From 08c189d247a43a120afb74a130b35f3f22b310ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gerg=C5=91=20Jedlicska?= Date: Mon, 18 Sep 2023 13:31:53 +0200 Subject: [PATCH] feat: add automation context implementation --- automate_function.py | 45 --- automate_sdk.py | 483 +++++++++++++++++++++++ flatten.py | 6 +- main.py | 70 +++- obj_res.py | 10 + poetry.lock | 842 +++++++++++++++++++++++----------------- pyproject.toml | 17 +- schema_generation.py | 6 - speckle_project_data.py | 13 - tests/test_function.py | 173 +++++++++ 10 files changed, 1230 insertions(+), 435 deletions(-) delete mode 100644 automate_function.py create mode 100644 automate_sdk.py create mode 100644 obj_res.py delete mode 100755 schema_generation.py delete mode 100644 speckle_project_data.py create mode 100644 tests/test_function.py diff --git a/automate_function.py b/automate_function.py deleted file mode 100644 index b13f4b4..0000000 --- a/automate_function.py +++ /dev/null @@ -1,45 +0,0 @@ -from pydantic import BaseModel, ConfigDict -from stringcase import camelcase - -from specklepy.transports.memory import MemoryTransport -from specklepy.transports.server import ServerTransport -from specklepy.api.operations import receive -from specklepy.api.client import SpeckleClient -from flatten import flatten_base -from speckle_project_data import SpeckleProjectData - - -class FunctionInputs(BaseModel): - """ - These are function author defined values, automate will make sure to supply them. - """ - - speckle_type_to_count: str - model_config = ConfigDict(alias_generator=camelcase) - - -def automate_function( - project_data: SpeckleProjectData, - function_inputs: FunctionInputs, - speckle_token: str, -): - client = SpeckleClient(project_data.speckle_server_url) - client.authenticate_with_token(speckle_token) - commit = client.commit.get(project_data.project_id, project_data.version_id) - - memory_transport = MemoryTransport() - server_transport = ServerTransport(project_data.project_id, client) - if not commit.referencedObject: - raise ValueError("The commit has no root referencedObject.") - - base = receive(commit.referencedObject, server_transport, memory_transport) - - count = 0 - for b in flatten_base(base): - if b.speckle_type == function_inputs.speckle_type_to_count: - count += 1 - - print( - f"Found {count} object that match the queried speckle type: ", - f"{function_inputs.speckle_type_to_count}", - ) diff --git a/automate_sdk.py b/automate_sdk.py new file mode 100644 index 0000000..711fbbd --- /dev/null +++ b/automate_sdk.py @@ -0,0 +1,483 @@ +"""WIP module for an automate python sdk.""" +import json +import os +import sys +import time +import traceback +from collections import defaultdict +from dataclasses import dataclass, field +from enum import Enum +from pathlib import Path +from typing import Callable, TypeVar, overload + +import httpx +from gql import gql +from pydantic import BaseModel, ConfigDict +from specklepy.api import operations +from specklepy.api.client import SpeckleClient +from specklepy.objects.base import Base +from specklepy.transports.memory import MemoryTransport +from specklepy.transports.server import ServerTransport +from stringcase import camelcase + + +class AutomateBase(BaseModel): + """Use this class as a base model for automate related DTO.""" + + model_config = ConfigDict(alias_generator=camelcase, populate_by_name=True) + + +class AutomationRunData(BaseModel): + """Values of the project / model that triggered the run of this function.""" + + project_id: str + model_id: str + branch_name: str + version_id: str + speckle_server_url: str + + automation_id: str + automation_revision_id: str + automation_run_id: str + + function_id: str + function_revision: str + + model_config = ConfigDict( + alias_generator=camelcase, populate_by_name=True, protected_namespaces=() + ) + + +class AutomationStatus(str, Enum): + """Set the status of the automation.""" + + INITIALIZING = "INITIALIZING" + RUNNING = "RUNNING" + FAILED = "FAILED" + SUCCEEDED = "SUCCEEDED" + + +class ObjectResultLevel(str, Enum): + """Possible status message levels for object reports.""" + + INFO = "INFO" + WARNING = "WARNING" + ERROR = "ERROR" + + +class ObjectResult(AutomateBase): + """An object level result.""" + + level: ObjectResultLevel + status_message: str + + +class AutomationResult(AutomateBase): + """Schema accepted by the Speckle server as a result for an automation run.""" + + elapsed: float = 0 + result_view: str | None = None + result_versions: list[str] = field(default_factory=list) + blobs: list[str] = field(default_factory=list) + run_status: AutomationStatus = AutomationStatus.RUNNING + status_message: str | None = None + + object_results: dict[str, list[ObjectResult]] = field( + default_factory=lambda: defaultdict(list) # typing: ignore + ) + + +T = TypeVar("T", bound=AutomateBase) + + +@dataclass +class AutomationContext: + """A WIP umbrella class for automate sdk functionality. + + Potentially turn this into a context manager, to handle function enter exit status + changes. + """ + + automation_run_data: AutomationRunData + speckle_client: SpeckleClient + _server_transport: ServerTransport + _speckle_token: str + + #: keep a memory transponrt at hand, to speed up things if needed + _memory_transport: MemoryTransport = field(default_factory=MemoryTransport) + + #: added for performance measuring + _init_time: float = field(default_factory=time.perf_counter) + _automation_result: AutomationResult = field(default_factory=AutomationResult) + + @classmethod + def initialize( + cls, automation_run_data: str | AutomationRunData, speckle_token: str + ) -> "AutomationContext": + """Bootstrap the AutomateSDK from raw data. + + Todo: + ---- + * bootstrap a structlog logger instance + * expose a logger, that ppl can use instead of print + * log an initialization message + """ + # parse the json value if its not an initialized project data instance + automation_run_data = ( + automation_run_data + if isinstance(automation_run_data, AutomationRunData) + else AutomationRunData.model_validate_json(automation_run_data) + ) + speckle_client = SpeckleClient( + automation_run_data.speckle_server_url, + automation_run_data.speckle_server_url.startswith("https"), + ) + speckle_client.authenticate_with_token(speckle_token) + if not speckle_client.account: + msg = ( + f"Could not autenticate to {automation_run_data.speckle_server_url}", + "with the provided token", + ) + raise ValueError(msg) + server_transport = ServerTransport( + automation_run_data.project_id, speckle_client + ) + return cls(automation_run_data, speckle_client, server_transport, speckle_token) + + @property + def run_status(self) -> AutomationStatus: + """Get the status of the automation run.""" + return self._automation_result.run_status + + def elapsed(self) -> float: + """Return the elapsed time in seconds since the initialization time.""" + return time.perf_counter() - self._init_time + + def receive_version(self) -> Base: + """Receive the Speckle project version that triggered this automation run.""" + commit = self.speckle_client.commit.get( + self.automation_run_data.project_id, self.automation_run_data.version_id + ) + if not commit.referencedObject: + raise ValueError("The commit has no referencedObject, cannot receive it.") + base = operations.receive( + commit.referencedObject, self._server_transport, self._memory_transport + ) + print( + f"It took {self.elapsed():2f} seconds to receive", + f" the speckle version {self.automation_run_data.version_id}", + ) + return base + + def create_new_version_in_project( + self, root_object: Base, model_id: str, version_message: str = "" + ) -> None: + """Save a base model to a new version on the project. + + Args: + root_object (Base): The Speckle base object for the new version. + model_id (str): For now please use a `branchName`! + version_message (str): The message for the new version. + """ + if model_id == self.automation_run_data.model_id: + raise ValueError( + f"The target model id: {model_id} cannot match the model id" + f" that triggered this automation: {self.automation_run_data.model_id}" + ) + + root_object_id = operations.send( + root_object, + [self._server_transport, self._memory_transport], + use_default_cache=False, + ) + + version_id = self.speckle_client.commit.create( + stream_id=self.automation_run_data.project_id, + object_id=root_object_id, + branch_name=model_id, + message=version_message, + source_application="SpeckleAutomate", + ) + self._automation_result.result_versions.append(version_id) + + def report_run_status(self) -> None: + """Report the current run status to the Speckle server triggered the automation. + + Once the automation function exits, send the status to the speckle server. + Return the result from the server, it should be a link to the stored automation + result. + """ + query = gql( + """ + mutation ReportFunctionRunStatus( + $automationId: String!, + $automationRevisionId: String!, + $automationRunId: String!, + $versionId: String!, + $functionId: String!, + $runStatus: AutomationRunStatus! + $elapsed: Float! + $resultVersionIds: [String!]! + $statusMessage: String + $objectResults: JSONObject + ){ + automationMutations { + functionRunStatusReport(input: { + automationId: $automationId + automationRevisionId: $automationRevisionId + automationRunId: $automationRunId + versionId: $versionId + functionRuns: [ + { + functionId: $functionId + status: $runStatus, + elapsed: $elapsed, + resultVersionIds: $resultVersionIds, + statusMessage: $statusMessage + results: $objectResults + }] + }) + } + } + """ + ) + if self.run_status in [AutomationStatus.SUCCEEDED, AutomationStatus.FAILED]: + object_results = { + "version": "1.0.0", + "values": { + "speckleObjects": self._automation_result.model_dump(by_alias=True)[ + "objectResults" + ], + "blobs": self._automation_result.blobs, + }, + } + else: + object_results = None + params = { + "automationId": self.automation_run_data.automation_id, + "automationRevisionId": self.automation_run_data.automation_revision_id, + "automationRunId": self.automation_run_data.automation_run_id, + "versionId": self.automation_run_data.version_id, + "functionId": self.automation_run_data.function_id, + "runStatus": self.run_status.value, + "elapsed": self.elapsed(), + "resultVersionIds": self._automation_result.result_versions, + "objectResults": object_results, + } + self.speckle_client.httpclient.execute(query, params) + + def store_file_result(self, file_path: Path | str) -> None: + """Save a file attached to the project of this automation.""" + path_obj = ( + Path(file_path).resolve() if isinstance(file_path, str) else file_path + ) + + class UploadResult(AutomateBase): + blob_id: str + file_name: str + upload_status: int + + class BlobUploadResponse(AutomateBase): + upload_results: list[UploadResult] + + if not path_obj.exists(): + raise ValueError("The given file path doesn't exist") + files = {path_obj.name: open(str(path_obj), "rb")} + + url = ( + f"{self.automation_run_data.speckle_server_url}/api/stream/" + f"{self.automation_run_data.project_id}/blob" + ) + data = ( + httpx.post( + url, + files=files, + headers={"authorization": f"Bearer {self._speckle_token}"}, + ) + .raise_for_status() + .json() + ) + + upload_response = BlobUploadResponse.model_validate(data) + + if len(upload_response.upload_results) != 1: + raise ValueError("Expecting one upload result.") + + for upload_result in upload_response.upload_results: + self._automation_result.blobs.append(upload_result.blob_id) + + def mark_run_failed(self, status_message: str) -> None: + """Mark the current run a failure.""" + self._mark_run(AutomationStatus.FAILED, status_message) + + def mark_run_success(self, status_message: str | None) -> None: + """Mark the current run a success with an optional message.""" + self._mark_run(AutomationStatus.SUCCEEDED, status_message) + + def _mark_run(self, status: AutomationStatus, status_message: str | None) -> None: + duration = self.elapsed() + self._automation_result.status_message = status_message + self._automation_result.run_status = status + self._automation_result.elapsed = duration + + msg = f"Automation run {status.value} after {duration:2f} seconds." + print("\n".join([msg, status_message]) if status_message else msg) + + def add_object_error(self, object_id: str, error_cause: str) -> None: + """Add an error to a given objec id.""" + self._add_object_result(object_id, ObjectResultLevel.ERROR, error_cause) + + def add_object_warning(self, object_id: str, warning: str) -> None: + """Add a warning to a given object id.""" + self._add_object_result(object_id, ObjectResultLevel.WARNING, warning) + + def add_object_info(self, object_id: str, info: str) -> None: + """Add an info message to a given object.""" + self._add_object_result(object_id, ObjectResultLevel.INFO, info) + + def _add_object_result( + self, object_id: str, level: ObjectResultLevel, status_message: str + ) -> None: + print( + f"Object {object_id} was marked with {level.value.upper()}", + f" cause: {status_message}", + ) + self._automation_result.object_results[object_id].append( + ObjectResult(level=level, status_message=status_message) + ) + + +AutomateFunction = Callable[[AutomationContext, T], None] +AutomateFunctionWithoutInputs = Callable[[AutomationContext], None] + + +@overload +def execute_automate_function( + automate_function: AutomateFunction[T], + input_schema: type[T], +) -> None: + ... + + +@overload +def execute_automate_function(automate_function: AutomateFunctionWithoutInputs) -> None: + ... + + +def execute_automate_function( + automate_function: AutomateFunction[T] | AutomateFunctionWithoutInputs, + input_schema: type[T] | None = None, +): + """Runs the provided automate function with the input schema.""" + # first arg is the python file name, we do not need that + args = sys.argv[1:] + + if len(args) < 2: + raise ValueError("too few arguments specified need minimum 2") + + if len(args) > 4: + raise ValueError("too many arguments specified, max supported is 4") + + # we rely on a command name convention to decide what to do. + # this is here, so that the function authors do not see any of this + command = args[0] + + if command == "generate_schema": + path = Path(args[1]) + schema = json.dumps( + input_schema.model_json_schema(by_alias=True) if input_schema else {} + ) + path.write_text(schema) + + elif command == "run": + automation_run_data = args[1] + function_inputs = args[2] + + speckle_token = os.environ.get("SPECKLE_TOKEN", None) + if not speckle_token and len(args) != 4: + raise ValueError("Cannot get speckle token from arguments or environment") + + speckle_token = speckle_token if speckle_token else args[3] + + inputs = ( + input_schema.model_validate_json(function_inputs) + if input_schema + else input_schema + ) + + if inputs: + automate_sdk = run_function( + automate_function, # type: ignore + automation_run_data, + speckle_token, + inputs, + ) + else: + automate_sdk = run_function( + automate_function, # type: ignore + automation_run_data, + speckle_token, + ) + + exit_code = 0 if automate_sdk.run_status == AutomationStatus.SUCCEEDED else 1 + exit(exit_code) + + else: + raise NotImplementedError(f"Command: '{command}' is not supported.") + + +@overload +def run_function( + automate_function: AutomateFunction[T], + automation_run_data: AutomationRunData | str, + speckle_token: str, + inputs: T, +) -> AutomationContext: + ... + + +@overload +def run_function( + automate_function: AutomateFunctionWithoutInputs, + automation_run_data: AutomationRunData | str, + speckle_token: str, +) -> AutomationContext: + ... + + +def run_function( + automate_function: AutomateFunction[T] | AutomateFunctionWithoutInputs, + automation_run_data: AutomationRunData | str, + speckle_token: str, + inputs: T | None = None, +) -> AutomationContext: + """Run the provided function with the automate sdk context.""" + automate_sdk = AutomationContext.initialize(automation_run_data, speckle_token) + automate_sdk.report_run_status() + + try: + # avoiding complex type gymnastics here on the internals. + # the external type overloads make this correct + if inputs: + automate_function(automate_sdk, inputs) # type: ignore + else: + automate_function(automate_sdk) # type: ignore + + # the function author forgot to mark the function success + if automate_sdk.run_status not in [ + AutomationStatus.FAILED, + AutomationStatus.SUCCEEDED, + ]: + automate_sdk.mark_run_success( + "WARNING: Automate assumed a success status," + " but it was not marked as so by the function." + ) + except Exception: + trace = traceback.format_exc() + print(trace) + automate_sdk.mark_run_failed( + "Function error. Check the automation run logs for details." + ) + finally: + automate_sdk.report_run_status() + return automate_sdk diff --git a/flatten.py b/flatten.py index b1c2cfb..4c2d5bb 100644 --- a/flatten.py +++ b/flatten.py @@ -1,8 +1,12 @@ -from typing import Iterable +"""Helper module for a simple speckle object tree flattening.""" + +from collections.abc import Iterable + from specklepy.objects import Base def flatten_base(base: Base) -> Iterable[Base]: + """Take a base and flatten it to an iterable of bases.""" if hasattr(base, "elements"): for element in base["elements"]: yield from flatten_base(element) diff --git a/main.py b/main.py index b775743..0a420b4 100644 --- a/main.py +++ b/main.py @@ -1,26 +1,56 @@ -import typer -import os -from speckle_project_data import SpeckleProjectData -from automate_function import FunctionInputs, automate_function -from typing_extensions import Annotated -from typing import Optional +"""This module contains the business logic of the function. + +Make sure that this module exposes a `FunctionInputs` class +and an `automate_function` function definition. +""" +from specklepy.objects.geometry import Mesh + +from automate_sdk import ( + AutomateBase, + AutomationContext, + execute_automate_function, +) +from flatten import flatten_base -def main( - speckle_project_data: str, - function_inputs: str, - speckle_token: Annotated[Optional[str], typer.Argument()] = None, -): - speckle_token = ( - speckle_token if speckle_token else os.environ.get("SPECKLE_TOKEN", None) - ) - if not speckle_token: - raise ValueError("The supplied speckle token is not valid") +class FunctionInputs(AutomateBase): + """These are function author defined values. - project_data = SpeckleProjectData.model_validate_json(speckle_project_data) - inputs = FunctionInputs.model_validate_json(function_inputs) - automate_function(project_data, inputs, speckle_token) + Automate will make sure to supply them matching the types specified here. + """ + + forbidden_speckle_type: str + + +def automate_function( + automate_context: AutomationContext, + function_inputs: FunctionInputs, +) -> None: + """Hey, trying the automate sdk experience here.""" + version_root_object = automate_context.receive_version() + + count = 0 + for b in flatten_base(version_root_object): + if b.speckle_type == function_inputs.forbidden_speckle_type: + if not b.id: + raise ValueError("Cannot operate on objects without their id's.") + automate_context.add_object_error( + b.id, + "This project should not contain the type: " + f"{function_inputs.forbidden_speckle_type}", + ) + count += 1 + + if count > 0: + automate_context.mark_run_failed( + "Automation failed: " + f"Found {count} object that have a forbidden speckle type: " + f"{function_inputs.forbidden_speckle_type}" + ) + + else: + automate_context.mark_run_success("No forbidden types found.") if __name__ == "__main__": - typer.run(main) + execute_automate_function(automate_function, FunctionInputs) diff --git a/obj_res.py b/obj_res.py new file mode 100644 index 0000000..fa0436a --- /dev/null +++ b/obj_res.py @@ -0,0 +1,10 @@ +from automate_sdk import AutomationResult, ObjectResult, ObjectResultLevel + +res = AutomationResult() + + +res.object_results["foobar"].append( + ObjectResult(level=ObjectResultLevel.ERROR, status_message="foobar") +) + +print(res.model_dump(by_alias=True)) diff --git a/poetry.lock b/poetry.lock index a746a14..25ab36a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -11,6 +11,27 @@ files = [ {file = "annotated_types-0.5.0.tar.gz", hash = "sha256:47cdc3490d9ac1506ce92c7aaa76c579dc3509ff11e098fc867e5130ab7be802"}, ] +[[package]] +name = "anyio" +version = "4.0.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"}, + {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.22)"] + [[package]] name = "appdirs" version = "1.4.4" @@ -22,6 +43,24 @@ files = [ {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, ] +[[package]] +name = "attrs" +version = "23.1.0" +description = "Classes Without Boilerplate" +optional = false +python-versions = ">=3.7" +files = [ + {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"}, + {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"}, +] + +[package.extras] +cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]", "pre-commit"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] +tests = ["attrs[tests-no-zope]", "zope-interface"] +tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] + [[package]] name = "backoff" version = "2.2.1" @@ -35,36 +74,33 @@ files = [ [[package]] name = "black" -version = "23.3.0" +version = "23.9.1" description = "The uncompromising code formatter." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - 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[metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "1b8a82c8d00a7b305192b1ca8a50cf3498a6fad9d01611e57e241ba6bb959385" +content-hash = "a4fd24e538d011e9a91f29c80a4221504c7e35e07cecdac214e9710683974c65" diff --git a/pyproject.toml b/pyproject.toml index c8aab08..b8b233f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,16 +8,29 @@ packages = [{include = "src/speckle_automate_py"}] [tool.poetry.dependencies] python = "^3.10" -specklepy = "^2.16.0" -typer = "^0.9.0" +specklepy = "^2.16.2" pydantic = "^2.1.1" stringcase = "^1.2.0" +httpx = "^0.25.0" [tool.poetry.group.dev.dependencies] black = "^23.3.0" mypy = "^1.3.0" ruff = "^0.0.271" +pytest = "^7.4.2" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "UP", # pyupgrade + "D", # pydocstyle + "I", # isort +] + +[tool.ruff.pydocstyle] +convention = "google" diff --git a/schema_generation.py b/schema_generation.py deleted file mode 100755 index 05ecee4..0000000 --- a/schema_generation.py +++ /dev/null @@ -1,6 +0,0 @@ -import json -from main import FunctionInputs - - -if __name__ == "__main__": - print(json.dumps(FunctionInputs.model_json_schema())) \ No newline at end of file diff --git a/speckle_project_data.py b/speckle_project_data.py deleted file mode 100644 index 0112d4d..0000000 --- a/speckle_project_data.py +++ /dev/null @@ -1,13 +0,0 @@ -from pydantic import BaseModel, ConfigDict -from stringcase import camelcase - - -class SpeckleProjectData(BaseModel): - """Values of the project / model that triggered the run of this function.""" - - project_id: str - model_id: str - version_id: str - speckle_server_url: str - - model_config = ConfigDict(alias_generator=camelcase, protected_namespaces=()) diff --git a/tests/test_function.py b/tests/test_function.py new file mode 100644 index 0000000..cfea74d --- /dev/null +++ b/tests/test_function.py @@ -0,0 +1,173 @@ +"""Run integration tests with a speckle server.""" +import os +import secrets +import string +from pathlib import Path + +import pytest +from gql import gql +from specklepy.api import operations +from specklepy.api.client import SpeckleClient +from specklepy.objects.base import Base +from specklepy.transports.server import ServerTransport + +from automate_sdk import ( + AutomationContext, + AutomationRunData, + AutomationStatus, + run_function, +) +from main import FunctionInputs, automate_function + + +def crypto_random_string(length: int) -> str: + """Generate a semi crypto random string of a given length.""" + alphabet = string.ascii_letters + string.digits + return "".join(secrets.choice(alphabet) for _ in range(length)) + + +def register_new_automation( + project_id: str, + model_id: str, + speckle_client: SpeckleClient, + automation_id: str, + automation_name: str, + automation_revision_id: str, +): + """Register a new automation in the speckle server.""" + query = gql( + """ + mutation CreateAutomation( + $projectId: String! + $modelId: String! + $automationName: String! + $automationId: String! + $automationRevisionId: String! + ) { + automationMutations { + create( + input: { + projectId: $projectId + modelId: $modelId + automationName: $automationName + automationId: $automationId + automationRevisionId: $automationRevisionId + } + ) + } + } + """ + ) + params = { + "projectId": project_id, + "modelId": model_id, + "automationName": automation_name, + "automationId": automation_id, + "automationRevisionId": automation_revision_id, + } + speckle_client.httpclient.execute(query, params) + + +@pytest.fixture() +def speckle_token() -> str: + """Provide a speckle token for the test suite.""" + env_var = "SPECKLE_TOKEN" + token = os.getenv(env_var) + if not token: + raise ValueError(f"Cannot run tests without a {env_var} environment variable") + return token + + +@pytest.fixture() +def speckle_server_url() -> str: + """Provide a speckle server url for the test suite, default to localhost.""" + return os.getenv("SPECKLE_SERVER_URL", "http://127.0.0.1:3000") + + +@pytest.fixture() +def test_client(speckle_server_url: str, speckle_token: str) -> SpeckleClient: + """Initialize a SpeckleClient for testing.""" + test_client = SpeckleClient( + speckle_server_url, speckle_server_url.startswith("https") + ) + test_client.authenticate_with_token(speckle_token) + return test_client + + +@pytest.fixture() +def test_object() -> Base: + """Create a Base model for testing.""" + root_object = Base() + root_object.foo = "bar" + return root_object + + +@pytest.fixture() +def automation_run_data( + test_object: Base, test_client: SpeckleClient, speckle_server_url: str +) -> AutomationRunData: + """Set up an automation context for testing.""" + project_id = test_client.stream.create("Automate function e2e test") + branch_name = "main" + + model = test_client.branch.get(project_id, branch_name, commits_limit=1) + model_id: str = model.id + + root_obj_id = operations.send( + test_object, [ServerTransport(project_id, test_client)] + ) + version_id = test_client.commit.create(project_id, root_obj_id) + + automation_name = crypto_random_string(10) + automation_id = crypto_random_string(10) + automation_revision_id = crypto_random_string(10) + + register_new_automation( + project_id, + model_id, + test_client, + automation_id, + automation_name, + automation_revision_id, + ) + + automation_run_id = crypto_random_string(10) + function_id = crypto_random_string(10) + function_revision = crypto_random_string(10) + return AutomationRunData( + project_id=project_id, + model_id=model_id, + branch_name=branch_name, + version_id=version_id, + speckle_server_url=speckle_server_url, + automation_id=automation_id, + automation_revision_id=automation_revision_id, + automation_run_id=automation_run_id, + function_id=function_id, + function_revision=function_revision, + ) + + +def test_function_run(automation_run_data: AutomationRunData, speckle_token: str): + """Run an integration test for the automate function.""" + automate_sdk = run_function( + automate_function, + automation_run_data, + speckle_token, + FunctionInputs(forbidden_speckle_type="Base"), + ) + + assert automate_sdk.run_status == AutomationStatus.FAILED + + +def test_file_uploads(automation_run_data: AutomationRunData, speckle_token: str): + """Test file store capabilities of the automate sdk.""" + automate_context = AutomationContext.initialize(automation_run_data, speckle_token) + + path = Path(f"./{crypto_random_string(10)}").resolve() + path.write_text("foobar") + + automate_context.store_file_result(path) + + os.remove(path) + assert len(automate_context._automation_result.blobs) == 1