519 lines
16 KiB
Python
519 lines
16 KiB
Python
# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2025)
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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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from __future__ import annotations
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import contextlib
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import inspect
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import os
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import sys
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import threading
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import time
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import uuid
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from collections.abc import Sized
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from functools import wraps
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from typing import Any, Callable, Final, TypeVar, cast, overload
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from streamlit import config, file_util, util
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from streamlit.logger import get_logger
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from streamlit.proto.ForwardMsg_pb2 import ForwardMsg
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from streamlit.proto.PageProfile_pb2 import Argument, Command
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from streamlit.runtime.scriptrunner_utils.exceptions import RerunException
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from streamlit.runtime.scriptrunner_utils.script_run_context import get_script_run_ctx
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_LOGGER: Final = get_logger(__name__)
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# Limit the number of commands to keep the page profile message small
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_MAX_TRACKED_COMMANDS: Final = 200
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# Only track a maximum of 25 uses per unique command since some apps use
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# commands excessively (e.g. calling add_rows thousands of times in one rerun)
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# making the page profile useless.
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_MAX_TRACKED_PER_COMMAND: Final = 25
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# A mapping to convert from the actual name to preferred/shorter representations
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_OBJECT_NAME_MAPPING: Final = {
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"streamlit.delta_generator.DeltaGenerator": "DG",
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"pandas.core.frame.DataFrame": "DataFrame",
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"plotly.graph_objs._figure.Figure": "PlotlyFigure",
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"bokeh.plotting.figure.Figure": "BokehFigure",
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"matplotlib.figure.Figure": "MatplotlibFigure",
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"pandas.io.formats.style.Styler": "PandasStyler",
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"pandas.core.indexes.base.Index": "PandasIndex",
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"pandas.core.series.Series": "PandasSeries",
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"streamlit.connections.snowpark_connection.SnowparkConnection": "SnowparkConnection",
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"streamlit.connections.sql_connection.SQLConnection": "SQLConnection",
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}
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# A list of dependencies to check for attribution
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_ATTRIBUTIONS_TO_CHECK: Final = [
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# DB Clients:
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"pymysql",
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"MySQLdb",
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"mysql",
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"pymongo",
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"ibis",
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"boto3",
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"psycopg2",
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"psycopg3",
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"sqlalchemy",
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"elasticsearch",
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"pyodbc",
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"pymssql",
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"cassandra",
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"azure",
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"redis",
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"sqlite3",
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"neo4j",
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"duckdb",
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"opensearchpy",
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"supabase",
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# Dataframe Libraries:
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"polars",
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"dask",
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"vaex",
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"modin",
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"pyspark",
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"cudf",
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"xarray",
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"ray",
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"geopandas",
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"mars",
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"tables",
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"zarr",
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"datasets",
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# ML & LLM Tools:
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"mistralai",
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"openai",
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"langchain",
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"llama_index",
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"llama_cpp",
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"anthropic",
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"pyllamacpp",
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"cohere",
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"transformers",
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"nomic",
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"diffusers",
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"semantic_kernel",
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"replicate",
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"huggingface_hub",
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"wandb",
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"torch",
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"tensorflow",
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"trubrics",
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"comet_ml",
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"clarifai",
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"reka",
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"hegel",
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"fastchat",
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"assemblyai",
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"openllm",
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"embedchain",
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"haystack",
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"vllm",
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"alpa",
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"jinaai",
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"guidance",
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"litellm",
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"comet_llm",
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"instructor",
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"xgboost",
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"lightgbm",
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"catboost",
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"sklearn",
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# Workflow Tools:
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"prefect",
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"luigi",
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"airflow",
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"dagster",
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# Vector Stores:
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"pgvector",
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"faiss",
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"annoy",
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"pinecone",
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"chromadb",
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"weaviate",
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"qdrant_client",
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"pymilvus",
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"lancedb",
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# Others:
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"snowflake",
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"streamlit_extras",
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"streamlit_pydantic",
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"pydantic",
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"plost",
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"authlib",
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]
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_ETC_MACHINE_ID_PATH = "/etc/machine-id"
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_DBUS_MACHINE_ID_PATH = "/var/lib/dbus/machine-id"
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def _get_machine_id_v3() -> str:
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"""Get the machine ID.
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This is a unique identifier for a user for tracking metrics,
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that is broken in different ways in some Linux distros and Docker images.
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- at times just a hash of '', which means many machines map to the same ID
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- at times a hash of the same string, when running in a Docker container
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"""
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if os.path.isfile(_ETC_MACHINE_ID_PATH):
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with open(_ETC_MACHINE_ID_PATH) as f:
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machine_id = f.read()
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elif os.path.isfile(_DBUS_MACHINE_ID_PATH):
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with open(_DBUS_MACHINE_ID_PATH) as f:
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machine_id = f.read()
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else:
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machine_id = str(uuid.getnode())
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return machine_id
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def _get_machine_id_v4() -> str:
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"""Get a random ID that is stable for each machine, generating if needed.
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This is a unique identifier for a user for tracking metrics.
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Instead of relying on a hardware address in the container or host we'll
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generate a UUID and store it in the ~/.streamlit hidden folder.
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"""
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# If gatherUsageStats is False skip this whole code.
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# This is just for people who don't want the extra machine_id_v4 file
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# in their file system.
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if not config.get_option("browser.gatherUsageStats"):
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# This value will never be sent to our telemetry. Just including it here
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# to help debug.
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return "no-machine-id-v4"
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filepath = file_util.get_streamlit_file_path("machine_id_v4")
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stable_id = None
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if os.path.exists(filepath):
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with file_util.streamlit_read(filepath) as file:
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stable_id = file.read()
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if not stable_id:
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stable_id = str(uuid.uuid4())
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with file_util.streamlit_write(filepath) as output:
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output.write(stable_id)
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return stable_id
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class Installation:
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_instance_lock = threading.Lock()
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_instance: Installation | None = None
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@classmethod
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def instance(cls) -> Installation:
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"""Returns the singleton Installation."""
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# We use a double-checked locking optimization to avoid the overhead
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# of acquiring the lock in the common case:
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# https://en.wikipedia.org/wiki/Double-checked_locking
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if cls._instance is None:
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with cls._instance_lock:
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if cls._instance is None:
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cls._instance = Installation()
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return cls._instance
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def __init__(self) -> None:
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self.installation_id_v3 = str(
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uuid.uuid5(uuid.NAMESPACE_DNS, _get_machine_id_v3())
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)
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self.installation_id_v4 = _get_machine_id_v4()
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def __repr__(self) -> str:
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return util.repr_(self)
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@property
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def installation_id(self) -> str:
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return self.installation_id_v3
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def _get_type_name(obj: object) -> str:
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"""Get a simplified name for the type of the given object."""
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with contextlib.suppress(Exception):
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obj_type = obj if inspect.isclass(obj) else type(obj)
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type_name = "unknown"
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if hasattr(obj_type, "__qualname__"):
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type_name = obj_type.__qualname__
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elif hasattr(obj_type, "__name__"):
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type_name = obj_type.__name__
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if obj_type.__module__ != "builtins":
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# Add the full module path
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type_name = f"{obj_type.__module__}.{type_name}"
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if type_name in _OBJECT_NAME_MAPPING:
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type_name = _OBJECT_NAME_MAPPING[type_name]
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return type_name
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return "failed"
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def _get_top_level_module(func: Callable[..., Any]) -> str:
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"""Get the top level module for the given function."""
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module = inspect.getmodule(func)
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if module is None or not module.__name__:
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return "unknown"
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return module.__name__.split(".")[0]
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def _get_arg_metadata(arg: object) -> str | None:
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"""Get metadata information related to the value of the given object."""
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with contextlib.suppress(Exception):
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if isinstance(arg, (bool)):
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return f"val:{arg}"
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if isinstance(arg, Sized):
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return f"len:{len(arg)}"
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return None
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def _get_command_telemetry(
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_command_func: Callable[..., Any], _command_name: str, *args: Any, **kwargs: Any
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) -> Command:
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"""Get telemetry information for the given callable and its arguments."""
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arg_keywords = inspect.getfullargspec(_command_func).args
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self_arg: Any | None = None
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arguments: list[Argument] = []
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is_method = inspect.ismethod(_command_func)
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name = _command_name
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for i, arg in enumerate(args):
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pos = i
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if is_method:
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# If func is a method, ignore the first argument (self)
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i = i + 1 # noqa: PLW2901
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keyword = arg_keywords[i] if len(arg_keywords) > i else f"{i}"
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if keyword == "self":
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self_arg = arg
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continue
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argument = Argument(k=keyword, t=_get_type_name(arg), p=pos)
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arg_metadata = _get_arg_metadata(arg)
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if arg_metadata:
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argument.m = arg_metadata
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arguments.append(argument)
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for kwarg, kwarg_value in kwargs.items():
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argument = Argument(k=kwarg, t=_get_type_name(kwarg_value))
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arg_metadata = _get_arg_metadata(kwarg_value)
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if arg_metadata:
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argument.m = arg_metadata
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arguments.append(argument)
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top_level_module = _get_top_level_module(_command_func)
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if top_level_module != "streamlit":
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# If the gather_metrics decorator is used outside of streamlit library
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# we enforce a prefix to be added to the tracked command:
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name = f"external:{top_level_module}:{name}"
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if (
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name == "create_instance"
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and self_arg
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and hasattr(self_arg, "name")
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and self_arg.name
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):
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name = f"component:{self_arg.name}"
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return Command(name=name, args=arguments)
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def to_microseconds(seconds: float) -> int:
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"""Convert seconds into microseconds."""
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return int(seconds * 1_000_000)
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F = TypeVar("F", bound=Callable[..., Any])
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@overload
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def gather_metrics(
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name: str,
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func: F,
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) -> F: ...
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@overload
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def gather_metrics(
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name: str,
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func: None = None,
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) -> Callable[[F], F]: ...
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def gather_metrics(name: str, func: F | None = None) -> Callable[[F], F] | F:
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"""Function decorator to add telemetry tracking to commands.
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Parameters
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----------
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func : callable
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The function to track for telemetry.
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name : str or None
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Overwrite the function name with a custom name that is used for telemetry tracking.
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Example
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-------
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>>> @st.gather_metrics
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... def my_command(url):
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... return url
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>>> @st.gather_metrics(name="custom_name")
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... def my_command(url):
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... return url
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"""
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if not name:
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_LOGGER.warning("gather_metrics: name is empty")
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name = "undefined"
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if func is None:
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# Support passing the params via function decorator
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def wrapper(f: F) -> F:
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return gather_metrics(
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name=name,
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func=f,
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)
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return wrapper
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# To make mypy type narrow F | None -> F
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non_optional_func = func
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@wraps(non_optional_func)
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def wrapped_func(*args: Any, **kwargs: Any) -> Any:
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from timeit import default_timer as timer
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exec_start = timer()
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ctx = get_script_run_ctx(suppress_warning=True)
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tracking_activated = (
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ctx is not None
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and ctx.gather_usage_stats
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and not ctx.command_tracking_deactivated
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and len(ctx.tracked_commands)
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< _MAX_TRACKED_COMMANDS # Prevent too much memory usage
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)
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command_telemetry: Command | None = None
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# This flag is needed to make sure that only the command (the outermost command)
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# that deactivated tracking (via ctx.command_tracking_deactivated) is able to reset it
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# again. This is important to prevent nested commands from reactivating tracking.
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# At this point, we don't know yet if the command will deactivated tracking.
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has_set_command_tracking_deactivated = False
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if ctx and tracking_activated:
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try:
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command_telemetry = _get_command_telemetry(
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non_optional_func, name, *args, **kwargs
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)
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if (
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command_telemetry.name not in ctx.tracked_commands_counter
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or ctx.tracked_commands_counter[command_telemetry.name]
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< _MAX_TRACKED_PER_COMMAND
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):
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ctx.tracked_commands.append(command_telemetry)
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ctx.tracked_commands_counter.update([command_telemetry.name])
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# Deactivate tracking to prevent calls inside already tracked commands
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ctx.command_tracking_deactivated = True
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# The ctx.command_tracking_deactivated flag was set to True,
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# we also need to set has_set_command_tracking_deactivated to True
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# to make sure that this command is able to reset it again.
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has_set_command_tracking_deactivated = True
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except Exception as ex:
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# Always capture all exceptions since we want to make sure that
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# the telemetry never causes any issues.
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_LOGGER.debug("Failed to collect command telemetry", exc_info=ex)
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try:
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result = non_optional_func(*args, **kwargs)
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except RerunException:
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# Duplicated from below, because static analysis tools get confused
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# by deferring the rethrow.
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if tracking_activated and command_telemetry:
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command_telemetry.time = to_microseconds(timer() - exec_start)
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raise
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finally:
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# Activate tracking again if command executes without any exceptions
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# we only want to do that if this command has set the
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# flag to deactivate tracking.
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if ctx and has_set_command_tracking_deactivated:
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ctx.command_tracking_deactivated = False
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if tracking_activated and command_telemetry:
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# Set the execution time to the measured value
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command_telemetry.time = to_microseconds(timer() - exec_start)
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return result
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with contextlib.suppress(AttributeError):
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# Make this a well-behaved decorator by preserving important function
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# attributes.
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wrapped_func.__dict__.update(non_optional_func.__dict__)
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wrapped_func.__signature__ = inspect.signature(non_optional_func) # type: ignore
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return cast("F", wrapped_func)
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def create_page_profile_message(
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commands: list[Command],
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exec_time: int,
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prep_time: int,
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uncaught_exception: str | None = None,
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) -> ForwardMsg:
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"""Create and return the full PageProfile ForwardMsg."""
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msg = ForwardMsg()
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page_profile = msg.page_profile
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page_profile.commands.extend(commands)
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page_profile.exec_time = exec_time
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page_profile.prep_time = prep_time
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page_profile.headless = config.get_option("server.headless")
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# Collect all config options that have been manually set
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config_options: set[str] = set()
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if config._config_options:
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for option_name in config._config_options:
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if not config.is_manually_set(option_name):
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# We only care about manually defined options
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continue
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config_option = config._config_options[option_name]
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config_options.add(
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f"{option_name}:default" if config_option.is_default else option_name
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)
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page_profile.config.extend(config_options)
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# Check the predefined set of modules for attribution
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attributions: set[str] = {
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attribution
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for attribution in _ATTRIBUTIONS_TO_CHECK
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if attribution in sys.modules
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}
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page_profile.os = str(sys.platform)
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page_profile.timezone = str(time.tzname)
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page_profile.attributions.extend(attributions)
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if uncaught_exception:
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page_profile.uncaught_exception = uncaught_exception
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if ctx := get_script_run_ctx():
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page_profile.is_fragment_run = bool(ctx.fragment_ids_this_run)
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return msg
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