114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Middleware for injecting client-side feature flags into the Context."""
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import json
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import urllib.parse
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from tensorboard import context
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from tensorboard import errors
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class ClientFeatureFlagsMiddleware:
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"""Middleware for injecting client-side feature flags into the Context.
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The client webapp is expected to include a json-serialized version of its
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FeatureFlags in the `X-TensorBoard-Feature-Flags` header or the
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`tensorBoardFeatureFlags` query parameter. This middleware extracts the
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header or query parameter value and converts it into the client_feature_flags
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property for the DataProvider's Context object, where client_feature_flags
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is a Dict of string keys and arbitrary value types.
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In the event that both the header and query parameter are specified, the
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values from the header will take precedence.
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"""
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def __init__(self, application):
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"""Initializes this middleware.
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Args:
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application: The WSGI application to wrap (see PEP 3333).
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"""
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self._application = application
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def __call__(self, environ, start_response):
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header_feature_flags = self._parse_potential_header_param_flags(
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environ.get("HTTP_X_TENSORBOARD_FEATURE_FLAGS")
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)
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query_string_feature_flags = self._parse_potential_query_param_flags(
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environ.get("QUERY_STRING")
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)
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if not header_feature_flags and not query_string_feature_flags:
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return self._application(environ, start_response)
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# header flags take precedence
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for flag, value in header_feature_flags.items():
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query_string_feature_flags[flag] = value
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ctx = context.from_environ(environ).replace(
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client_feature_flags=query_string_feature_flags
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)
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context.set_in_environ(environ, ctx)
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return self._application(environ, start_response)
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def _parse_potential_header_param_flags(self, header_string):
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if not header_string:
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return {}
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try:
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header_feature_flags = json.loads(header_string)
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except json.JSONDecodeError:
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raise errors.InvalidArgumentError(
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"X-TensorBoard-Feature-Flags cannot be JSON decoded."
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)
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if not isinstance(header_feature_flags, dict):
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raise errors.InvalidArgumentError(
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"X-TensorBoard-Feature-Flags cannot be decoded to a dict."
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)
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return header_feature_flags
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def _parse_potential_query_param_flags(self, query_string):
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if not query_string:
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return {}
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try:
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query_string_json = urllib.parse.parse_qs(query_string)
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except ValueError:
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return {}
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# parse_qs returns the dictionary values as lists for each name.
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potential_feature_flags = query_string_json.get(
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"tensorBoardFeatureFlags", []
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)
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if not potential_feature_flags:
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return {}
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try:
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client_feature_flags = json.loads(potential_feature_flags[0])
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except json.JSONDecodeError:
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raise errors.InvalidArgumentError(
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"tensorBoardFeatureFlags cannot be JSON decoded."
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)
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if not isinstance(client_feature_flags, dict):
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raise errors.InvalidArgumentError(
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"tensorBoardFeatureFlags cannot be decoded to a dict."
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)
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return client_feature_flags
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