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How to use _query_count method in Playwright Python

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privacy_ledger.py

Source:privacy_ledger.py

1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14"""PrivacyLedger class for keeping a record of private queries.15"""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import collections20import tensorflow as tf21from privacy.analysis import tensor_buffer22from privacy.optimizers import dp_query23nest = tf.contrib.framework.nest24SampleEntry = collections.namedtuple(  # pylint: disable=invalid-name25    'SampleEntry', ['population_size', 'selection_probability', 'queries'])26GaussianSumQueryEntry = collections.namedtuple(  # pylint: disable=invalid-name27    'GaussianSumQueryEntry', ['l2_norm_bound', 'noise_stddev'])28class PrivacyLedger(object):29  """Class for keeping a record of private queries.30  The PrivacyLedger keeps a record of all queries executed over a given dataset31  for the purpose of computing privacy guarantees.32  """33  def __init__(34      self,35      population_size,36      selection_probability,37      max_samples,38      max_queries):39    """Initialize the PrivacyLedger.40    Args:41      population_size: An integer (may be variable) specifying the size of the42        population.43      selection_probability: A float (may be variable) specifying the44        probability each record is included in a sample.45      max_samples: The maximum number of samples. An exception is thrown if46        more than this many samples are recorded.47      max_queries: The maximum number of queries. An exception is thrown if48        more than this many queries are recorded.49    """50    self._population_size = population_size51    self._selection_probability = selection_probability52    # The query buffer stores rows corresponding to GaussianSumQueryEntries.53    self._query_buffer = tensor_buffer.TensorBuffer(54        max_queries, [3], tf.float32, 'query')55    self._sample_var = tf.Variable(56        initial_value=tf.zeros([3]), trainable=False, name='sample')57    # The sample buffer stores rows corresponding to SampleEntries.58    self._sample_buffer = tensor_buffer.TensorBuffer(59        max_samples, [3], tf.float32, 'sample')60    self._sample_count = tf.Variable(61        initial_value=0.0, trainable=False, name='sample_count')62    self._query_count = tf.Variable(63        initial_value=0.0, trainable=False, name='query_count')64    try:65      # Newer versions of TF66      self._cs = tf.CriticalSection()67    except AttributeError:68      # Older versions of TF69      self._cs = tf.contrib.framework.CriticalSection()70  def record_sum_query(self, l2_norm_bound, noise_stddev):71    """Records that a query was issued.72    Args:73      l2_norm_bound: The maximum l2 norm of the tensor group in the query.74      noise_stddev: The standard deviation of the noise applied to the sum.75    Returns:76      An operation recording the sum query to the ledger.77    """78    def _do_record_query():79      with tf.control_dependencies([80          tf.assign(self._query_count, self._query_count + 1)]):81        return self._query_buffer.append(82            [self._sample_count, l2_norm_bound, noise_stddev])83    return self._cs.execute(_do_record_query)84  def finalize_sample(self):85    """Finalizes sample and records sample ledger entry."""86    with tf.control_dependencies([87        tf.assign(88            self._sample_var,89            [self._population_size,90             self._selection_probability,91             self._query_count])]):92      with tf.control_dependencies([93          tf.assign(self._sample_count, self._sample_count + 1),94          tf.assign(self._query_count, 0)]):95        return self._sample_buffer.append(self._sample_var)96  def _format_ledger(self, sample_array, query_array):97    """Converts underlying representation into a list of SampleEntries."""98    samples = []99    query_pos = 0100    sample_pos = 0101    for sample in sample_array:102      num_queries = int(sample[2])103      queries = []104      for _ in range(num_queries):105        query = query_array[query_pos]106        assert int(query[0]) == sample_pos107        queries.append(GaussianSumQueryEntry(*query[1:]))108        query_pos += 1109      samples.append(SampleEntry(sample[0], sample[1], queries))110      sample_pos += 1111    return samples112  def get_formatted_ledger(self, sess):113    """Gets the formatted query ledger.114    Args:115      sess: The tensorflow session in which the ledger was created.116    Returns:117      The query ledger as a list of SampleEntries.118    """119    sample_array = sess.run(self._sample_buffer.values)120    query_array = sess.run(self._query_buffer.values)121    return self._format_ledger(sample_array, query_array)122  def get_formatted_ledger_eager(self):123    """Gets the formatted query ledger.124    Returns:125      The query ledger as a list of SampleEntries.126    """127    sample_array = self._sample_buffer.values.numpy()128    query_array = self._query_buffer.values.numpy()129    return self._format_ledger(sample_array, query_array)130class QueryWithLedger(dp_query.DPQuery):131  """A class for DP queries that record events to a PrivacyLedger.132  QueryWithLedger should be the top-level query in a structure of queries that133  may include sum queries, nested queries, etc. It should simply wrap another134  query and contain a reference to the ledger. Any contained queries (including135  those contained in the leaves of a nested query) should also contain a136  reference to the same ledger object.137  For example usage, see privacy_ledger_test.py.138  """139  def __init__(self, query, ledger):140    """Initializes the QueryWithLedger.141    Args:142      query: The query whose events should be recorded to the ledger. Any143        subqueries (including those in the leaves of a nested query) should144        also contain a reference to the same ledger given here.145      ledger: A PrivacyLedger to which privacy events should be recorded.146    """147    self._query = query148    self._ledger = ledger149  def initial_global_state(self):150    """See base class."""151    return self._query.initial_global_state()152  def derive_sample_params(self, global_state):153    """See base class."""154    return self._query.derive_sample_params(global_state)155  def initial_sample_state(self, global_state, tensors):156    """See base class."""157    return self._query.initial_sample_state(global_state, tensors)158  def accumulate_record(self, params, sample_state, record):159    """See base class."""160    return self._query.accumulate_record(params, sample_state, record)161  def get_noised_result(self, sample_state, global_state):162    """Ensures sample is recorded to the ledger and returns noised result."""163    with tf.control_dependencies(nest.flatten(sample_state)):164      with tf.control_dependencies([self._ledger.finalize_sample()]):...

context.py

Source:context.py

1# Copyright 2016 Google LLC. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#    http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import collections15import time16import flask17from sqlalchemy import event18from scoreboard import main19from scoreboard import models20from scoreboard import utils21app = main.get_app()22DEFAULT_CSP_POLICY = {23        'default-src': ["'self'"],24        'script-src': [25            "'self'",26            "'unsafe-eval'",  # Needed for Charts.js27        ],28        'img-src': [29            "'self'",30            'data:',31        ],32        'object-src': ["'none'"],33        'font-src': [34            "'self'",35            'fonts.gstatic.com',36        ],37        'style-src': [38            "'self'",39            'fonts.googleapis.com',40            "'unsafe-inline'",  # Needed for Charts.js41        ],42        }43_CSP_POLICY_STRING = None44def get_csp_policy():45    global _CSP_POLICY_STRING46    if _CSP_POLICY_STRING is not None:47        return _CSP_POLICY_STRING48    if app.config.get('CSP_POLICY'):49        policy = app.config.get('CSP_POLICY')50    elif app.config.get('EXTEND_CSP_POLICY'):51        policy = collections.defaultdict(list)52        for k, v in DEFAULT_CSP_POLICY.items():53            policy[k] = v54        for k, v in app.config.get('EXTEND_CSP_POLICY').items():55            policy[k].extend(v)56    else:57        policy = DEFAULT_CSP_POLICY58    components = []59    for k, v in policy.items():60        sources = ' '.join(v)61        components.append(k + ' ' + sources)62    _CSP_POLICY_STRING = '; '.join(components)63    return _CSP_POLICY_STRING64# Setup flask.g65@app.before_request66def load_globals():67    """Prepopulate flask.g.* with properties."""68    try:69        del flask.g.user70    except AttributeError:71        pass72    try:73        del flask.g.team74    except AttributeError:75        pass76    if load_apikey():77        return78    if (app.config.get('SESSION_EXPIRATION_SECONDS') and79            flask.session.get('expires') and80            flask.session.get('expires') < time.time()):81        flask.session.clear()82    flask.g.uid = flask.session.get('user')83    flask.g.tid = flask.session.get('team')84    flask.g.admin = flask.session.get('admin') or False85def load_apikey():86    """Load flask.g.user, flask.g.uid from an API key."""87    try:88        key = flask.request.headers.get('X-SCOREBOARD-API-KEY')89        if not key or len(key) != 32:90            return91        user = models.User.get_by_api_key(key)92        if not user:93            return94        flask.g.user = user95        flask.g.uid = user.uid96        flask.g.admin = user.admin97        flask.g.tid = None98        return True99    except Exception:100        # Don't want any API key problems to block requests101        pass102# Add headers to responses103@app.after_request104def add_headers(response):105    """Add security-related headers to all outgoing responses."""106    h = response.headers107    h.setdefault('Content-Security-Policy', get_csp_policy())108    h.setdefault('X-Frame-Options', 'DENY')109    h.add('X-XSS-Protection', '1', mode='block')110    return response111@app.context_processor112def util_contexts():113    return dict(gametime=utils.GameTime)114_query_count = 0115if app.config.get('COUNT_QUERIES'):116    @event.listens_for(models.db.engine, 'before_cursor_execute')117    def receive_before_cursor_execute(118            conn, cursor, statement, parameters, context, executemany):119        global _query_count120        _query_count += 1121    @app.after_request122    def count_queries(response):123        global _query_count124        if _query_count > 0:125            app.logger.info('Request issued %d queries.', _query_count)126            _query_count = 0127        return response128def ensure_setup():129    if not app:...

query_delegate.py

Source:query_delegate.py

1# Copyright 2016 The Chromium OS Authors. All rights reserved.2# Use of this source code is governed by a BSD-style license that can be3# found in the LICENSE file.4"""Feedback query delegate interfaces and implementation registry."""5import multiprocessing6import common7from autotest_lib.client.common_lib.feedback import client8# Mapping of query identifiers to delegate classes.9_query_delegate_registry = {}10class _QueryDelegate(object):11    """A base class for query delegates."""12    _query_count = multiprocessing.Value('d', 0)13    def __init__(self, test, dut, multiplexer, atomic=True):14        """Constructs the delegate.15        @param test: The name of the test.16        @param dut: The name of the DUT.17        @param multiplexer: Feedback request multiplexer object.18        @param atomic: Whether this is an atomic query.19        """20        super(_QueryDelegate, self).__init__()21        self.test = test22        self.dut = dut23        self._multiplexer = multiplexer24        self._atomic = atomic25        # Assign a unique query number.26        with self._query_count.get_lock():27            self._query_num = self._query_count.value28            self._query_count.value += 129    def _process_request(self, req):30        """Submits a given request to the multiplexer for processing."""31        return self._multiplexer.process_request(req, self._query_num,32                                                 self._atomic)33    def prepare(self, **kwargs):34        """Delegate for a query's prepare() method."""35        return self._prepare_impl(**kwargs)36    def _prepare_impl(self, **kwargs):37        """Concrete implementation of the query's prepare() call."""38        raise NotImplementedError39    def validate(self, **kwargs):40        """Delegate for a query's validate() method.41        This clears the atomic sequence with the multiplexer to make sure it42        isn't blocked waiting for more requests from this query.43        """44        try:45            return self._validate_impl(**kwargs)46        finally:47            if self._atomic:48                self._multiplexer.end_atomic_seq(self._query_num)49    def _validate_impl(self, **kwargs):50        """Concrete implementation of the query's validate() call."""51        raise NotImplementedError52class OutputQueryDelegate(_QueryDelegate):53    """A base class for output query delegates."""54class InputQueryDelegate(_QueryDelegate):55    """A base class for input query delegates."""56    def emit(self):57        """Delegate for an input query's emit() method."""58        return self._emit_impl()59    def _emit_impl(self):60        """Concrete implementation of the query's emit() call."""61        raise NotImplementedError62def register_delegate_cls(query_id, delegate_cls):63    """Registers a delegate class with a given query identifier.64    @param query_id: Query identifier constant.65    @param delegate_cls: The class implementing a delegate for this query.66    """67    _query_delegate_registry[query_id] = delegate_cls68def get_delegate_cls(query_id):69    """Returns a query delegate class for a given query type.70    @param query_id: A query type identifier.71    @return A query delegate class.72    @raise ValueError: Unknown query type.73    @raise NotImplementedError: Query type not supported.74    """75    if query_id not in client.ALL_QUERIES:76        raise ValueError77    if query_id not in _query_delegate_registry:78        raise NotImplementedError...

OracleCache.py

Source:OracleCache.py

1# License: CC-02import hashlib3import json4import contextlib5import requests6class OracleCache():7	def __init__(self, oracle_uri):8		self._uri = oracle_uri9		self._sess = requests.Session()10		self._ora_cachefile = "ora_cache_" + hashlib.md5(oracle_uri.encode()).hexdigest() + ".json"11		try:12			with open(self._ora_cachefile) as f:13				self._cache = json.load(f)14		except (FileNotFoundError, json.decoder.JSONDecodeError):15			self._cache = { }16			self.write_cache()17		self._query_count = 018	@property19	def query_count(self):20		return self._query_count21	def write_cache(self):22		with open(self._ora_cachefile, "w") as f:23			json.dump(self._cache, f, separators = (",", ":"))24	def _execute(self, query):25		self._query_count += 126		response = self._sess.post(self._uri, headers = {27			"Content-Type": "application/json",28		}, data = json.dumps(query))29		if response.status_code == 200:30			response_data = response.json()31			return response_data32		else:33			raise Exception("Oracle responded with TurbogrÃ¼tze: %s / %s" % (response, response.content))34	def execute(self, query):35		key = json.dumps(query, sort_keys = True, separators = (",", ":"))36		if key in self._cache:37			return self._cache[key]38		response = self._execute(query)39		self._cache[key] = response40		return response41	def __enter__(self):42		return self43	def __exit__(self, *args):44		self.write_cache()45if __name__ == "__main__":46	# This is how you use this. Be careful that you DO NOT hardcode the URI47	# (like shown below), but use the command line argument instead.48	with OracleCache("https://127.0.0.1:5000/oracle/pkcs7_padding") as oc:...

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