert',
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:event_tracker',
'focus_row',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'focus_manager',
'dependencies': ['../../compiled_resources2.gyp:cr'],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'focus_outline_manager',
'dependencies': ['../../compiled_resources2.gyp:cr'],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'focus_row',
'dependencies': [
'../../compiled_resources2.gyp:assert',
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:event_tracker',
'../../compiled_resources2.gyp:util',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'menu_button',
'dependencies': [
'../../compiled_resources2.gyp:assert',
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:event_tracker',
'../compiled_resources2.gyp:ui',
'menu',
'menu_item',
'position_util',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'menu_item',
'dependencies': [
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:load_time_data',
'../compiled_resources2.gyp:ui',
'command',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'menu',
'dependencies': [
'../../compiled_resources2.gyp:assert',
'../../compiled_resources2.gyp:cr',
'../compiled_resources2.gyp:ui',
'menu_item',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'overlay',
'dependencies': [
'../../compiled_resources2.gyp:cr',
'../../compiled_resources2.gyp:util',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
{
'target_name': 'position_util',
'dependencies': [
'../../compiled_resources2.gyp:cr',
],
'includes': ['../../../../../../third_party/closure_compiler/compile_js2.gypi'],
},
],
}
|
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