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promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_mlflow_dependencies.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pytest
import promptflow
import promptflow._sdk._mlflow as module
@pytest.mark.sdk_test
@pytest.mark.unittest
class TestMLFlowDependencies:
def test_mlflow_dependencies(self):
assert module.DAG_FILE_NAME == "flow.dag.yaml"
assert module.Flow == promptflow._sdk.entities._flow.Flow
assert module.FlowInvoker == promptflow._sdk._serving.flow_invoker.FlowInvoker
assert module.remove_additional_includes is not None
assert module._merge_local_code_and_additional_includes is not None
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_cli_activity_name.py | import pytest
from promptflow._cli._pf.entry import get_parser_args
from promptflow._cli._utils import _get_cli_activity_name
def get_cli_activity_name(cmd):
prog, args = get_parser_args(list(cmd)[1:])
return _get_cli_activity_name(cli=prog, args=args)
@pytest.mark.unittest
class TestCliTimeConsume:
def test_pf_run_create(self, activity_name="pf.run.create") -> None:
assert get_cli_activity_name(
cmd=(
"pf",
"run",
"create",
"--flow",
"print_input_flow",
"--data",
"print_input_flow.jsonl",
)) == activity_name
def test_pf_run_update(self, activity_name="pf.run.update") -> None:
assert get_cli_activity_name(
cmd=(
"pf",
"run",
"update",
"--name",
"test_name",
"--set",
"description=test pf run update"
)) == activity_name
def test_pf_flow_test(self, activity_name="pf.flow.test"):
assert get_cli_activity_name(
cmd=(
"pf",
"flow",
"test",
"--flow",
"print_input_flow",
"--inputs",
"text=https://www.youtube.com/watch?v=o5ZQyXaAv1g",
)) == activity_name
def test_pf_flow_build(self, activity_name="pf.flow.build"):
assert get_cli_activity_name(
cmd=(
"pf",
"flow",
"build",
"--source",
"print_input_flow/flow.dag.yaml",
"--output",
"./",
"--format",
"docker",
)) == activity_name
def test_pf_connection_create(self, activity_name="pf.connection.create"):
assert get_cli_activity_name(
cmd=(
"pf",
"connection",
"create",
"--file",
"azure_openai_connection.yaml",
"--name",
"test_name",
)) == activity_name
def test_pf_connection_list(self, activity_name="pf.connection.list"):
assert get_cli_activity_name(cmd=("pf", "connection", "list")) == activity_name
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_config.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
import pytest
from promptflow._sdk._configuration import Configuration, InvalidConfigValue
from promptflow._sdk._constants import FLOW_DIRECTORY_MACRO_IN_CONFIG
from promptflow._sdk._utils import ClientUserAgentUtil
CONFIG_DATA_ROOT = Path(__file__).parent.parent.parent / "test_configs" / "configs"
@pytest.fixture
def config():
return Configuration.get_instance()
@pytest.mark.unittest
class TestConfig:
def test_set_config(self, config):
config.set_config("a.b.c.test_key", "test_value")
assert config.get_config("a.b.c.test_key") == "test_value"
# global config may contain other keys
assert config.config["a"] == {"b": {"c": {"test_key": "test_value"}}}
def test_get_config(self, config):
config.set_config("test_key", "test_value")
assert config.get_config("test_key") == "test_value"
def test_get_or_set_installation_id(self, config):
user_id = config.get_or_set_installation_id()
assert user_id is not None
def test_config_instance(self, config):
new_config = Configuration.get_instance()
assert new_config is config
def test_set_invalid_run_output_path(self, config: Configuration) -> None:
expected_error_message = (
"Cannot specify flow directory as run output path; "
"if you want to specify run output path under flow directory, "
"please use its child folder, e.g. '${flow_directory}/.runs'."
)
# directly set
with pytest.raises(InvalidConfigValue) as e:
config.set_config(key=Configuration.RUN_OUTPUT_PATH, value=FLOW_DIRECTORY_MACRO_IN_CONFIG)
assert expected_error_message in str(e)
# override
with pytest.raises(InvalidConfigValue) as e:
Configuration(overrides={Configuration.RUN_OUTPUT_PATH: FLOW_DIRECTORY_MACRO_IN_CONFIG})
assert expected_error_message in str(e)
def test_ua_set_load(self, config: Configuration) -> None:
config.set_config(key=Configuration.USER_AGENT, value="test/1.0.0")
user_agent = config.get_user_agent()
assert user_agent == "PFCustomer_test/1.0.0"
# load empty ua won't break
config.set_config(key=Configuration.USER_AGENT, value="")
user_agent = config.get_user_agent()
assert user_agent == ""
# empty ua won't add to context
ClientUserAgentUtil.update_user_agent_from_config()
user_agent = ClientUserAgentUtil.get_user_agent()
# in test environment, user agent may contain promptflow-local-serving/0.0.1 test-user-agent
assert "test/1.0.0" not in user_agent
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/record_storage.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import hashlib
import json
import os
import shelve
from pathlib import Path
from typing import Dict
from filelock import FileLock
from promptflow.exceptions import PromptflowException
from .constants import ENVIRON_TEST_MODE, RecordMode
class RecordItemMissingException(PromptflowException):
"""Exception raised when record item missing."""
pass
class RecordFileMissingException(PromptflowException):
"""Exception raised when record file missing or invalid."""
pass
class RecordStorage(object):
"""
RecordStorage is used to store the record of node run.
File often stored in .promptflow/node_cache.shelve
Currently only text input/output could be recorded.
Example of cached items:
{
"/record/file/resolved": {
"hash_value": { # hash_value is sha1 of dict, accelerate the search
"input": {
"key1": "value1", # Converted to string, type info dropped
},
"output": "output_convert_to_string",
"output_type": "output_type" # Currently support only simple strings.
}
}
}
"""
_standard_record_folder = ".promptflow"
_standard_record_name = "node_cache.shelve"
_instance = None
def __init__(self, record_file: str = None):
"""
RecordStorage is used to store the record of node run.
"""
self._record_file: Path = None
self.cached_items: Dict[str, Dict[str, Dict[str, object]]] = {}
self.record_file = record_file
@property
def record_file(self) -> Path:
return self._record_file
@record_file.setter
def record_file(self, record_file_input) -> None:
"""
Will load record_file if exist.
"""
if record_file_input == self._record_file:
return
if isinstance(record_file_input, str):
self._record_file = Path(record_file_input).resolve()
elif isinstance(record_file_input, Path):
self._record_file = record_file_input.resolve()
else:
return
if not self._record_file.parts[-1].endswith(RecordStorage._standard_record_name):
record_folder = self._record_file / RecordStorage._standard_record_folder
self._record_file = record_folder / RecordStorage._standard_record_name
else:
record_folder = self._record_file.parent
self._record_file_str = str(self._record_file.resolve())
# cache folder we could create if not exist.
if not record_folder.exists():
record_folder.mkdir(parents=True, exist_ok=True)
# if file exist, load file
if self.exists_record_file(record_folder, self._record_file.parts[-1]):
self._load_file()
else:
self.cached_items = {
self._record_file_str: {},
}
def exists_record_file(self, record_folder, file_name) -> bool:
files = os.listdir(record_folder)
for file in files:
if file.startswith(file_name):
return True
return False
def _write_file(self, hashkey) -> None:
file_content = self.cached_items.get(self._record_file_str, None)
if file_content is not None:
file_content_line = file_content.get(hashkey, None)
if file_content_line is not None:
lock = FileLock(self.record_file.parent / "record_file.lock")
with lock:
saved_dict = shelve.open(self._record_file_str, "c", writeback=False)
saved_dict[hashkey] = file_content_line
saved_dict.close()
else:
raise RecordItemMissingException(f"Record item not found in cache with hashkey {hashkey}.")
else:
raise RecordFileMissingException(
f"This exception should not happen here, but record file is not found {self._record_file_str}."
)
def _load_file(self) -> None:
local_content = self.cached_items.get(self._record_file_str, None)
if not local_content:
if RecordStorage.is_recording_mode():
lock = FileLock(self.record_file.parent / "record_file.lock")
with lock:
if not self.exists_record_file(self.record_file.parent, self.record_file.parts[-1]):
return
self.cached_items[self._record_file_str] = {}
saved_dict = shelve.open(self._record_file_str, "r", writeback=False)
for key, value in saved_dict.items():
self.cached_items[self._record_file_str][key] = value
saved_dict.close()
else:
if not self.exists_record_file(self.record_file.parent, self.record_file.parts[-1]):
return
self.cached_items[self._record_file_str] = {}
saved_dict = shelve.open(self._record_file_str, "r", writeback=False)
for key, value in saved_dict.items():
self.cached_items[self._record_file_str][key] = value
saved_dict.close()
def delete_lock_file(self):
lock_file = self.record_file.parent / "record_file.lock"
if lock_file.exists():
os.remove(lock_file)
def get_record(self, input_dict: Dict) -> object:
"""
Get record from local storage.
:param input_dict: input dict of critical AOAI inputs
:type input_dict: Dict
:raises RecordFileMissingException: Record file not exist
:raises RecordItemMissingException: Record item not exist in record file
:return: original output of node run
:rtype: object
"""
input_dict = self._recursive_create_hashable_args(input_dict)
hash_value: str = hashlib.sha1(str(sorted(input_dict.items())).encode("utf-8")).hexdigest()
current_saved_records: Dict[str, str] = self.cached_items.get(self._record_file_str, None)
if current_saved_records is None:
raise RecordFileMissingException(f"Record file not found {self.record_file}.")
saved_output = current_saved_records.get(hash_value, None)
if saved_output is None:
raise RecordItemMissingException(
f"Record item not found in file {self.record_file}.\n" f"values: {json.dumps(input_dict)}\n"
)
# not all items are reserved in the output dict.
output = saved_output["output"]
output_type = saved_output["output_type"]
if "generator" in output_type:
return self._create_output_generator(output, output_type)
else:
return output
def _recursive_create_hashable_args(self, item):
if isinstance(item, tuple):
return [self._recursive_create_hashable_args(i) for i in item]
if isinstance(item, list):
return [self._recursive_create_hashable_args(i) for i in item]
if isinstance(item, dict):
return {k: self._recursive_create_hashable_args(v) for k, v in item.items()}
elif "module: promptflow.connections" in str(item) or "object at" in str(item):
return []
else:
return item
def _parse_output_generator(self, output):
"""
Special handling for generator type. Since pickle will not work for generator.
Returns the real list for reocrding, and create a generator for original output.
Parse output has a simplified hypothesis: output is simple dict, list or generator,
because a full schema of output is too heavy to handle.
Example: {"answer": <generator>, "a": "b"}, <generator>
"""
output_type = ""
output_value = None
output_generator = None
if isinstance(output, dict):
output_value = {}
output_generator = {}
for item in output.items():
k, v = item
if type(v).__name__ == "generator":
vlist = list(v)
def vgenerator():
for vitem in vlist:
yield vitem
output_value[k] = vlist
output_generator[k] = vgenerator()
output_type = "dict[generator]"
else:
output_value[k] = v
elif type(output).__name__ == "generator":
output_value = list(output)
def generator():
for item in output_value:
yield item
output_generator = generator()
output_type = "generator"
else:
output_value = output
output_generator = None
output_type = type(output).__name__
return output_value, output_generator, output_type
def _create_output_generator(self, output, output_type):
"""
Special handling for generator type.
Returns a generator for original output.
Create output has a simplified hypothesis:
All list with output type generator is treated as generator.
"""
output_generator = None
if output_type == "dict[generator]":
output_generator = {}
for k, v in output.items():
if type(v).__name__ == "list":
def vgenerator():
for item in v:
yield item
output_generator[k] = vgenerator()
else:
output_generator[k] = v
elif output_type == "generator":
def generator():
for item in output:
yield item
output_generator = generator()
return output_generator
def set_record(self, input_dict: Dict, output):
"""
Set record to local storage, always override the old record.
:param input_dict: input dict of critical AOAI inputs
:type input_dict: OrderedDict
:param output: original output of node run
:type output: object
"""
# filter args, object at will not hash
input_dict = self._recursive_create_hashable_args(input_dict)
hash_value: str = hashlib.sha1(str(sorted(input_dict.items())).encode("utf-8")).hexdigest()
current_saved_records: Dict[str, str] = self.cached_items.get(self._record_file_str, None)
output_value, output_generator, output_type = self._parse_output_generator(output)
if current_saved_records is None:
current_saved_records = {}
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
else:
saved_output = current_saved_records.get(hash_value, None)
if saved_output is not None:
if saved_output["output"] == output_value and saved_output["output_type"] == output_type:
if "generator" in output_type:
return output_generator
else:
return output_value
else:
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
else:
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
self.cached_items[self._record_file_str] = current_saved_records
self._write_file(hash_value)
if "generator" in output_type:
return output_generator
else:
return output_value
@classmethod
def get_test_mode_from_environ(cls) -> str:
return os.getenv(ENVIRON_TEST_MODE, RecordMode.LIVE)
@classmethod
def is_recording_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.RECORD
@classmethod
def is_replaying_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.REPLAY
@classmethod
def is_live_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.LIVE
@classmethod
def get_instance(cls, record_file=None) -> "RecordStorage":
"""
Use this to get instance to avoid multiple copies of same record storage.
:param record_file: initiate at first entrance, defaults to None in the first call will raise exception.
:type record_file: str or Path, optional
:return: instance of RecordStorage
:rtype: RecordStorage
"""
# if not in recording mode, return None
if not (RecordStorage.is_recording_mode() or RecordStorage.is_replaying_mode()):
return None
# Create instance if not exist
if cls._instance is None:
if record_file is None:
raise RecordFileMissingException("record_file is value None")
cls._instance = RecordStorage(record_file)
if record_file is not None:
cls._instance.record_file = record_file
return cls._instance
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/constants.py | ENVIRON_TEST_MODE = "PROMPT_FLOW_TEST_MODE"
class RecordMode:
LIVE = "live"
RECORD = "record"
REPLAY = "replay"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/mock_tool.py | import functools
import inspect
from promptflow._core.tool import STREAMING_OPTION_PARAMETER_ATTR, ToolType
from promptflow._core.tracer import TraceType, _create_trace_from_function_call
from .record_storage import RecordFileMissingException, RecordItemMissingException, RecordStorage
# recording array is a global variable to store the function names that need to be recorded
recording_array = ["fetch_text_content_from_url", "my_python_tool"]
def recording_array_extend(items):
global recording_array
recording_array.extend(items)
def recording_array_reset():
global recording_array
recording_array = ["fetch_text_content_from_url", "my_python_tool"]
def _prepare_input_dict(func, args, kwargs):
"""Prepare input dict for record storage"""
if func.__name__ == "partial":
func_wo_partial = func.func
else:
func_wo_partial = func
input_dict = {}
for key in kwargs:
input_dict[key] = kwargs[key]
if type(func).__name__ == "partial":
input_dict["_args"] = func.args
for key in func.keywords:
input_dict[key] = func.keywords[key]
else:
input_dict["_args"] = []
input_dict["_func"] = func_wo_partial.__qualname__
return input_dict
def _replace_tool_rule(func):
"""Replace tool with the following rules."""
global recording_array
if func.__name__ == "partial":
func_wo_partial = func.func
else:
func_wo_partial = func
if func_wo_partial.__qualname__.startswith("AzureOpenAI"):
return True
elif func_wo_partial.__qualname__.startswith("OpenAI"):
return True
elif func_wo_partial.__module__ == "promptflow.tools.aoai":
return True
elif func_wo_partial.__module__ == "promptflow.tools.openai_gpt4v":
return True
elif func_wo_partial.__module__ == "promptflow.tools.openai":
return True
elif func_wo_partial.__qualname__ in recording_array:
return True
else:
return False
def call_func(func, args, kwargs):
input_dict = _prepare_input_dict(func, args, kwargs)
if RecordStorage.is_replaying_mode():
return RecordStorage.get_instance().get_record(input_dict)
# Record mode will record item to record file
elif RecordStorage.is_recording_mode():
try:
# prevent recording the same item twice
obj = RecordStorage.get_instance().get_record(input_dict)
except (RecordItemMissingException, RecordFileMissingException):
# recording the item
obj = RecordStorage.get_instance().set_record(input_dict, func(*args, **kwargs))
return obj
async def call_func_async(func, args, kwargs):
input_dict = _prepare_input_dict(func, args, kwargs)
if RecordStorage.is_replaying_mode():
return RecordStorage.get_instance().get_record(input_dict)
# Record mode will record item to record file
elif RecordStorage.is_recording_mode():
try:
# prevent recording the same item twice
obj = RecordStorage.get_instance().get_record(input_dict)
except (RecordItemMissingException, RecordFileMissingException):
# recording the item
obj = RecordStorage.get_instance().set_record(input_dict, await func(*args, **kwargs))
return obj
def mock_tool(original_tool):
"""
Basically this is the original tool decorator.
The key modification is, at every func(*args, **argv) call. There is a surrounding record/replay logic:
if replay:
return replay:
elif record:
if recorded:
return recorded
call func(*args, **argv) and record the result
Actually it needn't to be such a long function, but tool decorator should not trigger a long stack trace.
"""
def tool(
func=None,
*args_mock,
name: str = None,
description: str = None,
type: str = None,
input_settings=None,
streaming_option_parameter=None,
**kwargs_mock,
):
def tool_decorator(func):
from promptflow.exceptions import UserErrorException
def create_trace(func, args, kwargs):
return _create_trace_from_function_call(func, args=args, kwargs=kwargs, trace_type=TraceType.TOOL)
if inspect.iscoroutinefunction(func):
@functools.wraps(func)
async def decorated_tool(*args, **kwargs):
from promptflow._core.tracer import Tracer
if Tracer.active_instance() is None:
return await call_func_async(func, args, kwargs)
try:
Tracer.push(create_trace(func, args, kwargs))
output = await call_func_async(func, args, kwargs)
return Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
new_f = decorated_tool
else:
@functools.wraps(func)
def decorated_tool(*args, **kwargs):
from promptflow._core.tracer import Tracer
if Tracer.active_instance() is None:
return call_func(func, args, kwargs)
try:
Tracer.push(create_trace(func, args, kwargs))
output = call_func(func, args, kwargs)
return Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
new_f = decorated_tool
if type is not None and type not in [k.value for k in ToolType]:
raise UserErrorException(f"Tool type {type} is not supported yet.")
new_f.__original_function = func
new_f.__tool = None # This will be set when generating the tool definition.
new_f.__name = name
new_f.__description = description
new_f.__type = type
new_f.__input_settings = input_settings
new_f.__extra_info = kwargs_mock
if streaming_option_parameter and isinstance(streaming_option_parameter, str):
setattr(new_f, STREAMING_OPTION_PARAMETER_ATTR, streaming_option_parameter)
return new_f
# tool replacements.
if func is not None:
if not _replace_tool_rule(func):
return original_tool(
func,
*args_mock,
name=name,
description=description,
type=type,
input_settings=input_settings,
**kwargs_mock,
)
return tool_decorator(func)
return original_tool( # no recording for @tool(name="func_name")
func,
*args_mock,
name=name,
description=description,
type=type,
input_settings=input_settings,
**kwargs_mock,
)
return tool
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/__init__.py | from .constants import ENVIRON_TEST_MODE, RecordMode
from .mock_tool import mock_tool, recording_array_extend, recording_array_reset
from .record_storage import RecordFileMissingException, RecordItemMissingException, RecordStorage
__all__ = [
"RecordStorage",
"RecordMode",
"ENVIRON_TEST_MODE",
"RecordFileMissingException",
"RecordItemMissingException",
"mock_tool",
"recording_array_extend",
"recording_array_reset",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/conftest.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import os
import uuid
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Callable, Optional
from unittest.mock import patch
import jwt
import pytest
from azure.core.exceptions import ResourceNotFoundError
from mock import mock
from pytest_mock import MockerFixture
from promptflow._sdk._constants import FlowType, RunStatus
from promptflow._sdk._utils import ClientUserAgentUtil
from promptflow._sdk.entities import Run
from promptflow.azure import PFClient
from promptflow.azure._entities._flow import Flow
from ._azure_utils import get_cred
from .recording_utilities import (
PFAzureIntegrationTestRecording,
SanitizedValues,
VariableRecorder,
get_created_flow_name_from_flow_path,
get_pf_client_for_replay,
is_live,
is_record,
is_replay,
)
FLOWS_DIR = "./tests/test_configs/flows"
EAGER_FLOWS_DIR = "./tests/test_configs/eager_flows"
DATAS_DIR = "./tests/test_configs/datas"
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
def package_scope_in_live_mode() -> str:
"""Determine the scope of some expected sharing fixtures.
We have many tests against flows and runs, and it's very time consuming to create a new flow/run
for each test. So we expect to leverage pytest fixture concept to share flows/runs across tests.
However, we also have replay tests, which require function scope fixture as it will locate the
recording YAML based on the test function info.
Use this function to determine the scope of the fixtures dynamically. For those fixtures that
will request dynamic scope fixture(s), they also need to be dynamic scope.
"""
# package-scope should be enough for Azure tests
return "package" if is_live() else "function"
@pytest.fixture(scope=package_scope_in_live_mode())
def user_object_id() -> str:
if is_replay():
return SanitizedValues.USER_OBJECT_ID
credential = get_cred()
access_token = credential.get_token("https://management.azure.com/.default")
decoded_token = jwt.decode(access_token.token, options={"verify_signature": False})
return decoded_token["oid"]
@pytest.fixture(scope=package_scope_in_live_mode())
def tenant_id() -> str:
if is_replay():
return SanitizedValues.TENANT_ID
credential = get_cred()
access_token = credential.get_token("https://management.azure.com/.default")
decoded_token = jwt.decode(access_token.token, options={"verify_signature": False})
return decoded_token["tid"]
@pytest.fixture(scope=package_scope_in_live_mode())
def ml_client(
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
"""return a machine learning client using default e2e testing workspace"""
from azure.ai.ml import MLClient
return MLClient(
credential=get_cred(),
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
cloud="AzureCloud",
)
@pytest.fixture(scope=package_scope_in_live_mode())
def remote_client(subscription_id: str, resource_group_name: str, workspace_name: str):
from promptflow.azure import PFClient
if is_replay():
client = get_pf_client_for_replay()
else:
client = PFClient(
credential=get_cred(),
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
)
assert "promptflow-sdk" in ClientUserAgentUtil.get_user_agent()
assert "promptflow/" not in ClientUserAgentUtil.get_user_agent()
yield client
@pytest.fixture
def remote_workspace_resource_id(subscription_id: str, resource_group_name: str, workspace_name: str) -> str:
return "azureml:" + RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
@pytest.fixture(scope=package_scope_in_live_mode())
def pf(remote_client):
# do not add annotation here, because PFClient will trigger promptflow.azure imports and break the isolation
# between azure and non-azure tests
yield remote_client
@pytest.fixture
def remote_web_classification_data(remote_client):
from azure.ai.ml.entities import Data
data_name, data_version = "webClassification1", "1"
try:
return remote_client.ml_client.data.get(name=data_name, version=data_version)
except ResourceNotFoundError:
return remote_client.ml_client.data.create_or_update(
Data(name=data_name, version=data_version, path=f"{DATAS_DIR}/webClassification1.jsonl", type="uri_file")
)
@pytest.fixture(scope="session")
def runtime(runtime_name: str) -> str:
return runtime_name
PROMPTFLOW_ROOT = Path(__file__) / "../../.."
MODEL_ROOT = Path(PROMPTFLOW_ROOT / "tests/test_configs/flows")
@pytest.fixture
def flow_serving_client_remote_connection(mocker: MockerFixture, remote_workspace_resource_id):
from promptflow._sdk._serving.app import create_app as create_serving_app
model_path = (Path(MODEL_ROOT) / "basic-with-connection").resolve().absolute().as_posix()
mocker.patch.dict(os.environ, {"PROMPTFLOW_PROJECT_PATH": model_path})
mocker.patch.dict(os.environ, {"USER_AGENT": "test-user-agent"})
app = create_serving_app(
config={"connection.provider": remote_workspace_resource_id},
environment_variables={"API_TYPE": "${azure_open_ai_connection.api_type}"},
)
app.config.update(
{
"TESTING": True,
}
)
return app.test_client()
@pytest.fixture
def flow_serving_client_with_prt_config_env(
mocker: MockerFixture, subscription_id, resource_group_name, workspace_name
): # noqa: E501
connections = {
"PRT_CONFIG_OVERRIDE": f"deployment.subscription_id={subscription_id},"
f"deployment.resource_group={resource_group_name},"
f"deployment.workspace_name={workspace_name},"
"app.port=8088",
}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def flow_serving_client_with_connection_provider_env(mocker: MockerFixture, remote_workspace_resource_id):
connections = {"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def flow_serving_client_with_aml_resource_id_env(mocker: MockerFixture, remote_workspace_resource_id):
aml_resource_id = "{}/onlineEndpoints/{}/deployments/{}".format(remote_workspace_resource_id, "myendpoint", "blue")
connections = {"AML_DEPLOYMENT_RESOURCE_ID": aml_resource_id}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def serving_client_with_connection_name_override(mocker: MockerFixture, remote_workspace_resource_id):
connections = {
"aoai_connection": "azure_open_ai_connection",
"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id,
}
return create_serving_client_with_connections("llm_connection_override", mocker, connections)
@pytest.fixture
def serving_client_with_connection_data_override(mocker: MockerFixture, remote_workspace_resource_id):
model_name = "llm_connection_override"
model_path = (Path(MODEL_ROOT) / model_name).resolve().absolute()
# load arm connection template
connection_arm_template = model_path.joinpath("connection_arm_template.json").read_text()
connections = {
"aoai_connection": connection_arm_template,
"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id,
}
return create_serving_client_with_connections(model_name, mocker, connections)
def create_serving_client_with_connections(model_name, mocker: MockerFixture, connections: dict = {}):
from promptflow._sdk._serving.app import create_app as create_serving_app
model_path = (Path(MODEL_ROOT) / model_name).resolve().absolute().as_posix()
mocker.patch.dict(os.environ, {"PROMPTFLOW_PROJECT_PATH": model_path})
mocker.patch.dict(
os.environ,
{
**connections,
},
)
# Set credential to None for azureml extension type
# As we mock app in github workflow, which do not have managed identity credential
func = "promptflow._sdk._serving.extension.azureml_extension._get_managed_identity_credential_with_retry"
with mock.patch(func) as mock_cred_func:
mock_cred_func.return_value = None
app = create_serving_app(
environment_variables={"API_TYPE": "${azure_open_ai_connection.api_type}"},
extension_type="azureml",
)
app.config.update(
{
"TESTING": True,
}
)
return app.test_client()
@pytest.fixture(scope=package_scope_in_live_mode())
def variable_recorder() -> VariableRecorder:
yield VariableRecorder()
@pytest.fixture(scope=package_scope_in_live_mode())
def randstr(variable_recorder: VariableRecorder) -> Callable[[str], str]:
"""Return a "random" UUID."""
def generate_random_string(variable_name: str) -> str:
random_string = str(uuid.uuid4())
if is_live():
return random_string
elif is_replay():
return variable_name
else:
return variable_recorder.get_or_record_variable(variable_name, random_string)
return generate_random_string
@pytest.fixture(scope=package_scope_in_live_mode())
def vcr_recording(
request: pytest.FixtureRequest, user_object_id: str, tenant_id: str, variable_recorder: VariableRecorder
) -> Optional[PFAzureIntegrationTestRecording]:
"""Fixture to record or replay network traffic.
If the test mode is "live", nothing will happen.
If the test mode is "record" or "replay", this fixture will locate a YAML (recording) file
based on the test file, class and function name, write to (record) or read from (replay) the file.
"""
if is_live():
yield None
else:
recording = PFAzureIntegrationTestRecording.from_test_case(
test_class=request.cls,
test_func_name=request.node.name,
user_object_id=user_object_id,
tenant_id=tenant_id,
variable_recorder=variable_recorder,
)
recording.enter_vcr()
request.addfinalizer(recording.exit_vcr)
yield recording
# we expect this fixture only work when running live test without recording
# when recording, we don't want to record any application insights secrets
# when replaying, we also don't need this
@pytest.fixture(autouse=not is_live())
def mock_appinsights_log_handler(mocker: MockerFixture) -> None:
dummy_logger = logging.getLogger("dummy")
mocker.patch("promptflow._sdk._telemetry.telemetry.get_telemetry_logger", return_value=dummy_logger)
return
@pytest.fixture
def single_worker_thread_pool() -> None:
"""Mock to use one thread for thread pool executor.
VCR.py cannot record network traffic in other threads, and we have multi-thread operations
during resolving the flow. Mock it using one thread to make VCR.py work.
"""
def single_worker_thread_pool_executor(*args, **kwargs):
return ThreadPoolExecutor(max_workers=1)
if is_live():
yield
else:
with patch(
"promptflow.azure.operations._run_operations.ThreadPoolExecutor",
new=single_worker_thread_pool_executor,
):
yield
@pytest.fixture
def mock_set_headers_with_user_aml_token(mocker: MockerFixture) -> None:
"""Mock set aml-user-token operation.
There will be requests fetching cloud metadata during retrieving AML token, which will break during replay.
As the logic comes from azure-ai-ml, changes in Prompt Flow can hardly affect it, mock it here.
"""
if not is_live():
mocker.patch(
"promptflow.azure._restclient.flow_service_caller.FlowServiceCaller._set_headers_with_user_aml_token"
)
yield
@pytest.fixture
def mock_get_azure_pf_client(mocker: MockerFixture, remote_client) -> None:
"""Mock PF Azure client to avoid network traffic during replay test."""
if not is_live():
mocker.patch(
"promptflow._cli._pf_azure._run._get_azure_pf_client",
return_value=remote_client,
)
mocker.patch(
"promptflow._cli._pf_azure._flow._get_azure_pf_client",
return_value=remote_client,
)
yield
@pytest.fixture(scope=package_scope_in_live_mode())
def mock_get_user_identity_info(user_object_id: str, tenant_id: str) -> None:
"""Mock get user object id and tenant id, currently used in flow list operation."""
if not is_live():
with patch(
"promptflow.azure._restclient.flow_service_caller.FlowServiceCaller._get_user_identity_info",
return_value=(user_object_id, tenant_id),
):
yield
else:
yield
@pytest.fixture(scope=package_scope_in_live_mode())
def created_flow(pf: PFClient, randstr: Callable[[str], str], variable_recorder: VariableRecorder) -> Flow:
"""Create a flow for test."""
flow_display_name = randstr("flow_display_name")
flow_source = FLOWS_DIR + "/simple_hello_world/"
description = "test flow description"
tags = {"owner": "sdk-test"}
result = pf.flows.create_or_update(
flow=flow_source, display_name=flow_display_name, type=FlowType.STANDARD, description=description, tags=tags
)
remote_flow_dag_path = result.path
# make sure the flow is created successfully
assert pf.flows._storage_client._check_file_share_file_exist(remote_flow_dag_path) is True
assert result.display_name == flow_display_name
assert result.type == FlowType.STANDARD
assert result.tags == tags
assert result.path.endswith("flow.dag.yaml")
# flow in Azure will have different file share name with timestamp
# and this is a client-side behavior, so we need to sanitize this in recording
# so extract this during record test
if is_record():
flow_name_const = "flow_name"
flow_name = get_created_flow_name_from_flow_path(result.path)
variable_recorder.get_or_record_variable(flow_name_const, flow_name)
yield result
@pytest.fixture(scope=package_scope_in_live_mode())
def created_batch_run_without_llm(pf: PFClient, randstr: Callable[[str], str], runtime: str) -> Run:
"""Create a batch run that does not require LLM."""
name = randstr("batch_run_name")
run = pf.run(
# copy test_configs/flows/simple_hello_world to a separate folder
# as pf.run will generate .promptflow/flow.tools.json
# it will affect Azure file share upload logic and replay test
flow=f"{FLOWS_DIR}/hello-world",
data=f"{DATAS_DIR}/webClassification3.jsonl",
column_mapping={"name": "${data.url}"},
name=name,
display_name="sdk-cli-test-fixture-batch-run-without-llm",
)
run = pf.runs.stream(run=name)
assert run.status == RunStatus.COMPLETED
yield run
@pytest.fixture(scope=package_scope_in_live_mode())
def simple_eager_run(pf: PFClient, randstr: Callable[[str], str]) -> Run:
"""Create a simple eager run."""
run = pf.run(
flow=f"{EAGER_FLOWS_DIR}/simple_with_req",
data=f"{DATAS_DIR}/simple_eager_flow_data.jsonl",
name=randstr("name"),
)
pf.runs.stream(run)
run = pf.runs.get(run)
assert run.status == RunStatus.COMPLETED
yield run
@pytest.fixture(scope=package_scope_in_live_mode())
def created_eval_run_without_llm(
pf: PFClient, randstr: Callable[[str], str], runtime: str, created_batch_run_without_llm: Run
) -> Run:
"""Create a evaluation run against batch run without LLM dependency."""
name = randstr("eval_run_name")
run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
data=f"{DATAS_DIR}/webClassification3.jsonl",
run=created_batch_run_without_llm,
column_mapping={"groundtruth": "${data.answer}", "prediction": "${run.outputs.result}"},
runtime=runtime,
name=name,
display_name="sdk-cli-test-fixture-eval-run-without-llm",
)
run = pf.runs.stream(run=name)
assert run.status == RunStatus.COMPLETED
yield run
@pytest.fixture(scope=package_scope_in_live_mode())
def created_failed_run(pf: PFClient, randstr: Callable[[str], str], runtime: str) -> Run:
"""Create a failed run."""
name = randstr("failed_run_name")
run = pf.run(
flow=f"{FLOWS_DIR}/partial_fail",
data=f"{DATAS_DIR}/webClassification3.jsonl",
runtime=runtime,
name=name,
display_name="sdk-cli-test-fixture-failed-run",
)
# set raise_on_error to False to promise returning something
run = pf.runs.stream(run=name, raise_on_error=False)
assert run.status == RunStatus.FAILED
yield run
@pytest.fixture(autouse=not is_live())
def mock_vcrpy_for_httpx() -> None:
# there is a known issue in vcrpy handling httpx response: https://github.com/kevin1024/vcrpy/pull/591
# the related code change has not been merged, so we need such a fixture for patch
def _transform_headers(httpx_response):
out = {}
for key, var in httpx_response.headers.raw:
decoded_key = key.decode("utf-8")
decoded_var = var.decode("utf-8")
if decoded_key.lower() == "content-encoding" and decoded_var in ("gzip", "deflate"):
continue
out.setdefault(decoded_key, [])
out[decoded_key].append(decoded_var)
return out
with patch("vcr.stubs.httpx_stubs._transform_headers", new=_transform_headers):
yield
@pytest.fixture(autouse=not is_live())
def mock_to_thread() -> None:
# https://docs.python.org/3/library/asyncio-task.html#asyncio.to_thread
# to_thread actually uses a separate thread, which will break mocks
# so we need to mock it to avoid using a separate thread
# this is only for AsyncRunDownloader.to_thread
async def to_thread(func, /, *args, **kwargs):
func(*args, **kwargs)
with patch(
"promptflow.azure.operations._async_run_downloader.to_thread",
new=to_thread,
):
yield
@pytest.fixture
def mock_isinstance_for_mock_datastore() -> None:
"""Mock built-in function isinstance.
We have an isinstance check during run download for datastore type for better error message;
while our mock datastore in replay mode is not a valid type, so mock it with strict condition.
"""
if not is_replay():
yield
else:
from azure.ai.ml.entities._datastore.azure_storage import AzureBlobDatastore
from .recording_utilities.utils import MockDatastore
original_isinstance = isinstance
def mock_isinstance(*args):
if original_isinstance(args[0], MockDatastore) and args[1] == AzureBlobDatastore:
return True
return original_isinstance(*args)
with patch("builtins.isinstance", new=mock_isinstance):
yield
@pytest.fixture(autouse=True)
def mock_check_latest_version() -> None:
"""Mock check latest version.
As CI uses docker, it will always trigger this check behavior, and we don't have recording for this;
and this will hit many unknown issue with vcrpy.
"""
with patch("promptflow._utils.version_hint_utils.check_latest_version", new=lambda: None):
yield
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/_azure_utils.py | import signal
from azure.identity import AzureCliCredential, DefaultAzureCredential
def get_cred():
"""get credential for azure storage"""
# resolve requests
try:
credential = AzureCliCredential()
token = credential.get_token("https://management.azure.com/.default")
except Exception:
credential = DefaultAzureCredential()
# ensure we can get token
token = credential.get_token("https://management.azure.com/.default")
assert token is not None
return credential
PYTEST_TIMEOUT_METHOD = "signal" if hasattr(signal, "SIGALRM") else "thread" # use signal when os support SIGALRM
DEFAULT_TEST_TIMEOUT = 10 * 60 # 10mins
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_run_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import json
import shutil
from logging import Logger
from pathlib import Path
from tempfile import TemporaryDirectory
from time import sleep
from typing import Callable
from unittest.mock import MagicMock, patch
import pandas as pd
import pydash
import pytest
from promptflow._sdk._constants import DownloadedRun, RunStatus
from promptflow._sdk._errors import InvalidRunError, InvalidRunStatusError, RunNotFoundError
from promptflow._sdk._load_functions import load_run
from promptflow._sdk.entities import Run
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow._utils.yaml_utils import load_yaml
from promptflow.azure import PFClient
from promptflow.azure._constants._flow import ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from promptflow.azure._entities._flow import Flow
from promptflow.exceptions import UserErrorException
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
FLOWS_DIR = "./tests/test_configs/flows"
EAGER_FLOWS_DIR = "./tests/test_configs/eager_flows"
RUNS_DIR = "./tests/test_configs/runs"
DATAS_DIR = "./tests/test_configs/datas"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestFlowRun:
def test_run_bulk(self, pf, runtime: str, randstr: Callable[[str], str]):
name = randstr("name")
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
def test_run_bulk_from_yaml(self, pf, runtime: str, randstr: Callable[[str], str]):
run_id = randstr("run_id")
run = load_run(
source=f"{RUNS_DIR}/sample_bulk_run_cloud.yaml",
params_override=[{"name": run_id, "runtime": runtime}],
)
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_basic_evaluation(self, pf, runtime: str, randstr: Callable[[str], str]):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=data_path,
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("batch_run_name"),
)
assert isinstance(run, Run)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
eval_run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
data=data_path,
run=run,
column_mapping={"groundtruth": "${data.answer}", "prediction": "${run.outputs.category}"},
runtime=runtime,
name=randstr("eval_run_name"),
)
assert isinstance(eval_run, Run)
eval_run = pf.runs.stream(run=eval_run.name)
assert eval_run.status == RunStatus.COMPLETED
def test_basic_evaluation_without_data(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification3.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("batch_run_name"),
)
assert isinstance(run, Run)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
eval_run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
run=run,
column_mapping={
# evaluation reference run.inputs
"groundtruth": "${run.inputs.url}",
"prediction": "${run.outputs.category}",
},
runtime=runtime,
name=randstr("eval_run_name"),
)
assert isinstance(eval_run, Run)
eval_run = pf.runs.stream(run=eval_run.name)
assert eval_run.status == RunStatus.COMPLETED
def test_run_bulk_with_remote_flow(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str], created_flow: Flow
):
"""Test run bulk with remote workspace flow."""
name = randstr("name")
run = pf.run(
flow=f"azureml:{created_flow.name}",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
def test_run_bulk_with_registry_flow(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str], registry_name: str
):
"""Test run bulk with remote registry flow."""
name = randstr("name")
run = pf.run(
flow=f"azureml://registries/{registry_name}/models/simple_hello_world/versions/202311241",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
# test invalid registry flow
with pytest.raises(UserErrorException, match="Invalid remote flow pattern, got"):
pf.run(
flow="azureml://registries/no-flow",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
def test_run_with_connection_overwrite(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
connections={"classify_with_llm": {"connection": "azure_open_ai", "model": "gpt-3.5-turbo"}},
runtime=runtime,
name=randstr("name"),
)
assert isinstance(run, Run)
def test_run_with_env_overwrite(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_run_display_name_with_macro(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run.display_name = "my_display_name_${variant_id}_${timestamp}"
run = pf.runs.create_or_update(run=run)
assert run.display_name.startswith("my_display_name_variant_0_")
assert "${timestamp}" not in run.display_name
assert isinstance(run, Run)
def test_default_run_display_name(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run = pf.runs.create_or_update(run=run)
assert run.display_name == run.name
assert isinstance(run, Run)
def test_run_with_remote_data(
self, pf, runtime: str, remote_web_classification_data, randstr: Callable[[str], str]
):
# run with arm id
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"azureml:{remote_web_classification_data.id}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name1"),
)
assert isinstance(run, Run)
# run with name version
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"azureml:{remote_web_classification_data.name}:{remote_web_classification_data.version}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name2"),
)
assert isinstance(run, Run)
# TODO: confirm whether this test is a end-to-end test
def test_run_bulk_not_exist(self, pf, runtime: str, randstr: Callable[[str], str]):
test_data = f"{DATAS_DIR}/webClassification1.jsonl"
with pytest.raises(UserErrorException) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
# data with file:/// prefix is not supported, should raise not exist error
data=f"file:///{Path(test_data).resolve().absolute()}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
assert "does not exist" in str(e.value)
def test_list_runs(self, pf):
runs = pf.runs.list(max_results=10)
for run in runs:
print(json.dumps(run._to_dict(), indent=4))
assert len(runs) == 10
def test_show_run(self, pf: PFClient, created_eval_run_without_llm: Run):
run = pf.runs.get(run=created_eval_run_without_llm.name)
run_dict = run._to_dict()
print(json.dumps(run_dict, indent=4))
# it's hard to assert with precise value, so just assert existence, type and length
expected_keys = [
"name",
"created_on",
"status",
"display_name",
"description",
"tags",
"properties",
"creation_context",
"start_time",
"end_time",
"duration",
"portal_url",
"data",
"output",
"run",
]
for expected_key in expected_keys:
assert expected_key in run_dict
if expected_key == "description":
assert run_dict[expected_key] is None
elif expected_key in {"tags", "properties", "creation_context"}:
assert isinstance(run_dict[expected_key], dict)
else:
assert isinstance(run_dict[expected_key], str)
assert len(run_dict[expected_key]) > 0
def test_show_run_details(self, pf: PFClient, created_batch_run_without_llm: Run):
# get first 2 results
details = pf.get_details(run=created_batch_run_without_llm.name, max_results=2)
assert details.shape[0] == 2
# get first 10 results while it only has 3
details = pf.get_details(run=created_batch_run_without_llm.name, max_results=10)
assert details.shape[0] == 3
# get all results
details = pf.get_details(run=created_batch_run_without_llm.name, all_results=True)
assert details.shape[0] == 3
# get all results even if max_results is set to 2
details = pf.get_details(
run=created_batch_run_without_llm.name,
max_results=2,
all_results=True,
)
assert details.shape[0] == 3
def test_show_metrics(self, pf: PFClient, created_eval_run_without_llm: Run):
metrics = pf.runs.get_metrics(run=created_eval_run_without_llm.name)
print(json.dumps(metrics, indent=4))
# as we use unmatched data, we can assert the accuracy is 0
assert metrics == {"accuracy": 0.0}
def test_stream_invalid_run_logs(self, pf, randstr: Callable[[str], str]):
# test get invalid run name
non_exist_run = randstr("non_exist_run")
with pytest.raises(RunNotFoundError, match=f"Run {non_exist_run!r} not found"):
pf.runs.stream(run=non_exist_run)
def test_stream_run_logs(self, pf: PFClient, created_batch_run_without_llm: Run):
run = pf.runs.stream(run=created_batch_run_without_llm.name)
assert run.status == RunStatus.COMPLETED
def test_stream_failed_run_logs(self, pf: PFClient, created_failed_run: Run, capfd: pytest.CaptureFixture):
# (default) raise_on_error=True
with pytest.raises(InvalidRunStatusError):
pf.stream(run=created_failed_run.name)
# raise_on_error=False
pf.stream(run=created_failed_run.name, raise_on_error=False)
out, _ = capfd.readouterr()
assert "The input for batch run is incorrect. Couldn't find these mapping relations: ${data.key}" in out
def test_failed_run_to_dict_exclude(self, pf: PFClient, created_failed_run: Run):
failed_run = pf.runs.get(run=created_failed_run.name)
# Azure run object reference a dict, use deepcopy to avoid unexpected modification
default = copy.deepcopy(failed_run._to_dict())
exclude = failed_run._to_dict(exclude_additional_info=True, exclude_debug_info=True)
assert "additionalInfo" in default["error"]["error"] and "additionalInfo" not in exclude["error"]["error"]
assert "debugInfo" in default["error"]["error"] and "debugInfo" not in exclude["error"]["error"]
@pytest.mark.skipif(
condition=not is_live(),
reason="cannot differ the two requests to run history in replay mode.",
)
def test_archive_and_restore_run(self, pf: PFClient, created_batch_run_without_llm: Run):
from promptflow._sdk._constants import RunHistoryKeys
run_meta_data = RunHistoryKeys.RunMetaData
hidden = RunHistoryKeys.HIDDEN
run_id = created_batch_run_without_llm.name
# test archive
pf.runs.archive(run=run_id)
run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
assert run_data[hidden] is True
# test restore
pf.runs.restore(run=run_id)
run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
assert run_data[hidden] is False
def test_update_run(self, pf: PFClient, created_batch_run_without_llm: Run, randstr: Callable[[str], str]):
run_id = created_batch_run_without_llm.name
test_mark = randstr("test_mark")
new_display_name = f"test_display_name_{test_mark}"
new_description = f"test_description_{test_mark}"
new_tags = {"test_tag": test_mark}
run = pf.runs.update(
run=run_id,
display_name=new_display_name,
description=new_description,
tags=new_tags,
)
# sleep to wait for update to take effect
sleep(3)
assert run.display_name == new_display_name
assert run.description == new_description
assert run.tags["test_tag"] == test_mark
# test wrong type of parameters won't raise error, just log warnings and got ignored
run = pf.runs.update(
run=run_id,
tags={"test_tag": {"a": 1}},
)
# sleep to wait for update to take effect
sleep(3)
assert run.display_name == new_display_name
assert run.description == new_description
assert run.tags["test_tag"] == test_mark
def test_cancel_run(self, pf, runtime: str, randstr: Callable[[str], str]):
# create a run
run_name = randstr("name")
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=run_name,
)
pf.runs.cancel(run=run_name)
sleep(3)
run = pf.runs.get(run=run_name)
# the run status might still be cancel requested, but it should be canceled eventually
assert run.status in [RunStatus.CANCELED, RunStatus.CANCEL_REQUESTED]
@pytest.mark.skipif(
condition=not is_live(), reason="request uri contains temp folder name, need some time to sanitize."
)
def test_run_with_additional_includes(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_additional_include",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
# Test additional includes don't exist
with pytest.raises(ValueError) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_invalid_additional_include",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name_invalid"),
)
assert "Unable to find additional include ../invalid/file/path" in str(e.value)
@pytest.mark.skip(reason="Cannot find tools of the flow with symbolic.")
def test_run_with_symbolic(self, remote_client, pf, runtime, prepare_symbolic_flow):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_symbolic",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
)
remote_client.runs.stream(run=run.name)
def test_run_bulk_without_retry(self, remote_client):
from azure.core.exceptions import ServiceResponseError
from azure.core.pipeline.transport._requests_basic import RequestsTransport
from azure.core.rest._requests_basic import RestRequestsTransportResponse
from requests import Response
from promptflow.azure._restclient.flow.models import SubmitBulkRunRequest
from promptflow.azure._restclient.flow_service_caller import FlowRequestException, FlowServiceCaller
from promptflow.azure.operations import RunOperations
mock_run = MagicMock()
mock_run._runtime = "fake_runtime"
mock_run._to_rest_object.return_value = SubmitBulkRunRequest()
mock_run._use_remote_flow = False
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
mock_request.side_effect = ServiceResponseError(
"Connection aborted.",
error=ConnectionResetError(10054, "An existing connection was forcibly closed", None, 10054, None),
)
with pytest.raises(ServiceResponseError):
remote_client.runs.create_or_update(run=mock_run)
# won't retry connection error since POST without response code is not retryable according to
# retry policy
assert mock_request.call_count == 1
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
fake_response = Response()
# won't retry 500
fake_response.status_code = 500
fake_response._content = b'{"error": "error"}'
fake_response._content_consumed = True
mock_request.return_value = RestRequestsTransportResponse(
request=None,
internal_response=fake_response,
)
with pytest.raises(FlowRequestException):
remote_client.runs.create_or_update(run=mock_run)
assert mock_request.call_count == 1
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
fake_response = Response()
# will retry 503
fake_response.status_code = 503
fake_response._content = b'{"error": "error"}'
fake_response._content_consumed = True
mock_request.return_value = RestRequestsTransportResponse(
request=None,
internal_response=fake_response,
)
with pytest.raises(FlowRequestException):
remote_client.runs.create_or_update(run=mock_run)
assert mock_request.call_count == 4
def test_pf_run_with_env_var(self, pf, randstr: Callable[[str], str]):
from promptflow.azure.operations import RunOperations
def create_or_update(run, **kwargs):
# make run.flow a datastore path uri, so that it can be parsed by AzureMLDatastorePathUri
run.flow = "azureml://datastores/workspaceblobstore/paths/LocalUpload/not/important/path"
return run
with patch.object(RunOperations, "create_or_update") as mock_create_or_update:
mock_create_or_update.side_effect = create_or_update
env_var = {"API_BASE": "${azure_open_ai_connection.api_base}"}
run = pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
environment_variables=env_var,
name=randstr("name"),
)
assert run._to_rest_object().environment_variables == env_var
def test_automatic_runtime(self, pf, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
def submit(*args, **kwargs):
body = kwargs.get("body", None)
assert body.runtime_name == "automatic"
assert body.vm_size is None
assert body.max_idle_time_seconds is None
return body
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(RunOperations, "get"):
mock_submit.side_effect = submit
# no runtime provided, will use automatic runtime
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name1"),
)
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(RunOperations, "get"):
mock_submit.side_effect = submit
# automatic is a reserved runtime name, will use automatic runtime if specified.
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime="automatic",
name=randstr("name2"),
)
def test_automatic_runtime_with_resources(self, pf, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow.models import SessionSetupModeEnum
source = f"{RUNS_DIR}/sample_bulk_run_with_resources.yaml"
run_id = randstr("run_id")
run = load_run(
source=source,
params_override=[{"name": run_id}],
)
rest_run = run._to_rest_object()
assert rest_run.vm_size == "Standard_D2"
assert rest_run.max_idle_time_seconds == 3600
assert rest_run.session_setup_mode == SessionSetupModeEnum.SYSTEM_WAIT
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_run_data_not_provided(self, pf, randstr: Callable[[str], str]):
with pytest.raises(UserErrorException) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
name=randstr("name"),
)
assert "at least one of data or run must be provided" in str(e)
def test_run_without_dump(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._orm.run_info import RunInfo
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
# cloud run should not dump to database
with pytest.raises(RunNotFoundError):
RunInfo.get(run.name)
def test_input_mapping_with_dict(self, pf, runtime: str, randstr: Callable[[str], str]):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
run = pf.run(
flow=f"{FLOWS_DIR}/flow_with_dict_input",
data=data_path,
column_mapping=dict(key={"a": 1}, extra="${data.url}"),
runtime=runtime,
name=randstr("name"),
)
assert '"{\\"a\\": 1}"' in run.properties["azureml.promptflow.inputs_mapping"]
run = pf.runs.stream(run=run)
assert run.status == "Completed"
def test_get_invalid_run_cases(self, pf, randstr: Callable[[str], str]):
# test get invalid run type
with pytest.raises(InvalidRunError, match="expected 'str' or 'Run' object"):
pf.runs.get(run=object())
# test get invalid run name
non_exist_run = randstr("non_exist_run")
with pytest.raises(RunNotFoundError, match=f"Run {non_exist_run!r} not found"):
pf.runs.get(run=non_exist_run)
# TODO: need to confirm whether this is an end-to-end test
def test_exp_id(self):
with TemporaryDirectory() as tmp_dir:
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow dir with space")
run = Run(
flow=Path(f"{tmp_dir}/flow dir with space"),
data=f"{DATAS_DIR}/webClassification3.jsonl",
)
rest_run = run._to_rest_object()
assert rest_run.run_experiment_name == "flow_dir_with_space"
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow-dir-with-dash")
run = Run(
flow=Path(f"{tmp_dir}/flow-dir-with-dash"),
data=f"{DATAS_DIR}/webClassification3.jsonl",
)
rest_run = run._to_rest_object()
assert rest_run.run_experiment_name == "flow_dir_with_dash"
def test_tools_json_ignored(self, pf, randstr: Callable[[str], str]):
from azure.ai.ml._artifacts._blob_storage_helper import BlobStorageClient
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
files_to_upload = []
def fake_upload_file(storage_client, source: str, dest, *args, **kwargs):
files_to_upload.append(source)
storage_client.uploaded_file_count += 1
with patch("azure.ai.ml._utils._asset_utils.upload_file") as mock_upload_file, patch.object(
FlowServiceCaller, "submit_bulk_run"
), patch.object(BlobStorageClient, "_set_confirmation_metadata"), patch.object(RunOperations, "get"):
mock_upload_file.side_effect = fake_upload_file
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
pf.run(
flow=f"{FLOWS_DIR}/flow_with_dict_input",
data=data_path,
column_mapping={"key": {"value": "1"}, "url": "${data.url}"},
runtime="fake_runtime",
name=randstr("name"),
)
# make sure .promptflow/flow.tools.json not uploaded
for f in files_to_upload:
if ".promptflow/flow.tools.json" in f:
raise Exception(f"flow.tools.json should not be uploaded, got {f}")
def test_flow_id_in_submission(self, pf, runtime: str, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
flow_path = f"{FLOWS_DIR}/print_env_var"
flow_lineage_id = get_flow_lineage_id(flow_path)
flow_session_id = pf._runs._get_session_id(flow_path)
def submit(*args, **kwargs):
body = kwargs.get("body", None)
assert flow_session_id == body.session_id
assert flow_lineage_id == body.flow_lineage_id
return body
# flow session id is same with or without session creation
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(
RunOperations, "get"
), patch.object(FlowServiceCaller, "create_flow_session"):
mock_submit.side_effect = submit
pf.run(
flow=flow_path,
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(
RunOperations, "get"
), patch.object(FlowServiceCaller, "create_flow_session"):
mock_submit.side_effect = submit
# no runtime provided, will use automatic runtime
pf.run(
flow=flow_path,
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name2"),
)
def test_run_submission_exception(self, pf):
from azure.core.exceptions import HttpResponseError
from promptflow.azure._restclient.flow.operations import BulkRunsOperations
from promptflow.azure._restclient.flow_service_caller import FlowRequestException, FlowServiceCaller
def fake_submit(*args, **kwargs):
headers = kwargs.get("headers", None)
request_id_in_headers = headers["x-ms-client-request-id"]
# request id in headers should be same with request id in service caller
assert request_id_in_headers == pf.runs._service_caller._request_id
raise HttpResponseError("customized error message.")
with patch.object(BulkRunsOperations, "submit_bulk_run") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
mock_request.side_effect = fake_submit
with pytest.raises(FlowRequestException) as e:
original_request_id = pf.runs._service_caller._request_id
pf.runs._service_caller.submit_bulk_run(
subscription_id="fake_subscription_id",
resource_group_name="fake_resource_group",
workspace_name="fake_workspace_name",
)
# request id has been updated
assert original_request_id != pf.runs._service_caller._request_id
# original error message should be included in FlowRequestException
assert "customized error message" in str(e.value)
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
def test_get_detail_against_partial_fail_run(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
run = pf.run(
flow=f"{FLOWS_DIR}/partial_fail",
data=f"{FLOWS_DIR}/partial_fail/data.jsonl",
runtime=runtime,
name=randstr("name"),
)
pf.runs.stream(run=run.name)
detail = pf.get_details(run=run.name)
assert len(detail) == 3
# TODO: seems another unit test...
def test_vnext_workspace_base_url(self):
from promptflow.azure._restclient.service_caller_factory import _FlowServiceCallerFactory
mock_workspace = MagicMock()
mock_workspace.discovery_url = "https://promptflow.azure-api.net/discovery/workspaces/fake_workspace_id"
service_caller = _FlowServiceCallerFactory.get_instance(
workspace=mock_workspace, credential=MagicMock(), operation_scope=MagicMock()
)
assert service_caller.caller._client._base_url == "https://promptflow.azure-api.net/"
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_download_run(self, pf: PFClient, created_batch_run_without_llm: Run):
expected_files = [
DownloadedRun.RUN_METADATA_FILE_NAME,
DownloadedRun.LOGS_FILE_NAME,
DownloadedRun.METRICS_FILE_NAME,
f"{DownloadedRun.SNAPSHOT_FOLDER}/flow.dag.yaml",
]
with TemporaryDirectory() as tmp_dir:
pf.runs.download(run=created_batch_run_without_llm.name, output=tmp_dir)
for file in expected_files:
assert Path(tmp_dir, created_batch_run_without_llm.name, file).exists()
def test_request_id_when_making_http_requests(self, pf, runtime: str, randstr: Callable[[str], str]):
from azure.core.exceptions import HttpResponseError
from promptflow.azure._restclient.flow.operations import BulkRunsOperations
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
request_ids = set()
def fake_submit(*args, **kwargs):
headers = kwargs.get("headers", None)
request_id_in_headers = headers["x-ms-client-request-id"]
# request id in headers should be same with request id in service caller
assert request_id_in_headers == pf.runs._service_caller._request_id
# request id in request is same request id in collected logs
assert request_id_in_headers in request_ids
raise HttpResponseError("customized error message.")
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
request_ids.add(request_id)
with patch.object(BulkRunsOperations, "submit_bulk_run") as mock_request, patch.object(
Logger, "info"
) as mock_logger:
mock_logger.side_effect = check_inner_call
mock_request.side_effect = fake_submit
with pytest.raises(FlowRequestException) as e:
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
# request id in service caller is same request id in collected logs
assert pf.runs._service_caller._request_id in request_ids
# only 1 request id generated in logs
assert len(request_ids) == 1
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
old_request_id = request_ids.pop()
with pytest.raises(FlowRequestException) as e:
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
# request id in service caller is same request id in collected logs
assert pf.runs._service_caller._request_id in request_ids
# request id is not same with before
assert old_request_id not in request_ids
# only 1 request id generated in logs
assert len(request_ids) == 1
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
def test_get_details_against_partial_completed_run(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str]
) -> None:
flow_mod2 = f"{FLOWS_DIR}/mod-n/two"
flow_mod3 = f"{FLOWS_DIR}/mod-n/three"
data_path = f"{DATAS_DIR}/numbers.jsonl"
# batch run against data
run1 = pf.run(
flow=flow_mod2,
data=data_path,
column_mapping={"number": "${data.value}"},
runtime=runtime,
name=randstr("run1"),
)
pf.runs.stream(run1)
details1 = pf.get_details(run1)
assert len(details1) == 20
assert len(details1[details1["outputs.output"].notnull()]) == 10
# assert to ensure inputs and outputs are aligned
for _, row in details1.iterrows():
if pd.notnull(row["outputs.output"]):
assert int(row["inputs.number"]) == int(row["outputs.output"])
# batch run against previous run
run2 = pf.run(
flow=flow_mod3,
run=run1,
column_mapping={"number": "${run.outputs.output}"},
runtime=runtime,
name=randstr("run2"),
)
pf.runs.stream(run2)
details2 = pf.get_details(run2)
assert len(details2) == 10
assert len(details2[details2["outputs.output"].notnull()]) == 4
# assert to ensure inputs and outputs are aligned
for _, row in details2.iterrows():
if pd.notnull(row["outputs.output"]):
assert int(row["inputs.number"]) == int(row["outputs.output"])
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_auto_resolve_requirements(self, pf: PFClient, randstr: Callable[[str], str]):
# will add requirements.txt to flow.dag.yaml if exists when submitting run.
with TemporaryDirectory() as temp:
temp = Path(temp)
shutil.copytree(f"{FLOWS_DIR}/flow_with_requirements_txt", temp / "flow_with_requirements_txt")
run = pf.run(
flow=temp / "flow_with_requirements_txt",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name"),
)
pf.runs.stream(run)
pf.runs.download(run=run.name, output=temp)
flow_dag = load_yaml(Path(temp, run.name, "snapshot/flow.dag.yaml"))
assert "requirements.txt" in flow_dag[ENVIRONMENT][PYTHON_REQUIREMENTS_TXT]
local_flow_dag = load_yaml(f"{FLOWS_DIR}/flow_with_requirements_txt/flow.dag.yaml")
assert "environment" not in local_flow_dag
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_requirements_in_additional_includes(self, pf: PFClient, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/flow_with_additional_include_req",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name"),
)
run = pf.runs.stream(run)
assert run._error is None
with TemporaryDirectory() as temp:
pf.runs.download(run=run.name, output=temp)
assert Path(temp, run.name, "snapshot/requirements").exists()
@pytest.mark.skipif(
condition=is_live(),
reason="removed requirement.txt to avoid compliance check.",
)
def test_eager_flow_crud(self, pf: PFClient, randstr: Callable[[str], str], simple_eager_run: Run):
run = simple_eager_run
run = pf.runs.get(run)
assert run.status == RunStatus.COMPLETED
details = pf.runs.get_details(run)
assert details.shape[0] == 1
metrics = pf.runs.get_metrics(run)
assert metrics == {}
# TODO(2917923): cannot differ the two requests to run history in replay mode."
# run_meta_data = RunHistoryKeys.RunMetaData
# hidden = RunHistoryKeys.HIDDEN
# run_id = run.name
# # test archive
# pf.runs.archive(run=run_id)
# run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
# assert run_data[hidden] is True
#
# # test restore
# pf.runs.restore(run=run_id)
# run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
# assert run_data[hidden] is False
@pytest.mark.skipif(
condition=is_live(),
reason="removed requirement.txt to avoid compliance check.",
)
def test_eager_flow_cancel(self, pf: PFClient, randstr: Callable[[str], str]):
"""Test cancel eager flow."""
# create a run
run_name = randstr("name")
pf.run(
flow=f"{EAGER_FLOWS_DIR}/long_running",
data=f"{DATAS_DIR}/simple_eager_flow_data.jsonl",
name=run_name,
)
pf.runs.cancel(run=run_name)
sleep(3)
run = pf.runs.get(run=run_name)
# the run status might still be cancel requested, but it should be canceled eventually
assert run.status in [RunStatus.CANCELED, RunStatus.CANCEL_REQUESTED]
@pytest.mark.skipif(
condition=is_live(),
reason="removed requirement.txt to avoid compliance check.",
)
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_eager_flow_download(self, pf: PFClient, simple_eager_run: Run):
run = simple_eager_run
expected_files = [
DownloadedRun.RUN_METADATA_FILE_NAME,
DownloadedRun.LOGS_FILE_NAME,
DownloadedRun.METRICS_FILE_NAME,
f"{DownloadedRun.SNAPSHOT_FOLDER}/flow.dag.yaml",
]
# test download
with TemporaryDirectory() as tmp_dir:
pf.runs.download(run=run.name, output=tmp_dir)
for file in expected_files:
assert Path(tmp_dir, run.name, file).exists()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/classificationAccuracy.csv | groundtruth,prediction
App,App
App,App
App,App
App,App
App,App
App,App
App,App
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_azure_cli_perf.py | import os
import sys
import timeit
from typing import Callable
from unittest import mock
import pytest
from promptflow._cli._user_agent import USER_AGENT as CLI_USER_AGENT # noqa: E402
from promptflow._sdk._telemetry import log_activity
from promptflow._sdk._utils import ClientUserAgentUtil
from sdk_cli_azure_test.recording_utilities import is_replay
FLOWS_DIR = "./tests/test_configs/flows"
DATAS_DIR = "./tests/test_configs/datas"
def mock_log_activity(*args, **kwargs):
custom_message = "github run: https://github.com/microsoft/promptflow/actions/runs/{0}".format(
os.environ.get("GITHUB_RUN_ID")
)
if len(args) == 4:
if args[3] is not None:
args[3]["custom_message"] = custom_message
else:
args = list(args)
args[3] = {"custom_message": custom_message}
elif "custom_dimensions" in kwargs and kwargs["custom_dimensions"] is not None:
kwargs["custom_dimensions"]["custom_message"] = custom_message
else:
kwargs["custom_dimensions"] = {"custom_message": custom_message}
return log_activity(*args, **kwargs)
def run_cli_command(cmd, time_limit=3600):
from promptflow._cli._pf_azure.entry import main
sys.argv = list(cmd)
st = timeit.default_timer()
with mock.patch.object(ClientUserAgentUtil, "get_user_agent") as get_user_agent_fun, mock.patch(
"promptflow._sdk._telemetry.activity.log_activity", side_effect=mock_log_activity
), mock.patch("promptflow._cli._pf_azure.entry.log_activity", side_effect=mock_log_activity):
# Client side will modify user agent only through ClientUserAgentUtil to avoid impact executor/runtime.
get_user_agent_fun.return_value = f"{CLI_USER_AGENT} perf_monitor/1.0"
user_agent = ClientUserAgentUtil.get_user_agent()
assert user_agent == f"{CLI_USER_AGENT} perf_monitor/1.0"
main()
ed = timeit.default_timer()
print(f"{cmd}, \nTotal time: {ed - st}s")
if is_replay():
assert ed - st < time_limit, f"The time limit is {time_limit}s, but it took {ed - st}s."
@pytest.fixture
def operation_scope_args(subscription_id: str, resource_group_name: str, workspace_name: str):
return [
"--subscription",
subscription_id,
"--resource-group",
resource_group_name,
"--workspace-name",
workspace_name,
]
@pytest.mark.perf_monitor_test
@pytest.mark.usefixtures(
"mock_get_azure_pf_client",
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestAzureCliPerf:
def test_pfazure_run_create(self, operation_scope_args, randstr: Callable[[str], str], time_limit=15):
name = randstr("name")
run_cli_command(
cmd=(
"pfazure",
"run",
"create",
"--flow",
f"{FLOWS_DIR}/print_input_flow",
"--data",
f"{DATAS_DIR}/print_input_flow.jsonl",
"--name",
name,
*operation_scope_args,
),
time_limit=time_limit,
)
def test_pfazure_run_update(self, operation_scope_args, time_limit=15):
run_cli_command(
cmd=(
"pfazure",
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
),
time_limit=time_limit,
)
def test_run_restore(self, operation_scope_args, time_limit=15):
run_cli_command(
cmd=(
"pfazure",
"run",
"restore",
"--name",
"test_run",
*operation_scope_args,
),
time_limit=time_limit,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py | import re
from pathlib import Path
import pydash
import pytest
from promptflow._utils.yaml_utils import dump_yaml, load_yaml_string
from promptflow.connections import AzureOpenAIConnection
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
def assert_dict_equals_with_skip_fields(item1, item2, skip_fields):
for fot_key in skip_fields:
pydash.set_(item1, fot_key, None)
pydash.set_(item2, fot_key, None)
assert item1 == item2
def normalize_arm_id(origin_value: str):
if origin_value:
m = re.match(
r"(.*)/subscriptions/[a-z0-9\-]+/resourceGroups/[a-z0-9\-]+/providers/"
r"Microsoft.MachineLearningServices/workspaces/[a-z0-9\-]+/([a-z]+)/[^/]+/versions/([a-z0-9\-]+)",
origin_value,
)
if m:
prefix, asset_type, _ = m.groups()
return (
f"{prefix}/subscriptions/xxx/resourceGroups/xxx/providers/"
f"Microsoft.MachineLearningServices/workspaces/xxx/{asset_type}/xxx/versions/xxx"
)
return None
def update_saved_spec(component, saved_spec_path: str):
yaml_text = component._to_yaml()
saved_spec_path = Path(saved_spec_path)
yaml_content = load_yaml_string(yaml_text)
if yaml_content.get("creation_context"):
for key in yaml_content.get("creation_context"):
yaml_content["creation_context"][key] = "xxx"
for key in ["task.code", "task.environment", "id"]:
target_value = normalize_arm_id(pydash.get(yaml_content, key))
if target_value:
pydash.set_(yaml_content, key, target_value)
yaml_text = dump_yaml(yaml_content)
if saved_spec_path.is_file():
current_spec_text = saved_spec_path.read_text()
if current_spec_text == yaml_text:
return
saved_spec_path.parent.mkdir(parents=True, exist_ok=True)
saved_spec_path.write_text(yaml_text)
@pytest.mark.skipif(
condition=not is_live(),
reason="flow in pipeline tests require secrets config file, only run in live mode.",
)
@pytest.mark.usefixtures("use_secrets_config_file")
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
class TestFlowInAzureML:
@pytest.mark.parametrize(
"load_params, expected_spec_attrs",
[
pytest.param(
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"environment_variables": {
"verbose": "true",
},
},
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"type": "parallel",
},
id="parallel",
),
],
)
def test_flow_as_component(
self,
azure_open_ai_connection: AzureOpenAIConnection,
temp_output_dir,
ml_client,
load_params: dict,
expected_spec_attrs: dict,
request,
) -> None:
# keep the simplest test here, more tests are in azure-ai-ml
from azure.ai.ml import load_component
flows_dir = "./tests/test_configs/flows"
flow_func: Component = load_component(
f"{flows_dir}/web_classification/flow.dag.yaml", params_override=[load_params]
)
# TODO: snapshot of flow component changed every time?
created_component = ml_client.components.create_or_update(flow_func, is_anonymous=True)
update_saved_spec(
created_component, f"./tests/test_configs/flows/saved_component_spec/{request.node.callspec.id}.yaml"
)
component_dict = created_component._to_dict()
slimmed_created_component_attrs = {key: pydash.get(component_dict, key) for key in expected_spec_attrs.keys()}
assert slimmed_created_component_attrs == expected_spec_attrs
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
from pathlib import Path
import pytest
from promptflow.azure._entities._flow import Flow
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestFlow:
def test_create_flow(self, created_flow: Flow):
# most of the assertions are in the fixture itself
assert isinstance(created_flow, Flow)
def test_get_flow(self, pf, created_flow: Flow):
result = pf.flows.get(name=created_flow.name)
# assert created flow is the same as the one retrieved
attributes = vars(result)
for attr in attributes:
assert getattr(result, attr) == getattr(created_flow, attr), f"Assertion failed for attribute: {attr!r}"
@pytest.mark.skipif(
condition=not is_live(),
reason="Complicated test combining `pf flow test` and global config",
)
def test_flow_test_with_config(self, remote_workspace_resource_id):
from promptflow import PFClient
client = PFClient(config={"connection.provider": remote_workspace_resource_id})
output = client.test(flow=flow_test_dir / "web_classification")
assert output.keys() == {"category", "evidence"}
@pytest.mark.usefixtures("mock_get_user_identity_info")
def test_list_flows(self, pf):
flows = pf.flows.list(max_results=3)
for flow in flows:
print(json.dumps(flow._to_dict(), indent=4))
assert len(flows) == 3
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_serve.py | import json
import pytest
from ..recording_utilities import is_live
testdata = """The event sourcing pattern involves using an append-only store to record the full series
of actions on that data. The Azure Cosmos DB change feed is a great choice as a central data store in
event sourcing architectures in which all data ingestion is modeled as writes (no updates or deletes).
In this case, each write to Azure Cosmos DB is an \"event,\" so there's a full record of past events
in the change feed. Typical uses of the events published by the central event store are to maintain materialized
views or to integrate with external systems. Because there's no time limit for retention in the change feed latest
version mode, you can replay all past events by reading from the beginning of your Azure Cosmos DB container's
change feed. You can even have multiple change feed consumers subscribe to the same container's change feed."""
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_remote_connection")
@pytest.mark.e2etest
def test_local_serving_api_with_remote_connection(flow_serving_client_remote_connection):
response = flow_serving_client_remote_connection.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_remote_connection.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_prt_config_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_prt_config_env(flow_serving_client_with_prt_config_env):
response = flow_serving_client_with_prt_config_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_prt_config_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
response = flow_serving_client_with_prt_config_env.get("/")
assert b"Welcome to promptflow app" in response.data
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_connection_provider_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_conn_provider_env(flow_serving_client_with_connection_provider_env):
response = flow_serving_client_with_connection_provider_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_connection_provider_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
response = flow_serving_client_with_connection_provider_env.get("/")
assert b"Welcome to promptflow app" in response.data
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_connection_provider_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_aml_resource_id_env(flow_serving_client_with_aml_resource_id_env):
response = flow_serving_client_with_aml_resource_id_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_aml_resource_id_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("serving_client_with_connection_name_override")
@pytest.mark.e2etest
def test_azureml_serving_api_with_connection_name_override(serving_client_with_connection_name_override):
response = serving_client_with_connection_name_override.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = serving_client_with_connection_name_override.post("/score", data=json.dumps({"text": testdata}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "api_base" not in json.loads(response.data.decode()).values()
@pytest.mark.usefixtures("serving_client_with_connection_data_override")
@pytest.mark.e2etest
def test_azureml_serving_api_with_connection_data_override(serving_client_with_connection_data_override):
response = serving_client_with_connection_data_override.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = serving_client_with_connection_data_override.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "api_base" in json.loads(response.data.decode()).values()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pydash
import pytest
from promptflow._sdk.entities._connection import _Connection
from promptflow.connections import AzureOpenAIConnection, CustomConnection
from promptflow.contracts.types import Secret
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@pytest.fixture
def connection_ops(pf):
return pf._connections
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures("vcr_recording")
class TestConnectionOperations:
@pytest.mark.skip(reason="Skip to avoid flooded connections in workspace.")
def test_connection_get_create_delete(self, connection_ops):
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
connection = _Connection(
name="test_connection_1",
type="AzureOpenAI",
configs=AzureOpenAIConnection(
api_key=Secret("test_key"),
api_base="test_base",
api_type="azure",
api_version="2023-07-01-preview",
),
)
try:
result = connection_ops.get(name=connection.name)
except FlowRequestException:
result = connection_ops.create_or_update(connection)
config_dict = pydash.omit(result._to_dict(), "configs.api_key")
assert config_dict == {
"name": "test_connection_1",
"connection_type": "AzureOpenAI",
"connection_scope": "User",
"configs": {"api_base": "test_base", "api_type": "azure", "api_version": "2023-07-01-preview"},
}
# soft delete
connection_ops.delete(name=connection.name)
@pytest.mark.skip(reason="Skip to avoid flooded connections in workspace.")
def test_custom_connection_create(self, connection_ops):
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
connection = _Connection(
name="test_connection_2", type="Custom", custom_configs=CustomConnection(a="1", b=Secret("2"))
)
try:
result = connection_ops.get(name=connection.name)
except FlowRequestException:
result = connection_ops.create_or_update(connection)
config_dict = pydash.omit(result._to_dict(), "custom_configs")
assert config_dict == {"connection_scope": "User", "connection_type": "Custom", "name": "test_connection_2"}
# soft delete
connection_ops.delete(name=connection.name)
def test_list_connection_spec(self, connection_ops):
result = {v.connection_type: v._to_dict() for v in connection_ops.list_connection_specs()}
# Assert custom keys type
assert "Custom" in result
assert result["Custom"] == {
"module": "promptflow.connections",
"connection_type": "Custom",
"flow_value_type": "CustomConnection",
"config_specs": [],
}
# assert strong type
assert "AzureOpenAI" in result
aoai_config_specs = result["AzureOpenAI"]["config_specs"]
for config_dict in aoai_config_specs:
if config_dict["name"] == "api_version":
del config_dict["default_value"]
expected_config_specs = [
{"name": "api_key", "display_name": "API key", "config_value_type": "Secret", "is_optional": False},
{"name": "api_base", "display_name": "API base", "config_value_type": "String", "is_optional": False},
{
"name": "api_type",
"display_name": "API type",
"config_value_type": "String",
"default_value": "azure",
"is_optional": False,
},
{
"name": "api_version",
"display_name": "API version",
"config_value_type": "String",
"is_optional": False,
},
]
for spec in expected_config_specs:
assert spec in result["AzureOpenAI"]["config_specs"]
def test_get_connection(self, connection_ops):
# Note: No secrets will be returned by MT api
result = connection_ops.get(name="azure_open_ai_connection")
assert (
result._to_dict().items()
> {
"api_type": "azure",
"module": "promptflow.connections",
"name": "azure_open_ai_connection",
}.items()
)
result = connection_ops.get(name="custom_connection")
assert (
result._to_dict().items()
> {
"name": "custom_connection",
"module": "promptflow.connections",
"configs": {},
"secrets": {},
}.items()
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_cli_with_azure.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import os
import sys
import uuid
from typing import Callable
import pytest
from mock.mock import patch
from promptflow._constants import PF_USER_AGENT
from promptflow._core.operation_context import OperationContext
from promptflow._sdk._utils import ClientUserAgentUtil
from promptflow._sdk.entities import Run
from promptflow._utils.utils import environment_variable_overwrite, parse_ua_to_dict
from promptflow.azure import PFClient
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
FLOWS_DIR = "./tests/test_configs/flows"
DATAS_DIR = "./tests/test_configs/datas"
RUNS_DIR = "./tests/test_configs/runs"
# TODO: move this to a shared utility module
def run_pf_command(*args, pf, runtime=None, cwd=None):
from promptflow._cli._pf_azure.entry import main
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = (
["pfazure"]
+ list(args)
+ [
"--subscription",
pf._ml_client.subscription_id,
"--resource-group",
pf._ml_client.resource_group_name,
"--workspace-name",
pf._ml_client.workspace_name,
]
)
if runtime:
sys.argv += ["--runtime", runtime]
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_get_azure_pf_client",
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestCliWithAzure:
def test_basic_flow_run_bulk_without_env(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
run_pf_command(
"run",
"create",
"--flow",
f"{FLOWS_DIR}/web_classification",
"--data",
f"{DATAS_DIR}/webClassification3.jsonl",
"--name",
name,
pf=pf,
runtime=runtime,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
@pytest.mark.skip("Custom tool pkg and promptprompt pkg with CustomStrongTypeConnection not installed on runtime.")
def test_basic_flow_with_package_tool_with_custom_strong_type_connection(self, pf, runtime) -> None:
name = str(uuid.uuid4())
run_pf_command(
"run",
"create",
"--flow",
f"{FLOWS_DIR}/flow_with_package_tool_with_custom_strong_type_connection",
"--data",
f"{FLOWS_DIR}/flow_with_package_tool_with_custom_strong_type_connection/data.jsonl",
"--name",
name,
pf=pf,
runtime=runtime,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
def test_run_with_remote_data(
self, pf, runtime: str, remote_web_classification_data, randstr: Callable[[str], str]
) -> None:
# run with arm id
name = randstr("name1")
run_pf_command(
"run",
"create",
"--flow",
"web_classification",
"--data",
f"azureml:{remote_web_classification_data.id}",
"--name",
name,
pf=pf,
runtime=runtime,
cwd=f"{FLOWS_DIR}",
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
# run with name version
name = randstr("name2")
run_pf_command(
"run",
"create",
"--flow",
"web_classification",
"--data",
f"azureml:{remote_web_classification_data.name}:{remote_web_classification_data.version}",
"--name",
name,
pf=pf,
runtime=runtime,
cwd=f"{FLOWS_DIR}",
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
def test_run_file_with_set(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
run_pf_command(
"run",
"create",
"--file",
f"{RUNS_DIR}/run_with_env.yaml",
"--set",
f"runtime={runtime}",
"--name",
name,
pf=pf,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
assert run.properties["azureml.promptflow.runtime_name"] == runtime
@pytest.mark.skipif(condition=not is_live(), reason="This test requires an actual PFClient")
def test_azure_cli_ua(self, pf: PFClient):
# clear user agent before test
context = OperationContext().get_instance()
context.user_agent = ""
with environment_variable_overwrite(PF_USER_AGENT, ""):
with pytest.raises(SystemExit):
run_pf_command(
"run",
"show",
"--name",
"not_exist",
pf=pf,
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk", "promptflow-cli"}
def test_cli_telemetry(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
@contextlib.contextmanager
def check_workspace_info(*args, **kwargs):
if "custom_dimensions" in kwargs:
assert kwargs["custom_dimensions"]["workspace_name"] == pf._ml_client.workspace_name
assert kwargs["custom_dimensions"]["resource_group_name"] == pf._ml_client.resource_group_name
assert kwargs["custom_dimensions"]["subscription_id"] == pf._ml_client.subscription_id
yield None
with patch("promptflow._sdk._telemetry.activity.log_activity") as mock_log_activity:
mock_log_activity.side_effect = check_workspace_info
run_pf_command(
"run",
"create",
"--file",
f"{RUNS_DIR}/run_with_env.yaml",
"--set",
f"runtime={runtime}",
"--name",
name,
pf=pf,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_telemetry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import os
import shutil
import sys
import tempfile
import uuid
from logging import Logger
from pathlib import Path
from typing import Callable
from unittest.mock import MagicMock, patch
import pydash
import pytest
from promptflow import load_run
from promptflow._constants import PF_USER_AGENT
from promptflow._core.operation_context import OperationContext
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._telemetry import (
ActivityType,
PromptFlowSDKLogHandler,
get_appinsights_log_handler,
get_telemetry_logger,
is_telemetry_enabled,
log_activity,
)
from promptflow._sdk._utils import ClientUserAgentUtil, call_from_extension
from promptflow._utils.utils import environment_variable_overwrite, parse_ua_to_dict
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@contextlib.contextmanager
def cli_consent_config_overwrite(val):
config = Configuration.get_instance()
original_consent = config.get_telemetry_consent()
config.set_telemetry_consent(val)
try:
yield
finally:
if original_consent:
config.set_telemetry_consent(original_consent)
else:
config.set_telemetry_consent(True)
@contextlib.contextmanager
def extension_consent_config_overwrite(val):
config = Configuration.get_instance()
original_consent = config.get_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY)
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=val)
try:
yield
finally:
if original_consent:
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=original_consent)
else:
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=True)
RUNS_DIR = "./tests/test_configs/runs"
FLOWS_DIR = "./tests/test_configs/flows"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.usefixtures("mock_set_headers_with_user_aml_token", "single_worker_thread_pool", "vcr_recording")
@pytest.mark.e2etest
class TestTelemetry:
def test_logging_handler(self):
# override environment variable
with cli_consent_config_overwrite(True):
handler = get_appinsights_log_handler()
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
with cli_consent_config_overwrite(False):
handler = get_appinsights_log_handler()
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
def test_call_from_extension(self):
from promptflow._core.operation_context import OperationContext
assert call_from_extension() is False
with environment_variable_overwrite(PF_USER_AGENT, "prompt-flow-extension/1.0.0"):
assert call_from_extension() is True
# remove extension ua in context
context = OperationContext().get_instance()
context.user_agent = context.user_agent.replace("prompt-flow-extension/1.0.0", "")
def test_custom_event(self, pf):
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def log_event(*args, **kwargs):
record = args[0]
assert record.custom_dimensions is not None
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
envelope = handler.log_record_to_envelope(record)
custom_dimensions = pydash.get(envelope, "data.baseData.properties")
assert isinstance(custom_dimensions, dict)
# Note: need privacy review if we add new fields.
if "start" in record.message:
assert custom_dimensions.keys() == {
"request_id",
"activity_name",
"activity_type",
"subscription_id",
"resource_group_name",
"workspace_name",
"level",
"python_version",
"user_agent",
"installation_id",
"first_call",
"from_ci",
}
elif "complete" in record.message:
assert custom_dimensions.keys() == {
"request_id",
"activity_name",
"activity_type",
"subscription_id",
"resource_group_name",
"workspace_name",
"completion_status",
"duration_ms",
"level",
"python_version",
"user_agent",
"installation_id",
"first_call",
"from_ci",
}
else:
raise ValueError("Invalid message: {}".format(record.message))
assert record.message.startswith("pfazure.runs.get")
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = log_event
# mock_error_logger.side_effect = log_event
try:
pf.runs.get("not_exist")
except RunNotFoundError:
pass
def test_default_logging_behavior(self):
assert is_telemetry_enabled() is True
# default enable telemetry
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
def test_close_logging_handler(self):
with cli_consent_config_overwrite(False):
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
with extension_consent_config_overwrite(False):
with environment_variable_overwrite(PF_USER_AGENT, "prompt-flow-extension/1.0.0"):
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
# default enable telemetry
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
def test_cached_logging_handler(self):
# should get same logger & handler instance if called multiple times
logger = get_telemetry_logger()
handler = next((h for h in logger.handlers if isinstance(h, PromptFlowSDKLogHandler)), None)
another_logger = get_telemetry_logger()
another_handler = next((h for h in another_logger.handlers if isinstance(h, PromptFlowSDKLogHandler)), None)
assert logger is another_logger
assert handler is another_handler
def test_sdk_telemetry_ua(self, pf):
from promptflow import PFClient
from promptflow.azure import PFClient as PFAzureClient
# log activity will pick correct ua
def assert_ua(*args, **kwargs):
ua = pydash.get(kwargs, "extra.custom_dimensions.user_agent", None)
ua_dict = parse_ua_to_dict(ua)
assert ua_dict.keys() == {"promptflow-sdk"}
logger = MagicMock()
logger.info = MagicMock()
logger.info.side_effect = assert_ua
# clear user agent before test
context = OperationContext().get_instance()
context.user_agent = ""
# get telemetry logger from SDK should not have extension ua
# start a clean local SDK client
with environment_variable_overwrite(PF_USER_AGENT, ""):
PFClient()
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk"}
# Call log_activity
with log_activity(logger, "test_activity", activity_type=ActivityType.PUBLICAPI):
# Perform some activity
pass
# start a clean Azure SDK client
with environment_variable_overwrite(PF_USER_AGENT, ""):
PFAzureClient(
ml_client=pf._ml_client,
subscription_id=pf._ml_client.subscription_id,
resource_group_name=pf._ml_client.resource_group_name,
workspace_name=pf._ml_client.workspace_name,
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk"}
# Call log_activity
with log_activity(logger, "test_activity", activity_type=ActivityType.PUBLICAPI):
# Perform some activity
pass
PFAzureClient(
ml_client=pf._ml_client,
subscription_id=pf._ml_client.subscription_id,
resource_group_name=pf._ml_client.resource_group_name,
workspace_name=pf._ml_client.workspace_name,
user_agent="a/1.0.0",
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk", "a"}
context = OperationContext().get_instance()
context.user_agent = ""
def test_inner_function_call(self, pf, runtime: str, randstr: Callable[[str], str]):
request_ids = set()
first_sdk_calls = []
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
first_sdk_call = pydash.get(kwargs, "extra.custom_dimensions.first_call")
request_ids.add(request_id)
first_sdk_calls.append(first_sdk_call)
with patch.object(Logger, "info") as mock_logger:
mock_logger.side_effect = check_inner_call
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
pf.runs.create_or_update(run=run)
# only 1 request id
assert len(request_ids) == 1
# only 1 and last call is public call
assert first_sdk_calls[0] is True
assert first_sdk_calls[-1] is True
assert set(first_sdk_calls[1:-1]) == {False}
def test_different_request_id(self):
from promptflow import PFClient
pf = PFClient()
request_ids = set()
first_sdk_calls = []
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
first_sdk_call = pydash.get(kwargs, "extra.custom_dimensions.first_call")
request_ids.add(request_id)
first_sdk_calls.append(first_sdk_call)
with patch.object(Logger, "info") as mock_logger:
mock_logger.side_effect = check_inner_call
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
)
# create 2 times will get 2 request ids
run.name = str(uuid.uuid4())
pf.runs.create_or_update(run=run)
run.name = str(uuid.uuid4())
pf.runs.create_or_update(run=run)
# only 1 request id
assert len(request_ids) == 2
# 1 and last call is public call
assert first_sdk_calls[0] is True
assert first_sdk_calls[-1] is True
def test_scrub_fields(self):
from promptflow import PFClient
pf = PFClient()
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def log_event(*args, **kwargs):
record = args[0]
assert record.custom_dimensions is not None
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
envelope = handler.log_record_to_envelope(record)
# device name removed
assert "ai.cloud.roleInstance" not in envelope.tags
assert "ai.device.id" not in envelope.tags
# role name should be scrubbed or kept in whitelist
assert envelope.tags["ai.cloud.role"] in [os.path.basename(sys.argv[0]), "***"]
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = log_event
# mock_error_logger.side_effect = log_event
try:
pf.runs.get("not_exist")
except RunNotFoundError:
pass
def test_different_event_for_node_run(self):
from promptflow import PFClient
pf = PFClient()
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def assert_node_run(*args, **kwargs):
record = args[0]
assert record.msg.startswith("pf.flows.node_test")
assert record.custom_dimensions["activity_name"] == "pf.flows.node_test"
def assert_flow_test(*args, **kwargs):
record = args[0]
assert record.msg.startswith("pf.flows.test")
assert record.custom_dimensions["activity_name"] == "pf.flows.test"
with tempfile.TemporaryDirectory() as temp_dir:
shutil.copytree((Path(FLOWS_DIR) / "print_env_var").resolve().as_posix(), temp_dir, dirs_exist_ok=True)
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = assert_node_run
pf.flows.test(temp_dir, node="print_env", inputs={"key": "API_BASE"})
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = assert_flow_test
pf.flows.test(temp_dir, inputs={"key": "API_BASE"})
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_arm_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pytest
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@pytest.fixture
def connection_ops(pf):
return pf._arm_connections
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures("vcr_recording")
class TestArmConnectionOperations:
def test_get_connection(self, connection_ops):
# Note: Secrets will be returned by arm api
result = connection_ops.get(name="azure_open_ai_connection")
assert (
result._to_dict().items()
> {
"api_type": "azure",
"module": "promptflow.connections",
"name": "azure_open_ai_connection",
}.items()
)
result = connection_ops.get(name="custom_connection")
assert (
result._to_dict().items()
> {
"name": "custom_connection",
"module": "promptflow.connections",
"configs": {},
}.items()
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_cli.py | import os
import os.path
import sys
from pathlib import Path
import pytest
from promptflow._cli._pf.entry import main
from ..recording_utilities import is_live
FLOWS_DIR = "./tests/test_configs/flows"
RUNS_DIR = "./tests/test_configs/runs"
CONNECTIONS_DIR = "./tests/test_configs/connections"
DATAS_DIR = "./tests/test_configs/datas"
# TODO: move this to a shared utility module
def run_pf_command(*args, cwd=None):
"""Run a pf command with the given arguments and working directory.
There have been some unknown issues in using subprocess on CI, so we use this function instead, which will also
provide better debugging experience.
"""
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = ["pf"] + list(args)
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.mark.skipif(condition=not is_live(), reason="CLI tests, only run in live mode.")
@pytest.mark.cli_test
@pytest.mark.e2etest
class TestCli:
# PF cli test is here because when designate connection provider to remote, we need azure dependencies.
def test_pf_flow_test(self, remote_workspace_resource_id):
# Test with connection provider
run_pf_command(
"flow",
"test",
"--flow",
f"{FLOWS_DIR}/web_classification",
"--config",
f"connection.provider={remote_workspace_resource_id}",
)
output_path = Path(FLOWS_DIR) / "web_classification" / ".promptflow" / "flow.output.json"
assert output_path.exists()
def test_flow_chat(self, monkeypatch, capsys, remote_workspace_resource_id):
chat_list = ["hi", "what is chat gpt?"]
def mock_input(*args, **kwargs):
if chat_list:
return chat_list.pop()
else:
raise KeyboardInterrupt()
monkeypatch.setattr("builtins.input", mock_input)
run_pf_command(
"flow",
"test",
"--flow",
f"{FLOWS_DIR}/chat_flow",
"--interactive",
"--verbose",
"--config",
f"connection.provider={remote_workspace_resource_id}",
)
output_path = Path(FLOWS_DIR) / "chat_flow" / ".promptflow" / "chat.output.json"
assert output_path.exists()
detail_path = Path(FLOWS_DIR) / "chat_flow" / ".promptflow" / "chat.detail.json"
assert detail_path.exists()
outerr = capsys.readouterr()
# Check node output
assert "chat_node:" in outerr.out
assert "show_answer:" in outerr.out
assert "[show_answer]: print:" in outerr.out
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_entity.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
import tempfile
import uuid
from pathlib import Path
import pytest
from mock.mock import Mock
from promptflow._sdk._load_functions import load_run
from promptflow._sdk._vendor import get_upload_files_from_folder
from promptflow._utils.flow_utils import load_flow_dag
from promptflow.azure._constants._flow import ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from promptflow.azure._entities._flow import Flow
tests_root_dir = Path(__file__).parent.parent.parent
FLOWS_DIR = (tests_root_dir / "test_configs/flows").resolve()
RUNS_DIR = (tests_root_dir / "test_configs/runs").resolve()
def load_flow(source):
from promptflow.azure._load_functions import load_flow
return load_flow(source=source)
@pytest.mark.unittest
class TestFlow:
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow(self):
local_file = tests_root_dir / "test_configs/flows/meta_files/flow.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "web_classificiation_flow_3",
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"display_name": "Web Classification",
"type": "default",
"path": "./flow.dag.yaml",
}
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"flow_name": "Web Classification",
"flow_run_settings": {},
"flow_type": "default",
"is_archived": True,
"flow_definition_file_path": "./flow.dag.yaml",
}
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow_from_remote_storage(self):
from promptflow.azure.operations._flow_operations import FlowOperations
local_file = tests_root_dir / "test_configs/flows/meta_files/remote_fs.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "classification_accuracy_eval",
"path": "azureml://datastores/workspaceworkingdirectory/paths/Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"type": "evaluation",
}
FlowOperations._try_resolve_code_for_flow(flow, Mock())
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"flow_definition_file_path": "Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"flow_run_settings": {},
"flow_type": "evaluation",
"is_archived": True,
}
def test_ignore_files_in_flow(self):
local_file = tests_root_dir / "test_configs/flows/web_classification"
with tempfile.TemporaryDirectory() as temp:
flow_path = Path(temp) / "flow"
shutil.copytree(local_file, flow_path)
assert (Path(temp) / "flow/.promptflow/flow.tools.json").exists()
(Path(flow_path) / ".runs").mkdir(parents=True)
(Path(flow_path) / ".runs" / "mock.file").touch()
flow = load_flow(source=flow_path)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
# assert that .runs/mock.file are ignored
assert ".runs/mock.file" not in flow_files
# Web classification may be executed and include flow.detail.json, flow.logs, flow.outputs.json
assert all(
file in flow_files
for file in [
".promptflow/flow.tools.json",
"classify_with_llm.jinja2",
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"fetch_text_content_from_url_input.jsonl",
"flow.dag.yaml",
"prepare_examples.py",
"samples.json",
"summarize_text_content.jinja2",
"summarize_text_content__variant_1.jinja2",
"webClassification20.csv",
]
)
def test_load_yaml_run_with_resources(self):
source = f"{RUNS_DIR}/sample_bulk_run_with_resources.yaml"
run = load_run(source=source, params_override=[{"name": str(uuid.uuid4())}])
assert run._resources["instance_type"] == "Standard_D2"
assert run._resources["idle_time_before_shutdown_minutes"] == 60
def test_flow_with_additional_includes(self):
flow_folder = FLOWS_DIR / "web_classification_with_additional_include"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
_, temp_flow = load_flow_dag(code.path)
assert "additional_includes" not in temp_flow
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
target_additional_includes = [
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"summarize_text_content.jinja2",
"external_files/convert_to_dict.py",
"external_files/fetch_text_content_from_url.py",
"external_files/summarize_text_content.jinja2",
]
# assert all additional includes are included
for file in target_additional_includes:
assert file in flow_files
def test_flow_with_ignore_file(self):
flow_folder = FLOWS_DIR / "flow_with_ignore_file"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
assert len(flow_files) > 0
target_ignored_files = ["ignored_folder/1.txt", "random.ignored"]
# assert all ignored files are ignored
for file in target_ignored_files:
assert file not in flow_files
def test_resolve_requirements(self):
flow_dag = {}
# Test when requirements.txt does not exist
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_ignore_file", flow_dag=flow_dag)
# Test when requirements.txt exists but already added to flow_dag
flow_dag[ENVIRONMENT] = {PYTHON_REQUIREMENTS_TXT: "another_requirements.txt"}
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
# Test when requirements.txt exists and not added to flow_dag
flow_dag = {}
assert Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
def test_resolve_requirements_for_flow(self):
with tempfile.TemporaryDirectory() as temp:
temp = Path(temp)
# flow without environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt")
flow_folder = temp / "flow_with_requirements_txt"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"python_requirements_txt": "requirements.txt"}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert ENVIRONMENT not in flow_dag
# flow with environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt_and_env"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt_and_env")
flow_folder = temp / "flow_with_requirements_txt_and_env"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {
"image": "python:3.8-slim",
"python_requirements_txt": "requirements.txt",
}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"image": "python:3.8-slim"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_run_operations.py | import tempfile
import pytest
from pytest_mock import MockerFixture
from promptflow.azure import PFClient
from promptflow.exceptions import UserErrorException
@pytest.mark.unittest
class TestRunOperations:
def test_download_run_with_invalid_workspace_datastore(self, pf: PFClient, mocker: MockerFixture):
# test download with invalid workspace datastore
mocker.patch.object(pf.runs, "_validate_for_run_download")
mocker.patch.object(pf.runs, "_workspace_default_datastore", "test")
with tempfile.TemporaryDirectory() as tmp_dir:
with pytest.raises(UserErrorException, match="workspace default datastore is not supported"):
pf.runs.download(run="fake_run_name", output=tmp_dir)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_pf_client_azure.py | import pytest
from promptflow._sdk._errors import RunOperationParameterError
@pytest.mark.unittest
class TestPFClientAzure:
def test_wrong_client_parameters(self):
from promptflow.azure import PFClient
# test wrong client parameters
with pytest.raises(RunOperationParameterError, match="You have passed in the wrong parameter name"):
PFClient(
subscription_id="fake_subscription_id",
resource_group="fake_resource_group",
workspace_name="fake_workspace_name",
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
from unittest.mock import patch
import pytest
from promptflow._sdk._errors import FlowOperationError
from promptflow.exceptions import UserErrorException
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
@pytest.mark.unittest
class TestFlowOperations:
def test_create_flow_with_invalid_parameters(self, pf):
with pytest.raises(UserErrorException, match=r"Flow source must be a directory with"):
pf.flows.create_or_update(flow="fake_source")
flow_source = flow_test_dir / "web_classification/"
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, display_name=False)
with pytest.raises(UserErrorException, match="Must be one of: standard, evaluation, chat"):
pf.flows.create_or_update(flow=flow_source, type="unknown")
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, description=False)
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, tags={"key": False})
@pytest.mark.usefixtures("enable_logger_propagate")
def test_create_flow_with_warnings(self, pf, caplog):
flow_source = flow_test_dir / "web_classification/"
pf.flows._validate_flow_creation_parameters(source=flow_source, random="random")
assert "random: Unknown field" in caplog.text
def test_list_flows_invalid_cases(self, pf):
with pytest.raises(FlowOperationError, match="'max_results' must be a positive integer"):
pf.flows.list(max_results=0)
with pytest.raises(FlowOperationError, match="'flow_type' must be one of"):
pf.flows.list(flow_type="unknown")
with pytest.raises(FlowOperationError, match="Invalid list view type"):
pf.flows.list(list_view_type="invalid")
def test_get_user_identity_info(self):
# we have a fixture "mock_get_user_identity_info" to mock this function during record and replay
# as we don't want to deal with token in these modes; meanwhile, considering coverage, add this
# unit test to try to cover this code path.
import jwt
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
mock_oid, mock_tid = "mock_oid", "mock_tid"
def mock_init(*args, **kwargs) -> str:
self = args[0]
self._credential = None
def mock_get_arm_token(*args, **kwargs) -> str:
return jwt.encode(
payload={
"oid": mock_oid,
"tid": mock_tid,
},
key="",
)
with patch(
"promptflow.azure._restclient.flow_service_caller.get_arm_token",
new=mock_get_arm_token,
):
with patch.object(FlowServiceCaller, "__init__", new=mock_init):
service_caller = FlowServiceCaller(workspace=None, credential=None, operation_scope=None)
user_object_id, user_tenant_id = service_caller._get_user_identity_info()
assert user_object_id == mock_oid
assert user_tenant_id == mock_tid
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_arm_connection_build.py | import copy
import pytest
def build_from_data_and_assert(data, expected):
from azure.ai.ml._restclient.v2023_06_01_preview.models import WorkspaceConnectionPropertiesV2BasicResource
from promptflow.azure.operations._arm_connection_operations import ArmConnectionOperations
data = copy.deepcopy(data)
obj = WorkspaceConnectionPropertiesV2BasicResource.deserialize(data)
assert ArmConnectionOperations.build_connection_dict_from_rest_object("mock", obj) == expected
@pytest.mark.unittest
def test_build_azure_openai_connection_from_rest_object():
# Test on ApiKey type with AzureOpenAI category
data = {
"id": "mock_id",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "AzureOpenAI",
"target": "<api-base>",
"metadata": {
"azureml.flow.connection_type": "AzureOpenAI",
"azureml.flow.module": "promptflow.connections",
"apiType": "azure",
"ApiVersion": "2023-07-01-preview",
"ResourceId": "mock_id",
},
},
}
expected = {
"type": "AzureOpenAIConnection",
"module": "promptflow.connections",
"value": {
"api_base": "<api-base>",
"api_key": "***",
"api_type": "azure",
"api_version": "2023-07-01-preview",
"resource_id": "mock_id",
},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_default_azure_openai_connection_missing_metadata():
# Test on ApiKey type with AzureOpenAI category
data = {
"id": "mock_id",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "AzureOpenAI",
"target": "<api-base>",
"metadata": {
# Missing ApiType and ApiVersion
# "ApiType": "azure",
# "ApiVersion": "2023-07-01-preview",
},
},
}
expected = {
"type": "AzureOpenAIConnection",
"module": "promptflow.connections",
"value": {
"api_base": "<api-base>",
"api_key": "***",
# Assert below keys are filtered out
# "api_type": None,
# "api_version": None,
},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_custom_keys_connection_from_rest_object():
# Test on CustomKeys type with CustomConnection category
data = {
"id": "mock_id",
"name": "custom_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "CustomKeys",
"credentials": {"keys": {"my_key1": "***", "my_key2": "***"}},
"category": "CustomKeys",
"target": "<api-base>",
"metadata": {
"azureml.flow.connection_type": "Custom",
"azureml.flow.module": "promptflow.connections",
"general_key": "general_value",
},
},
}
expected = {
"type": "CustomConnection",
"module": "promptflow.connections",
"value": {"my_key1": "***", "my_key2": "***", "general_key": "general_value"},
"secret_keys": ["my_key1", "my_key2"],
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_cognitive_search_connection_from_rest_object():
# Test on ApiKey type with CognitiveSearch category
data = {
"tags": None,
"location": None,
"id": "mock_id",
"name": "test",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveSearch",
"expiryTime": None,
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "CognitiveSearch",
"azureml.flow.module": "promptflow.connections",
"ApiVersion": "2023-07-01-Preview",
},
},
}
expected = {
"type": "CognitiveSearchConnection",
"module": "promptflow.connections",
"value": {"api_key": "***", "api_base": "mock_target", "api_version": "2023-07-01-Preview"},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_cognitive_service_category_connection_from_rest_object():
# Test on Api type with CognitiveService category
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveService",
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "AzureContentSafety",
"azureml.flow.module": "promptflow.connections",
"Kind": "Content Safety",
"ApiVersion": "2023-04-30-preview",
},
},
}
expected = {
"type": "AzureContentSafetyConnection",
"module": "promptflow.connections",
"value": {"api_key": "***", "endpoint": "mock_target", "api_version": "2023-04-30-preview"},
}
build_from_data_and_assert(data, expected)
# Test category + kind as connection type
del data["properties"]["metadata"]["azureml.flow.connection_type"]
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_connection_missing_metadata():
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveService",
"target": "mock_target",
"metadata": {
"ApiVersion": "2023-04-30-preview",
},
},
}
with pytest.raises(Exception) as e:
build_from_data_and_assert(data, {})
assert "is not recognized in PromptFlow" in str(e.value)
@pytest.mark.unittest
def test_build_connection_unknown_category():
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "Unknown",
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "AzureContentSafety",
"azureml.flow.module": "promptflow.connections",
"Kind": "Content Safety",
"ApiVersion": "2023-04-30-preview",
},
},
}
with pytest.raises(Exception) as e:
build_from_data_and_assert(data, {})
assert "Unknown connection mock category Unknown" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_pf_client.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import mock
import pytest
from promptflow import PFClient
from promptflow._sdk.operations._connection_operations import ConnectionOperations
from promptflow._sdk.operations._local_azure_connection_operations import LocalAzureConnectionOperations
from promptflow.exceptions import UserErrorException
from ..recording_utilities import is_live
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
@pytest.mark.sdk_test
@pytest.mark.e2etest
class TestPFClient:
# Test pf client when connection provider is azureml.
# This tests suites need azure dependencies.
@pytest.mark.skipif(condition=not is_live(), reason="This test requires an actual PFClient")
def test_connection_provider(self, subscription_id: str, resource_group_name: str, workspace_name: str):
target = "promptflow._sdk._pf_client.Configuration"
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "abc"
with pytest.raises(UserErrorException) as e:
client = PFClient()
assert client.connections
assert "Unsupported connection provider" in str(e.value)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "azureml:xx"
with pytest.raises(ValueError) as e:
client = PFClient()
assert client.connections
assert "Malformed connection provider string" in str(e.value)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "local"
client = PFClient()
assert isinstance(client.connections, ConnectionOperations)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "azureml:" + RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
client = PFClient()
assert isinstance(client.connections, LocalAzureConnectionOperations)
client = PFClient(
config={
"connection.provider": "azureml:"
+ RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
}
)
assert isinstance(client.connections, LocalAzureConnectionOperations)
def test_local_azure_connection_extract_workspace(self):
res = LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourceGroups/456/providers/Microsoft.MachineLearningServices/workspaces/789"
)
assert res == ("123", "456", "789")
res = LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourcegroups/456/workspaces/789"
)
assert res == ("123", "456", "789")
with pytest.raises(ValueError) as e:
LocalAzureConnectionOperations._extract_workspace("azureml:xx")
assert "Malformed connection provider string" in str(e.value)
with pytest.raises(ValueError) as e:
LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourceGroups/456/providers/Microsoft.MachineLearningServices/workspaces/"
)
assert "Malformed connection provider string" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_run_entity.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock
import pytest
from promptflow._sdk.entities import Run
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow.exceptions import UserErrorException
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
FLOWS_DIR = "./tests/test_configs/flows"
RUNS_DIR = "./tests/test_configs/runs"
DATAS_DIR = "./tests/test_configs/datas"
@pytest.mark.unittest
class TestRun:
def test_input_mapping_types(self, pf):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
flow_path = Path(f"{FLOWS_DIR}/flow_with_dict_input")
# run with dict inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key={"a": 1}),
)
rest_run = run._to_rest_object()
assert rest_run.inputs_mapping == {"key": '{"a": 1}'}
# run with list inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key=["a", "b"]),
)
rest_run = run._to_rest_object()
assert rest_run.inputs_mapping == {"key": '["a", "b"]'}
# unsupported inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key=Mock()),
)
with pytest.raises(UserErrorException):
run._to_rest_object()
run = Run(flow=flow_path, data=data_path, column_mapping="str")
with pytest.raises(UserErrorException):
run._to_rest_object()
def test_flow_id(self):
# same flow id for same flow in same GIT repo
flow_path = Path(f"{FLOWS_DIR}/flow_with_dict_input")
# run with dict inputs
session_id1 = get_flow_lineage_id(flow_path)
session_id2 = get_flow_lineage_id(flow_path)
assert session_id1 == session_id2
# same flow id for same flow in same device
with TemporaryDirectory() as tmp_dir:
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow_with_dict_input")
session_id3 = get_flow_lineage_id(f"{tmp_dir}/flow_with_dict_input")
session_id4 = get_flow_lineage_id(f"{tmp_dir}/flow_with_dict_input")
assert session_id3 == session_id4
assert session_id3 != session_id1
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_azure_cli_activity_name.py | import pytest
from promptflow._cli._pf_azure.entry import get_parser_args
from promptflow._cli._utils import _get_cli_activity_name
def get_cli_activity_name(cmd):
prog, args = get_parser_args(list(cmd)[1:])
return _get_cli_activity_name(cli=prog, args=args)
@pytest.mark.unittest
class TestAzureCliTimeConsume:
def test_pfazure_run_create(self, activity_name="pfazure.run.create"):
assert (
get_cli_activity_name(
cmd=("pfazure", "run", "create", "--flow", "print_input_flow", "--data", "print_input_flow.jsonl")
)
== activity_name
)
def test_pfazure_run_update(self, activity_name="pfazure.run.update"):
assert (
get_cli_activity_name(
cmd=(
"pfazure",
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
)
)
== activity_name
)
def test_run_restore(self, activity_name="pfazure.run.restore"):
assert get_cli_activity_name(cmd=("pfazure", "run", "restore", "--name", "test_run")) == activity_name
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from unittest.mock import MagicMock
import pytest
from promptflow.exceptions import UserErrorException
@pytest.mark.unittest
class TestUtils:
def test_url_parse(self):
from promptflow.azure._utils._url_utils import BulkRunId, BulkRunURL
flow_id = (
"azureml://experiment/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/flow/"
"0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/"
"run/f88faee6-e510-45b7-9e63-08671b30b3a2"
)
flow_id = BulkRunId(flow_id)
assert flow_id.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5"
assert flow_id.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165"
assert flow_id.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766"
flow_run_url = (
"https://ml.azure.com/prompts/flow/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/"
"0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/"
"details?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourcegroups/promptflow/"
"providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus"
)
flow_url = BulkRunURL(flow_run_url)
assert flow_url.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5"
assert flow_url.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165"
assert flow_url.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766"
def test_forbidden_new_caller(self):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
with pytest.raises(UserErrorException) as e:
FlowServiceCaller(MagicMock(), MagicMock(), MagicMock())
assert "_FlowServiceCallerFactory" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_cli.py | import contextlib
import os
import sys
from pathlib import Path
from typing import List
from unittest.mock import MagicMock, patch
import pandas as pd
import pytest
from pytest_mock import MockFixture
from promptflow._sdk._constants import VIS_PORTAL_URL_TMPL
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
def run_pf_command(*args, cwd=None):
from promptflow._cli._pf_azure.entry import main
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = ["pfazure"] + list(args)
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.fixture
def operation_scope_args(subscription_id: str, resource_group_name: str, workspace_name: str):
return [
"--subscription",
subscription_id,
"--resource-group",
resource_group_name,
"--workspace-name",
workspace_name,
]
@pytest.mark.usefixtures("mock_get_azure_pf_client")
@pytest.mark.unittest
class TestAzureCli:
def test_pf_azure_version(self, capfd):
run_pf_command("--version")
out, err = capfd.readouterr()
assert "0.0.1\n" in out
def test_run_show(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get")
# show_run will print the run object, so we need to mock the return value
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"show",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_show_details(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get_details")
# show_run_details will print details, so we need to mock the return value
mocked.return_value = pd.DataFrame([{"input": "input_value", "output": "output_value"}])
run_pf_command(
"run",
"show-details",
"--name",
"test_run",
"--max-results",
"10",
"--all-results",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_show_metrics(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get_metrics")
# show_metrics will print the metrics, so we need to mock the return value
mocked.return_value = {"accuracy": 0.9}
run_pf_command(
"run",
"show-metrics",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_list_runs(
self,
mocker: MockFixture,
operation_scope_args,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked_run = MagicMock()
mocked_run._to_dict.return_value = {"name": "test_run"}
mocked = mocker.patch.object(RunOperations, "list")
# list_runs will print the run list, so we need to mock the return value
mocked.return_value = [mocked_run]
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
*operation_scope_args,
)
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
"--output",
"table",
*operation_scope_args,
)
mocker.patch.dict(
os.environ,
{
"AZUREML_ARM_WORKSPACE_NAME": workspace_name,
"AZUREML_ARM_SUBSCRIPTION": subscription_id,
"AZUREML_ARM_RESOURCEGROUP": resource_group_name,
},
)
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
)
assert mocked.call_count == 3
def test_run_visualize(
self,
operation_scope_args: List[str],
capfd: pytest.CaptureFixture,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
) -> None:
# cloud version visualize is actually a string concatenation
names = "name1,name2,name3"
run_pf_command(
"run",
"visualize",
"--names",
names,
*operation_scope_args,
)
captured = capfd.readouterr()
expected_portal_url = VIS_PORTAL_URL_TMPL.format(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
names=names,
)
assert expected_portal_url in captured.out
def test_run_archive(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "archive")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"archive",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_restore(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "restore")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"restore",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_update(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
)
mocked.assert_called_once()
def test_flow_create(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked = mocker.patch.object(FlowOperations, "create_or_update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
flow_dir = Path(flow_test_dir, "web_classification").resolve().as_posix()
run_pf_command(
"flow",
"create",
"--flow",
flow_dir,
"--set",
"display_name=test_flow",
"type=standard",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
)
mocked.assert_called_with(
flow=flow_dir,
display_name="test_flow",
type="standard",
description="test_description",
tags={"key1": "value1"},
)
def test_flow_create_with_unknown_field(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked = mocker.patch.object(FlowOperations, "create_or_update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
flow_dir = Path(flow_test_dir, "web_classification").resolve().as_posix()
run_pf_command(
"flow",
"create",
"--flow",
flow_dir,
"--set",
"random_key=random_value",
*operation_scope_args,
)
mocked.assert_called_with(flow=flow_dir, random_key="random_value")
def test_flow_list(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked_flow = MagicMock()
mocked_flow._to_dict.return_value = {"name": "test_flow"}
mocked = mocker.patch.object(FlowOperations, "list")
mocked.return_value = [mocked_flow]
run_pf_command(
"flow",
"list",
"--max-results",
"10",
"--include-archived",
"--type",
"standard",
"--include-others",
"--output",
"table",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_telemetry(
self,
mocker: MockFixture,
operation_scope_args,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked_run = MagicMock()
mocked_run._to_dict.return_value = {"name": "test_run"}
mocked = mocker.patch.object(RunOperations, "list")
# list_runs will print the run list, so we need to mock the return value
mocked.return_value = [mocked_run]
mocker.patch.dict(
os.environ,
{
"AZUREML_ARM_WORKSPACE_NAME": workspace_name,
"AZUREML_ARM_SUBSCRIPTION": subscription_id,
"AZUREML_ARM_RESOURCEGROUP": resource_group_name,
},
)
@contextlib.contextmanager
def check_workspace_info(*args, **kwargs):
if "custom_dimensions" in kwargs:
assert kwargs["custom_dimensions"]["workspace_name"] == workspace_name
assert kwargs["custom_dimensions"]["resource_group_name"] == resource_group_name
assert kwargs["custom_dimensions"]["subscription_id"] == subscription_id
yield None
with patch("promptflow._sdk._telemetry.activity.log_activity") as mock_log_activity:
mock_log_activity.side_effect = check_workspace_info
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
*operation_scope_args,
)
def test_run_download(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "download")
mocked.return_value = "fake_output_run_dir"
run_pf_command(
"run",
"download",
"--name",
"test_run",
"--output",
"fake_output_dir",
"--overwrite",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_cancel(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "cancel")
run_pf_command(
"run",
"cancel",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_config.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
import pytest
from promptflow._sdk._configuration import ConfigFileNotFound, Configuration, InvalidConfigFile
from promptflow._utils.context_utils import _change_working_dir
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
CONFIG_DATA_ROOT = Path(__file__).parent.parent.parent / "test_configs" / "configs"
@pytest.fixture
def config():
return Configuration.get_instance()
@pytest.mark.unittest
class TestConfig:
def test_get_workspace_from_config(self):
# New instance instead of get_instance() to avoid side effect
conf = Configuration(overrides={"connection.provider": "azureml"})
# Test config within flow folder
target_folder = CONFIG_DATA_ROOT / "mock_flow1"
with _change_working_dir(target_folder):
config1 = conf.get_connection_provider()
assert config1 == "azureml:" + RESOURCE_ID_FORMAT.format("sub1", "rg1", AZUREML_RESOURCE_PROVIDER, "ws1")
# Test config using flow parent folder
target_folder = CONFIG_DATA_ROOT / "mock_flow2"
with _change_working_dir(target_folder):
config2 = conf.get_connection_provider()
assert config2 == "azureml:" + RESOURCE_ID_FORMAT.format(
"sub_default", "rg_default", AZUREML_RESOURCE_PROVIDER, "ws_default"
)
# Test config not found
with pytest.raises(ConfigFileNotFound):
Configuration._get_workspace_from_config(path=CONFIG_DATA_ROOT.parent)
# Test empty config
target_folder = CONFIG_DATA_ROOT / "mock_flow_empty_config"
with pytest.raises(InvalidConfigFile):
with _change_working_dir(target_folder):
conf.get_connection_provider()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/constants.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
ENVIRON_TEST_MODE = "PROMPT_FLOW_TEST_MODE"
class TestMode:
LIVE = "live"
RECORD = "record"
REPLAY = "replay"
FILTER_HEADERS = [
"aml-user-token",
"authorization",
"date",
"etag",
"request-context",
"x-aml-cluster",
"x-ms-access-tier",
"x-ms-access-tier-inferred",
"x-ms-client-request-id",
"x-ms-client-session-id",
"x-ms-client-user-type",
"x-ms-correlation-request-id",
"x-ms-file-permission-key",
"x-ms-lease-state",
"x-ms-lease-status",
"x-ms-server-encrypted",
"x-ms-ratelimit-remaining-subscription-reads",
"x-ms-ratelimit-remaining-subscription-writes",
"x-ms-response-type",
"x-ms-request-id",
"x-ms-routing-request-id",
"x-msedge-ref",
]
class SanitizedValues:
UUID = "00000000-0000-0000-0000-000000000000"
SUBSCRIPTION_ID = "00000000-0000-0000-0000-000000000000"
RESOURCE_GROUP_NAME = "00000"
WORKSPACE_NAME = "00000"
WORKSPACE_ID = "00000000-0000-0000-0000-000000000000"
TENANT_ID = "00000000-0000-0000-0000-000000000000"
USER_OBJECT_ID = "00000000-0000-0000-0000-000000000000"
# workspace
DISCOVERY_URL = "https://eastus.api.azureml.ms/discovery"
# datastore
FAKE_KEY = "this is fake key"
FAKE_ACCOUNT_NAME = "fake_account_name"
FAKE_CONTAINER_NAME = "fake-container-name"
FAKE_FILE_SHARE_NAME = "fake-file-share-name"
# aoai connection
FAKE_API_BASE = "https://fake.openai.azure.com"
# storage
UPLOAD_HASH = "000000000000000000000000000000000000"
BLOB_STORAGE_REQUEST_HOST = "fake_account_name.blob.core.windows.net"
FILE_SHARE_REQUEST_HOST = "fake_account_name.file.core.windows.net"
# PFS
RUNTIME_NAME = "fake-runtime-name"
SESSION_ID = "000000000000000000000000000000000000000000000000"
FLOW_LINEAGE_ID = "0000000000000000000000000000000000000000000000000000000000000000"
REGION = "fake-region"
FLOW_ID = "00000000-0000-0000-0000-000000000000"
# trick: "unknown_user" is the value when client fails to get username
# use this value so that we don't do extra logic when replay
USERNAME = "unknown_user"
# MISC
EMAIL_USERNAME = "username"
class AzureMLResourceTypes:
CONNECTION = "Microsoft.MachineLearningServices/workspaces/connections"
DATASTORE = "Microsoft.MachineLearningServices/workspaces/datastores"
WORKSPACE = "Microsoft.MachineLearningServices/workspaces"
TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING = [
"TestCliWithAzure",
"TestFlowRun",
"TestFlow",
"TestTelemetry",
"TestAzureCliPerf",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import re
from dataclasses import dataclass
from typing import Dict
import jwt
from azure.core.credentials import AccessToken
from vcr.request import Request
from .constants import ENVIRON_TEST_MODE, SanitizedValues, TestMode
def get_test_mode_from_environ() -> str:
return os.getenv(ENVIRON_TEST_MODE, TestMode.LIVE)
def is_live() -> bool:
return get_test_mode_from_environ() == TestMode.LIVE
def is_record() -> bool:
return get_test_mode_from_environ() == TestMode.RECORD
def is_replay() -> bool:
return get_test_mode_from_environ() == TestMode.REPLAY
class FakeTokenCredential:
"""Refer from Azure SDK for Python repository.
https://github.com/Azure/azure-sdk-for-python/blob/main/tools/azure-sdk-tools/devtools_testutils/fake_credentials.py
"""
def __init__(self):
token = jwt.encode(
payload={
"aud": "https://management.azure.com",
},
key="",
)
self.token = AccessToken(token, 0)
self.get_token_count = 0
def get_token(self, *args, **kwargs) -> AccessToken:
self.get_token_count += 1
return self.token
@dataclass
class MockDatastore:
"""Mock Datastore class for `DatastoreOperations.get_default().name`."""
name: str
account_name: str
container_name: str
endpoint: str
def mock_datastore_get_default(*args, **kwargs) -> MockDatastore:
return MockDatastore(
name="workspaceblobstore",
account_name=SanitizedValues.FAKE_ACCOUNT_NAME,
container_name=SanitizedValues.FAKE_CONTAINER_NAME,
endpoint="core.windows.net",
)
def mock_workspace_get(*args, **kwargs):
from azure.ai.ml.entities import Workspace
return Workspace(
name=SanitizedValues.WORKSPACE_NAME,
resource_group=SanitizedValues.RESOURCE_GROUP_NAME,
discovery_url=SanitizedValues.DISCOVERY_URL,
workspace_id=SanitizedValues.WORKSPACE_ID,
)
def get_pf_client_for_replay():
from azure.ai.ml import MLClient
from promptflow.azure import PFClient
ml_client = MLClient(
credential=FakeTokenCredential(),
subscription_id=SanitizedValues.SUBSCRIPTION_ID,
resource_group_name=SanitizedValues.RESOURCE_GROUP_NAME,
workspace_name=SanitizedValues.WORKSPACE_NAME,
)
ml_client.datastores.get_default = mock_datastore_get_default
ml_client.workspaces.get = mock_workspace_get
return PFClient(ml_client=ml_client)
def sanitize_azure_workspace_triad(value: str) -> str:
sanitized_sub = re.sub(
"/(subscriptions)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format("00000000-0000-0000-0000-000000000000"),
value,
flags=re.IGNORECASE,
)
# for regex pattern for resource group name and workspace name, refer from:
# https://learn.microsoft.com/en-us/rest/api/resources/resource-groups/create-or-update?tabs=HTTP
sanitized_rg = re.sub(
r"/(resourceGroups)/[-\w\._\(\)]+",
r"/\1/{}".format("00000"),
sanitized_sub,
flags=re.IGNORECASE,
)
sanitized_ws = re.sub(
r"/(workspaces)/[-\w\._\(\)]+[/?]",
r"/\1/{}/".format("00000"),
sanitized_rg,
flags=re.IGNORECASE,
)
# workspace name can be the last part of the string
# e.g. xxx/Microsoft.MachineLearningServices/workspaces/<workspace-name>
# apply a special handle here to sanitize
if sanitized_ws.startswith("https://"):
split1, split2 = sanitized_ws.split("/")[-2:]
if split1 == "workspaces":
sanitized_ws = sanitized_ws.replace(split2, SanitizedValues.WORKSPACE_NAME)
return sanitized_ws
def sanitize_experiment_id(value: str) -> str:
value = re.sub(
r"(experimentId)=[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"\1={}".format(SanitizedValues.WORKSPACE_ID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_upload_hash(value: str) -> str:
value = re.sub(
r"(az-ml-artifacts)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(LocalUpload)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_username(value: str) -> str:
value = re.sub(
r"/(Users%2F)([^%?]+)(%2F|\?)",
r"/\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(Users/)([^/]+)(/)",
r"\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_flow_asset_id(value: str) -> str:
# input: azureml://locations/<region>/workspaces/<workspace-id>/flows/<flow-id>
# sanitize those with angle brackets
sanitized_region = re.sub(
r"/(locations)/[^/]+",
r"/\1/{}".format(SanitizedValues.REGION),
value,
flags=re.IGNORECASE,
)
sanitized_workspace_id = re.sub(
r"/(workspaces)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.WORKSPACE_ID),
sanitized_region,
flags=re.IGNORECASE,
)
sanitized_flow_id = re.sub(
r"/(flows)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}/".format(SanitizedValues.FLOW_ID),
sanitized_workspace_id,
flags=re.IGNORECASE,
)
return sanitized_flow_id
def sanitize_pfs_request_body(body: str) -> str:
# sanitize workspace triad for longhand syntax asset, e.g. "batchDataInput.dataUri"
body = sanitize_azure_workspace_triad(body)
body_dict = json.loads(body)
# /BulkRuns/submit
if "runtimeName" in body_dict:
body_dict["runtimeName"] = SanitizedValues.RUNTIME_NAME
if "sessionId" in body_dict:
body_dict["sessionId"] = SanitizedValues.SESSION_ID
if "flowLineageId" in body:
body_dict["flowLineageId"] = SanitizedValues.FLOW_LINEAGE_ID
if "flowDefinitionResourceId" in body_dict:
body_dict["flowDefinitionResourceId"] = sanitize_flow_asset_id(body_dict["flowDefinitionResourceId"])
# PFS will help handle this field, so client does not need to pass this value
if "runExperimentName" in body:
body_dict["runExperimentName"] = ""
return json.dumps(body_dict)
def sanitize_pfs_response_body(body: str) -> str:
body_dict = json.loads(body)
# BulkRuns/{flowRunId}
if "studioPortalEndpoint" in body:
body_dict["studioPortalEndpoint"] = sanitize_azure_workspace_triad(body_dict["studioPortalEndpoint"])
return json.dumps(body_dict)
def sanitize_email(value: str) -> str:
return re.sub(r"([\w\.-]+)@(microsoft.com)", r"{}@\2".format(SanitizedValues.EMAIL_USERNAME), value)
def sanitize_file_share_flow_path(value: str) -> str:
flow_folder_name = "simple_hello_world"
if flow_folder_name not in value:
return value
start_index = value.index(flow_folder_name)
flow_name_length = 38 # len("simple_hello_world-01-01-2024-00-00-00")
flow_name = value[start_index : start_index + flow_name_length]
return value.replace(flow_name, "flow_name")
def _sanitize_session_id_creating_automatic_runtime(value: str) -> str:
value = re.sub(
"/(FlowSessions)/[0-9a-f]{48}",
r"/\1/{}".format(SanitizedValues.SESSION_ID),
value,
flags=re.IGNORECASE,
)
return value
def _sanitize_operation_id_polling_automatic_runtime(value: str) -> str:
value = re.sub(
"/(operations)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.UUID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_automatic_runtime_request_path(value: str) -> str:
return _sanitize_operation_id_polling_automatic_runtime(_sanitize_session_id_creating_automatic_runtime(value))
def _is_json_payload(headers: Dict, key: str) -> bool:
if not headers:
return False
content_type = headers.get(key)
if not content_type:
return False
# content-type can be an array, e.g. ["application/json; charset=utf-8"]
content_type = content_type[0] if isinstance(content_type, list) else content_type
content_type = content_type.split(";")[0].lower()
return "application/json" in content_type
def is_json_payload_request(request: Request) -> bool:
headers = request.headers
return _is_json_payload(headers, key="Content-Type")
def is_json_payload_response(response: Dict) -> bool:
headers = response.get("headers")
# PFAzureIntegrationTestRecording will lower keys in response headers
return _is_json_payload(headers, key="content-type")
def is_httpx_response(response: Dict) -> bool:
# different from other stubs in vcrpy, httpx response uses "content" instead of "body"
# this leads to different handle logic to response
# so we need a utility to check if a response is from httpx
return "content" in response
def get_created_flow_name_from_flow_path(flow_path: str) -> str:
# pytest fixture "created_flow" will create flow on file share with timestamp as suffix
# we need to extract the flow name from the path
# flow name is expected to start with "simple_hello_world" and follow with "/flow.dag.yaml"
return flow_path[flow_path.index("simple_hello_world") : flow_path.index("/flow.dag.yaml")]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/bases.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import inspect
import json
from pathlib import Path
from typing import Dict, List
import vcr
from vcr import matchers
from vcr.request import Request
from .constants import FILTER_HEADERS, TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING, SanitizedValues
from .processors import (
AzureMLExperimentIDProcessor,
AzureOpenAIConnectionProcessor,
AzureResourceProcessor,
AzureWorkspaceTriadProcessor,
DropProcessor,
EmailProcessor,
IndexServiceProcessor,
PFSProcessor,
RecordingProcessor,
StorageProcessor,
UserInfoProcessor,
)
from .utils import (
is_httpx_response,
is_json_payload_request,
is_live,
is_record,
is_replay,
sanitize_automatic_runtime_request_path,
sanitize_azure_workspace_triad,
sanitize_file_share_flow_path,
sanitize_pfs_request_body,
sanitize_upload_hash,
)
from .variable_recorder import VariableRecorder
class PFAzureIntegrationTestRecording:
def __init__(
self,
test_class,
test_func_name: str,
user_object_id: str,
tenant_id: str,
variable_recorder: VariableRecorder,
):
self.test_class = test_class
self.test_func_name = test_func_name
self.user_object_id = user_object_id
self.tenant_id = tenant_id
self.recording_file = self._get_recording_file()
self.recording_processors = self._get_recording_processors()
self.vcr = self._init_vcr()
self._cm = None # context manager from VCR
self.cassette = None
self.variable_recorder = variable_recorder
@staticmethod
def from_test_case(test_class, test_func_name: str, **kwargs) -> "PFAzureIntegrationTestRecording":
test_class_name = test_class.__name__
if test_class_name in TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING:
return PFAzureRunIntegrationTestRecording(
test_class=test_class,
test_func_name=test_func_name,
user_object_id=kwargs["user_object_id"],
tenant_id=kwargs["tenant_id"],
variable_recorder=kwargs["variable_recorder"],
)
else:
return PFAzureIntegrationTestRecording(
test_class=test_class,
test_func_name=test_func_name,
user_object_id=kwargs["user_object_id"],
tenant_id=kwargs["tenant_id"],
variable_recorder=kwargs["variable_recorder"],
)
def _get_recording_file(self) -> Path:
# recording files are expected to be located at "tests/test_configs/recordings"
# test file path should locate at "tests/sdk_cli_azure_test/e2etests"
test_file_path = Path(inspect.getfile(self.test_class)).resolve()
recording_dir = (test_file_path.parent.parent.parent / "test_configs" / "recordings").resolve()
recording_dir.mkdir(exist_ok=True)
test_file_name = test_file_path.stem
test_class_name = self.test_class.__name__
if "[" in self.test_func_name:
# for tests that use pytest.mark.parametrize, there will be "[]" in test function name
# recording filename pattern:
# {test_file_name}_{test_class_name}_{test_func_name}/{parameter_id}.yaml
test_func_name, parameter_id = self.test_func_name.split("[")
parameter_id = parameter_id.rstrip("]")
test_func_dir = (recording_dir / f"{test_file_name}_{test_class_name}_{test_func_name}").resolve()
test_func_dir.mkdir(exist_ok=True)
recording_file = (test_func_dir / f"{parameter_id}.yaml").resolve()
else:
# for most remaining tests
# recording filename pattern: {test_file_name}_{test_class_name}_{test_func_name}.yaml
recording_filename = f"{test_file_name}_{test_class_name}_{self.test_func_name}.yaml"
recording_file = (recording_dir / recording_filename).resolve()
if is_record() and recording_file.is_file():
recording_file.unlink()
return recording_file
def _init_vcr(self) -> vcr.VCR:
_vcr = vcr.VCR(
cassette_library_dir=self.recording_file.parent.as_posix(),
before_record_request=self._process_request_recording,
before_record_response=self._process_response_recording,
decode_compressed_response=True,
record_mode="none" if is_replay() else "all",
filter_headers=FILTER_HEADERS,
)
_vcr.match_on += ("body",)
return _vcr
def enter_vcr(self):
self._cm = self.vcr.use_cassette(self.recording_file.as_posix())
self.cassette = self._cm.__enter__()
def exit_vcr(self):
if is_record():
self._postprocess_recording()
self._cm.__exit__()
def _process_request_recording(self, request: Request) -> Request:
if is_live():
return request
if is_record():
for processor in self.recording_processors:
request = processor.process_request(request)
return request
def _process_response_recording(self, response: Dict) -> Dict:
if is_live():
return response
# httpx and non-httpx responses have different structure
# non-httpx has .body.string, while httpx has .content
# in our sanitizers (processors) logic, we only handle .body.string
# so make httpx align non-httpx for less code change
is_httpx = is_httpx_response(response)
if is_httpx:
body_string = response.pop("content")
response["body"] = {"string": body_string}
else:
response["body"]["string"] = response["body"]["string"].decode("utf-8")
if is_record():
# lower and filter some headers
headers = {}
for k in response["headers"]:
if k.lower() not in FILTER_HEADERS:
headers[k.lower()] = response["headers"][k]
response["headers"] = headers
for processor in self.recording_processors:
response = processor.process_response(response)
if is_httpx:
response["content"] = response["body"]["string"]
if not is_replay():
response.pop("body")
if isinstance(response["content"], bytes):
response["content"] = response["content"].decode("utf-8")
else:
# vcrpy does not handle well with httpx, so we need some transformations
# otherwise, replay tests will break during init VCR response instance
response["status"] = {"code": response["status_code"], "message": ""}
if isinstance(response["body"]["string"], str):
response["body"]["string"] = response["body"]["string"].encode("utf-8")
else:
response["body"]["string"] = response["body"]["string"].encode("utf-8")
return response
def _get_recording_processors(self) -> List[RecordingProcessor]:
return [
AzureMLExperimentIDProcessor(),
AzureOpenAIConnectionProcessor(),
AzureResourceProcessor(),
AzureWorkspaceTriadProcessor(),
DropProcessor(),
EmailProcessor(),
IndexServiceProcessor(),
PFSProcessor(),
StorageProcessor(),
UserInfoProcessor(user_object_id=self.user_object_id, tenant_id=self.tenant_id),
]
def _postprocess_recording(self) -> None:
self._apply_replacement_for_recordings()
return
def _apply_replacement_for_recordings(self) -> None:
for i in range(len(self.cassette.data)):
req, resp = self.cassette.data[i]
req = self.variable_recorder.sanitize_request(req)
resp = self.variable_recorder.sanitize_response(resp)
self.cassette.data[i] = (req, resp)
return
class PFAzureRunIntegrationTestRecording(PFAzureIntegrationTestRecording):
"""Test class for run operations in Prompt Flow Azure.
Different from other operations, run operations have:
- duplicate network requests for stream run
- blob storage requests contain upload hash
- Submit and get run data API requests are indistinguishable without run name in body
Use a separate class with more pre/post recording processing method or
request matchers to handle above cases.
"""
def _init_vcr(self) -> vcr.VCR:
_vcr = super(PFAzureRunIntegrationTestRecording, self)._init_vcr()
_vcr.register_matcher("path", self._custom_request_path_matcher)
_vcr.register_matcher("body", self._custom_request_body_matcher)
return _vcr
def enter_vcr(self):
self._cm = self.vcr.use_cassette(
self.recording_file.as_posix(),
allow_playback_repeats=True,
filter_query_parameters=["api-version"],
)
self.cassette = self._cm.__enter__()
def _postprocess_recording(self) -> None:
self._drop_duplicate_recordings()
super(PFAzureRunIntegrationTestRecording, self)._postprocess_recording()
def _drop_duplicate_recordings(self) -> None:
# stream run operation contains two requests:
# 1. get status; 2. get logs
# before the run is terminated, there will be many duplicate requests
# getting status/logs, which leads to infinite loop during replay
# therefore apply such post process to drop those duplicate recordings
dropped_recordings = []
run_data_requests = dict()
log_content_requests = dict()
for req, resp in self.cassette.data:
# run hisotry's rundata API
if str(req.path).endswith("/rundata"):
body = req.body.decode("utf-8")
body_dict = json.loads(body)
name = body_dict["runId"]
run_data_requests[name] = (req, resp)
continue
if str(req.path).endswith("/logContent"):
log_content_requests[req.uri] = (req, resp)
continue
dropped_recordings.append((req, resp))
# append rundata recording(s)
for req, resp in run_data_requests.values():
dropped_recordings.append((req, resp))
for req, resp in log_content_requests.values():
dropped_recordings.append((req, resp))
self.cassette.data = dropped_recordings
return
def _custom_request_path_matcher(self, r1: Request, r2: Request) -> bool:
# NOTE: orders of below conditions matter, please modify with caution
# in run download scenario, observed below wired path: https://<xxx>/https://<yyy>/<remaining>
# as we don't have append/replace logic, it might result from Azure blob client,
# which is hard to patch; therefore, hack this in matcher (here)
# https:// should appear in path, so it's safe to use this as a condition
if "https://" in r1.path:
_path = str(r1.path)
endpoint = ".blob.core.windows.net/"
duplicate_path = _path[_path.index(endpoint) + len(endpoint) :]
path_for_compare = _path[: _path.index("https://")] + duplicate_path[duplicate_path.index("/") + 1 :]
return path_for_compare == r2.path
# for blob storage request, sanitize the upload hash in path
if r1.host == r2.host and r1.host == SanitizedValues.BLOB_STORAGE_REQUEST_HOST:
return sanitize_upload_hash(r1.path) == r2.path
# for file share request, mainly target pytest fixture "created_flow"
if r1.host == r2.host and r1.host == SanitizedValues.FILE_SHARE_REQUEST_HOST:
return sanitize_file_share_flow_path(r1.path) == r2.path
# for automatic runtime, sanitize flow session id in path
if r1.host == r2.host and ("FlowSessions" in r1.path and "FlowSessions" in r2.path):
path1 = sanitize_automatic_runtime_request_path(r1.path)
path2 = sanitize_automatic_runtime_request_path(r2.path)
return sanitize_azure_workspace_triad(path1) == path2
return r1.path == r2.path
def _custom_request_body_matcher(self, r1: Request, r2: Request) -> bool:
if is_json_payload_request(r1) and r1.body is not None:
# note that `sanitize_upload_hash` is not idempotent
# so we should not modify r1 directly
# otherwise it will be sanitized multiple times with many zeros
_r1 = copy.deepcopy(r1)
body1 = _r1.body.decode("utf-8")
body1 = sanitize_pfs_request_body(body1)
body1 = sanitize_upload_hash(body1)
_r1.body = body1.encode("utf-8")
try:
return matchers.body(_r1, r2)
except AssertionError:
# if not match, extra sanitize flow file share path (if exists)
# for potential pytest fixture "created_flow" scenario
body_dict = json.loads(body1)
if "flowDefinitionFilePath" in body_dict:
body_dict["flowDefinitionFilePath"] = "Users/unknown_user/promptflow/flow_name/flow.dag.yaml"
body1 = json.dumps(body_dict)
_r1.body = body1.encode("utf-8")
return matchers.body(_r1, r2)
else:
return False
else:
return matchers.body(r1, r2)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/processors.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import base64
import json
from typing import Dict
from vcr.request import Request
from .constants import AzureMLResourceTypes, SanitizedValues
from .utils import (
is_json_payload_request,
is_json_payload_response,
sanitize_azure_workspace_triad,
sanitize_email,
sanitize_experiment_id,
sanitize_pfs_request_body,
sanitize_pfs_response_body,
sanitize_upload_hash,
sanitize_username,
)
class RecordingProcessor:
def process_request(self, request: Request) -> Request:
return request
def process_response(self, response: Dict) -> Dict:
return response
class AzureWorkspaceTriadProcessor(RecordingProcessor):
"""Sanitize subscription id, resource group name and workspace name."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_azure_workspace_triad(request.uri)
return request
def process_response(self, response: Dict) -> Dict:
response["body"]["string"] = sanitize_azure_workspace_triad(response["body"]["string"])
return response
class AzureMLExperimentIDProcessor(RecordingProcessor):
"""Sanitize Azure ML experiment id, currently we use workspace id as the value."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_experiment_id(request.uri)
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
if "experimentId" in response["body"]["string"]:
body = json.loads(response["body"]["string"])
if "experimentId" in body:
body["experimentId"] = SanitizedValues.WORKSPACE_ID
response["body"]["string"] = json.dumps(body)
return response
class AzureResourceProcessor(RecordingProcessor):
"""Sanitize sensitive data in Azure resource GET response."""
def __init__(self):
# datastore related
self.storage_account_names = set()
self.storage_container_names = set()
self.file_share_names = set()
def _sanitize_request_url_for_storage(self, uri: str) -> str:
# this instance will store storage account names and container names
# so we can apply the sanitization here with simple string replace rather than regex
for account_name in self.storage_account_names:
uri = uri.replace(account_name, SanitizedValues.FAKE_ACCOUNT_NAME)
for container_name in self.storage_container_names:
uri = uri.replace(container_name, SanitizedValues.FAKE_CONTAINER_NAME)
for file_share_name in self.file_share_names:
uri = uri.replace(file_share_name, SanitizedValues.FAKE_FILE_SHARE_NAME)
return uri
def process_request(self, request: Request) -> Request:
request.uri = self._sanitize_request_url_for_storage(request.uri)
return request
def _sanitize_response_body(self, body: Dict) -> Dict:
resource_type = body.get("type")
if resource_type == AzureMLResourceTypes.WORKSPACE:
body = self._sanitize_response_for_workspace(body)
elif resource_type == AzureMLResourceTypes.CONNECTION:
body = self._sanitize_response_for_arm_connection(body)
elif resource_type == AzureMLResourceTypes.DATASTORE:
body = self._sanitize_response_for_datastore(body)
return body
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
body = json.loads(response["body"]["string"])
if isinstance(body, dict):
# response can be a list sometimes (e.g. get workspace datastores)
# need to sanitize each with a for loop
if "value" in body:
resources = body["value"]
for i in range(len(resources)):
resources[i] = self._sanitize_response_body(resources[i])
body["value"] = resources
else:
body = self._sanitize_response_body(body)
response["body"]["string"] = json.dumps(body)
return response
def _sanitize_response_for_workspace(self, body: Dict) -> Dict:
filter_keys = ["identity", "properties", "systemData"]
for k in filter_keys:
if k in body:
body.pop(k)
# need during the constructor of FlowServiceCaller (for vNet case)
body["properties"] = {"discoveryUrl": SanitizedValues.DISCOVERY_URL}
name = body["name"]
body["name"] = SanitizedValues.WORKSPACE_NAME
body["id"] = body["id"].replace(name, SanitizedValues.WORKSPACE_NAME)
return body
def _sanitize_response_for_arm_connection(self, body: Dict) -> Dict:
if body["properties"]["authType"] == "CustomKeys":
# custom connection, sanitize "properties.credentials.keys"
body["properties"]["credentials"]["keys"] = {}
else:
# others, sanitize "properties.credentials.key"
body["properties"]["credentials"]["key"] = "_"
body["properties"]["target"] = "_"
return body
def _sanitize_response_for_datastore(self, body: Dict) -> Dict:
body["properties"]["subscriptionId"] = SanitizedValues.SUBSCRIPTION_ID
body["properties"]["resourceGroup"] = SanitizedValues.RESOURCE_GROUP_NAME
self.storage_account_names.add(body["properties"]["accountName"])
body["properties"]["accountName"] = SanitizedValues.FAKE_ACCOUNT_NAME
# blob storage
if "containerName" in body["properties"]:
self.storage_container_names.add(body["properties"]["containerName"])
body["properties"]["containerName"] = SanitizedValues.FAKE_CONTAINER_NAME
# file share
elif "fileShareName" in body["properties"]:
self.file_share_names.add(body["properties"]["fileShareName"])
body["properties"]["fileShareName"] = SanitizedValues.FAKE_FILE_SHARE_NAME
return body
class AzureOpenAIConnectionProcessor(RecordingProcessor):
"""Sanitize api_base in AOAI connection GET response."""
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
body = json.loads(response["body"]["string"])
if isinstance(body, dict) and body.get("connectionType") == "AzureOpenAI":
body["configs"]["api_base"] = SanitizedValues.FAKE_API_BASE
response["body"]["string"] = json.dumps(body)
return response
class StorageProcessor(RecordingProcessor):
"""Sanitize sensitive data during storage operations when submit run."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_upload_hash(request.uri)
request.uri = sanitize_username(request.uri)
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = sanitize_upload_hash(body)
body = sanitize_username(body)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = sanitize_username(response["body"]["string"])
body = json.loads(response["body"]["string"])
if isinstance(body, dict):
self._sanitize_list_secrets_response(body)
response["body"]["string"] = json.dumps(body)
return response
def _sanitize_list_secrets_response(self, body: Dict) -> Dict:
if "key" in body:
b64_key = base64.b64encode(SanitizedValues.FAKE_KEY.encode("ascii"))
body["key"] = str(b64_key, "ascii")
return body
class DropProcessor(RecordingProcessor):
"""Ignore some requests that won't be used during playback."""
def process_request(self, request: Request) -> Request:
if "/metadata/identity/oauth2/token" in request.path:
return None
return request
class PFSProcessor(RecordingProcessor):
"""Sanitize request/response for PFS operations."""
def process_request(self, request: Request) -> Request:
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = sanitize_pfs_request_body(body)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = sanitize_pfs_response_body(response["body"]["string"])
return response
class UserInfoProcessor(RecordingProcessor):
"""Sanitize user object id and tenant id in responses."""
def __init__(self, user_object_id: str, tenant_id: str):
self.user_object_id = user_object_id
self.tenant_id = tenant_id
def process_request(self, request: Request) -> Request:
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = str(body).replace(self.user_object_id, SanitizedValues.USER_OBJECT_ID)
body = body.replace(self.tenant_id, SanitizedValues.TENANT_ID)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = str(response["body"]["string"]).replace(
self.user_object_id, SanitizedValues.USER_OBJECT_ID
)
response["body"]["string"] = str(response["body"]["string"]).replace(
self.tenant_id, SanitizedValues.TENANT_ID
)
return response
class IndexServiceProcessor(RecordingProcessor):
"""Sanitize index service responses."""
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
if "continuationToken" in response["body"]["string"]:
body = json.loads(response["body"]["string"])
body.pop("continuationToken", None)
response["body"]["string"] = json.dumps(body)
return response
class EmailProcessor(RecordingProcessor):
"""Sanitize email address in responses."""
def process_response(self, response: Dict) -> Dict:
response["body"]["string"] = sanitize_email(response["body"]["string"])
return response
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/variable_recorder.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from typing import Dict
from vcr.request import Request
from .utils import is_httpx_response, is_json_payload_request
class VariableRecorder:
def __init__(self):
self.variables = dict()
def get_or_record_variable(self, variable: str, default: str) -> str:
return self.variables.setdefault(variable, default)
def sanitize_request(self, request: Request) -> Request:
request.uri = self._sanitize(request.uri)
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = self._sanitize(body)
request.body = body.encode("utf-8")
return request
def sanitize_response(self, response: Dict) -> Dict:
# httpx response: .content, string; no action needed
# non-httpx response: .body.string, bytes, need decode/encode
if is_httpx_response(response):
response["content"] = self._sanitize(response["content"])
else:
response["body"]["string"] = response["body"]["string"].decode("utf-8")
response["body"]["string"] = self._sanitize(response["body"]["string"])
response["body"]["string"] = response["body"]["string"].encode("utf-8")
return response
def _sanitize(self, value: str) -> str:
for k, v in self.variables.items():
value = value.replace(v, k)
return value
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from .bases import PFAzureIntegrationTestRecording
from .constants import SanitizedValues
from .utils import get_created_flow_name_from_flow_path, get_pf_client_for_replay, is_live, is_record, is_replay
from .variable_recorder import VariableRecorder
__all__ = [
"PFAzureIntegrationTestRecording",
"SanitizedValues",
"VariableRecorder",
"get_created_flow_name_from_flow_path",
"get_pf_client_for_replay",
"is_live",
"is_record",
"is_replay",
]
| 0 |
promptflow_repo/promptflow/src/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/exceptions.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
import string
import traceback
from enum import Enum
from functools import cached_property
class ErrorCategory(str, Enum):
USER_ERROR = "UserError"
SYSTEM_ERROR = "SystemError"
UNKNOWN = "Unknown"
class ErrorTarget(str, Enum):
"""The target of the error, indicates which part of the system the error occurs."""
EXECUTOR = "Executor"
BATCH = "Batch"
FLOW_EXECUTOR = "FlowExecutor"
NODE_EXECUTOR = "NodeExecutor"
TOOL = "Tool"
AZURE_RUN_STORAGE = "AzureRunStorage"
RUNTIME = "Runtime"
UNKNOWN = "Unknown"
RUN_TRACKER = "RunTracker"
RUN_STORAGE = "RunStorage"
CONTROL_PLANE_SDK = "ControlPlaneSDK"
SERVING_APP = "ServingApp"
FLOW_INVOKER = "FlowInvoker"
FUNCTION_PATH = "FunctionPath"
class PromptflowException(Exception):
"""Base exception for all errors.
:param message: A message describing the error. This is the error message the user will see.
:type message: str
:param target: The name of the element that caused the exception to be thrown.
:type target: ~promptflow.exceptions.ErrorTarget
:param error: The original exception if any.
:type error: Exception
"""
def __init__(
self,
message="",
message_format="",
target: ErrorTarget = ErrorTarget.UNKNOWN,
module=None,
**kwargs,
):
self._inner_exception = kwargs.get("error")
self._target = target
self._module = module
self._message_format = message_format
self._kwargs = kwargs
if message:
self._message = str(message)
elif self.message_format:
self._message = self.message_format.format(**self.message_parameters)
else:
self._message = self.__class__.__name__
super().__init__(self._message)
@property
def message(self):
"""The error message."""
return self._message
@property
def message_format(self):
"""The error message format."""
return self._message_format
@cached_property
def message_parameters(self):
"""The error message parameters."""
if not self._kwargs:
return {}
required_arguments = self.get_arguments_from_message_format(self.message_format)
parameters = {}
for argument in required_arguments:
if argument not in self._kwargs:
parameters[argument] = f"<{argument}>"
else:
parameters[argument] = self._kwargs[argument]
return parameters
@cached_property
def serializable_message_parameters(self):
"""The serializable error message parameters."""
return {k: str(v) for k, v in self.message_parameters.items()}
@property
def target(self):
"""The error target.
:return: The error target.
:rtype: ~promptflow.exceptions.ErrorTarget
"""
return self._target
@target.setter
def target(self, value):
"""Set the error target."""
self._target = value
@property
def module(self):
"""The module of the error that occurs.
It is similar to `target` but is more specific.
It is meant to store the Python module name of the code that raises the exception.
"""
return self._module
@module.setter
def module(self, value):
"""Set the module of the error that occurs."""
self._module = value
@property
def reference_code(self):
"""The reference code of the error."""
# In Python 3.11, the __str__ method of the Enum type returns the name of the enumeration member.
# However, in earlier Python versions, the __str__ method returns the value of the enumeration member.
# Therefore, when dealing with this situation, we need to make some additional adjustments.
target = self.target.value if isinstance(self.target, ErrorTarget) else self.target
if self.module:
return f"{target}/{self.module}"
else:
return target
@property
def inner_exception(self):
"""Get the inner exception.
The inner exception can be set via either style:
1) Set via the error parameter in the constructor.
raise PromptflowException("message", error=inner_exception)
2) Set via raise from statement.
raise PromptflowException("message") from inner_exception
"""
return self._inner_exception or self.__cause__
@property
def additional_info(self):
"""Return a dict of the additional info of the exception.
By default, this information could usually be empty.
However, we can still define additional info for some specific exception.
i.e. For ToolExcutionError, we may add the tool's line number, stacktrace to the additional info.
"""
return None
@property
def error_codes(self):
"""Returns a list of the error codes for this exception.
The error codes is defined the same as the class inheritance.
i.e. For ToolExcutionError which inherits from UserErrorException,
The result would be ["UserErrorException", "ToolExecutionError"].
"""
if getattr(self, "_error_codes", None):
return self._error_codes
from promptflow._utils.exception_utils import infer_error_code_from_class
def reversed_error_codes():
for clz in self.__class__.__mro__:
if clz is PromptflowException:
break
yield infer_error_code_from_class(clz)
self._error_codes = list(reversed_error_codes())
self._error_codes.reverse()
return self._error_codes
def get_arguments_from_message_format(self, message_format):
"""Get the arguments from the message format."""
def iter_field_name():
if not message_format:
return
for _, field_name, _, _ in string.Formatter().parse(message_format):
if field_name is not None:
yield field_name
return set(iter_field_name())
def __str__(self):
"""Return the error message.
Some child classes may override this method to return a more detailed error message."""
return self.message
class UserErrorException(PromptflowException):
"""Exception raised when invalid or unsupported inputs are provided."""
pass
class SystemErrorException(PromptflowException):
"""Exception raised when service error is triggered."""
pass
class ValidationException(UserErrorException):
"""Exception raised when validation fails."""
pass
class _ErrorInfo:
@classmethod
def get_error_info(cls, e: Exception):
if not isinstance(e, Exception):
return None, None, None, None, None
e = cls.select_exception(e)
if cls._is_system_error(e):
return (
ErrorCategory.SYSTEM_ERROR,
cls._error_type(e),
cls._error_target(e),
cls._error_message(e),
cls._error_detail(e),
)
if cls._is_user_error(e):
return (
ErrorCategory.USER_ERROR,
cls._error_type(e),
cls._error_target(e),
cls._error_message(e),
cls._error_detail(e),
)
return ErrorCategory.UNKNOWN, cls._error_type(e), ErrorTarget.UNKNOWN, "", cls._error_detail(e)
@classmethod
def select_exception(cls, e: Exception):
"""Select the exception in e and e.__cause__, and prioritize the Exception defined in the promptflow."""
if isinstance(e, PromptflowException):
return e
# raise Exception("message") from PromptflowException("message")
if e.__cause__ and isinstance(e.__cause__, PromptflowException):
return e.__cause__
return e
@classmethod
def _is_system_error(cls, e: Exception):
if isinstance(e, SystemErrorException):
return True
return False
@classmethod
def _is_user_error(cls, e: Exception):
if isinstance(e, UserErrorException):
return True
return False
@classmethod
def _error_type(cls, e: Exception):
"""Return exception type.
Note:
For PromptflowException(error=ValueError(message="xxx")) or
UserErrorException(error=ValueError(message="xxx")) or
SystemErrorException(error=ValueError(message="xxx")),
the desired return type is ValueError,
not PromptflowException, UserErrorException and SystemErrorException.
"""
error_type = type(e).__name__
if type(e) in (PromptflowException, UserErrorException, SystemErrorException):
if e.inner_exception:
error_type = type(e.inner_exception).__name__
return error_type
@classmethod
def _error_target(cls, e: Exception):
return getattr(e, "target", ErrorTarget.UNKNOWN)
@classmethod
def _error_message(cls, e: Exception):
return getattr(e, "message_format", "")
@classmethod
def _error_detail(cls, e: Exception):
exception_codes = cls._get_exception_codes(e)
exception_code = None
for item in exception_codes[::-1]:
if "promptflow" in item["module"]: # Only record information within the promptflow package
exception_code = item
break
if not exception_code:
return ""
return (
f"module={exception_code['module']}, "
f"code={exception_code['exception_code']}, "
f"lineno={exception_code['lineno']}."
)
@classmethod
def _get_exception_codes(cls, e: Exception) -> list:
"""
Obtain information on each line of the traceback, including the module name,
exception code and lineno where the error occurred.
:param e: Exception object
:return: A list, each item contains information for each row of the traceback, which format is like this:
{
'module': 'promptflow.executor.errors',
'exception_code': 'return self.inner_exception.additional_info',
'lineno': 223
}
"""
exception_codes = []
traceback_info = traceback.extract_tb(e.__traceback__)
for item in traceback_info:
lineno = item.lineno
filename = item.filename
line_code = item.line
module = inspect.getmodule(None, _filename=filename)
exception_code = {"module": "", "exception_code": line_code, "lineno": lineno}
if module is not None:
exception_code["module"] = module.__name__
exception_codes.append(exception_code)
return exception_codes
| 0 |
promptflow_repo/promptflow/src/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_constants.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
CONNECTION_NAME_PROPERTY = "__connection_name"
CONNECTION_SECRET_KEYS = "__secret_keys"
PROMPTFLOW_CONNECTIONS = "PROMPTFLOW_CONNECTIONS"
PROMPTFLOW_SECRETS_FILE = "PROMPTFLOW_SECRETS_FILE"
PF_NO_INTERACTIVE_LOGIN = "PF_NO_INTERACTIVE_LOGIN"
PF_LOGGING_LEVEL = "PF_LOGGING_LEVEL"
OPENAI_API_KEY = "openai-api-key"
BING_API_KEY = "bing-api-key"
AOAI_API_KEY = "aoai-api-key"
SERPAPI_API_KEY = "serpapi-api-key"
CONTENT_SAFETY_API_KEY = "content-safety-api-key"
ERROR_RESPONSE_COMPONENT_NAME = "promptflow"
EXTENSION_UA = "prompt-flow-extension"
LANGUAGE_KEY = "language"
DEFAULT_ENCODING = "utf-8"
# Constants related to execution
LINE_NUMBER_KEY = "line_number" # Using the same key with portal.
LINE_TIMEOUT_SEC = 600
class FlowLanguage:
"""The enum of tool source type."""
Python = "python"
CSharp = "csharp"
class AvailableIDE:
VS = "vs"
VS_CODE = "vsc"
USER_AGENT = "USER_AGENT"
PF_USER_AGENT = "PF_USER_AGENT"
CLI_PACKAGE_NAME = 'promptflow'
CURRENT_VERSION = 'current_version'
LATEST_VERSION = 'latest_version'
LAST_HINT_TIME = 'last_hint_time'
LAST_CHECK_TIME = 'last_check_time'
PF_VERSION_CHECK = "pf_version_check.json"
HINT_INTERVAL_DAY = 7
GET_PYPI_INTERVAL_DAY = 7
_ENV_PF_INSTALLER = 'PF_INSTALLER'
| 0 |
promptflow_repo/promptflow/src/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_version.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
VERSION = "0.0.1"
| 0 |
promptflow_repo/promptflow/src/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from promptflow._core.metric_logger import log_metric
# flake8: noqa
from promptflow._core.tool import ToolProvider, tool
from promptflow._core.tracer import trace
# control plane sdk functions
from promptflow._sdk._load_functions import load_flow, load_run
from ._sdk._pf_client import PFClient
from ._version import VERSION
# backward compatibility
log_flow_metric = log_metric
__version__ = VERSION
__all__ = ["PFClient", "load_flow", "load_run", "log_metric", "ToolProvider", "tool", "trace"]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_errors.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._sdk._constants import BULK_RUN_ERRORS
from promptflow.exceptions import ErrorTarget, SystemErrorException, UserErrorException
class SDKError(UserErrorException):
"""SDK base class, target default is CONTROL_PLANE_SDK."""
def __init__(
self,
message="",
message_format="",
target: ErrorTarget = ErrorTarget.CONTROL_PLANE_SDK,
module=None,
**kwargs,
):
super().__init__(message=message, message_format=message_format, target=target, module=module, **kwargs)
class SDKInternalError(SystemErrorException):
"""SDK internal error."""
def __init__(
self,
message="",
message_format="",
target: ErrorTarget = ErrorTarget.CONTROL_PLANE_SDK,
module=None,
**kwargs,
):
super().__init__(message=message, message_format=message_format, target=target, module=module, **kwargs)
class RunExistsError(SDKError):
"""Exception raised when run already exists."""
pass
class RunNotFoundError(SDKError):
"""Exception raised if run cannot be found."""
pass
class InvalidRunStatusError(SDKError):
"""Exception raised if run status is invalid."""
pass
class UnsecureConnectionError(SDKError):
"""Exception raised if connection is not secure."""
pass
class DecryptConnectionError(SDKError):
"""Exception raised if connection decryption failed."""
pass
class StoreConnectionEncryptionKeyError(SDKError):
"""Exception raised if no keyring backend."""
pass
class InvalidFlowError(SDKError):
"""Exception raised if flow definition is not legal."""
pass
class ConnectionNotFoundError(SDKError):
"""Exception raised if connection is not found."""
pass
class InvalidRunError(SDKError):
"""Exception raised if run name is not legal."""
pass
class GenerateFlowToolsJsonError(SDKError):
"""Exception raised if flow tools json generation failed."""
pass
class BulkRunException(SDKError):
"""Exception raised when bulk run failed."""
def __init__(self, *, message="", failed_lines, total_lines, errors, module: str = None, **kwargs):
self.failed_lines = failed_lines
self.total_lines = total_lines
self._additional_info = {
BULK_RUN_ERRORS: errors,
}
message = f"First error message is: {message}"
# bulk run error is line error only when failed_lines > 0
if isinstance(failed_lines, int) and isinstance(total_lines, int) and failed_lines > 0:
message = f"Failed to run {failed_lines}/{total_lines} lines. " + message
super().__init__(message=message, target=ErrorTarget.RUNTIME, module=module, **kwargs)
@property
def additional_info(self):
"""Set the tool exception details as additional info."""
return self._additional_info
class RunOperationParameterError(SDKError):
"""Exception raised when list run failed."""
pass
class RunOperationError(SDKError):
"""Exception raised when run operation failed."""
pass
class FlowOperationError(SDKError):
"""Exception raised when flow operation failed."""
pass
class ExperimentExistsError(SDKError):
"""Exception raised when experiment already exists."""
pass
class ExperimentNotFoundError(SDKError):
"""Exception raised if experiment cannot be found."""
pass
class ExperimentValidationError(SDKError):
"""Exception raised if experiment validation failed."""
pass
class ExperimentValueError(SDKError):
"""Exception raised if experiment validation failed."""
pass
class ExperimentHasCycle(SDKError):
"""Exception raised if experiment validation failed."""
pass
class DownloadInternalError(SDKInternalError):
"""Exception raised if download internal error."""
pass
class ExperimentCommandRunError(SDKError):
"""Exception raised if experiment validation failed."""
pass
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_mlflow.py | # flake8: noqa
"""Put some imports here for mlflow promptflow flavor usage.
DO NOT change the module names in "all" list. If the interface has changed in source code, wrap it here and keep
original function/module names the same as before, otherwise mldesigner will be broken by this change.
"""
from promptflow._sdk._constants import DAG_FILE_NAME
from promptflow._sdk._serving.flow_invoker import FlowInvoker
from promptflow._sdk._submitter import remove_additional_includes
from promptflow._sdk._utils import _merge_local_code_and_additional_includes
from promptflow._sdk.entities._flow import Flow
__all__ = [
"Flow",
"FlowInvoker",
"remove_additional_includes",
"_merge_local_code_and_additional_includes",
"DAG_FILE_NAME",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import collections
import hashlib
import json
import multiprocessing
import os
import platform
import re
import shutil
import stat
import sys
import tempfile
import zipfile
from contextlib import contextmanager
from enum import Enum
from functools import partial
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple, Union
from urllib.parse import urlparse
import keyring
import pydash
from cryptography.fernet import Fernet
from filelock import FileLock
from jinja2 import Template
from keyring.errors import NoKeyringError
from marshmallow import ValidationError
import promptflow
from promptflow._constants import EXTENSION_UA, PF_NO_INTERACTIVE_LOGIN, PF_USER_AGENT, USER_AGENT
from promptflow._core.tool_meta_generator import generate_tool_meta_dict_by_file
from promptflow._core.tools_manager import gen_dynamic_list, retrieve_tool_func_result
from promptflow._sdk._constants import (
DAG_FILE_NAME,
DEFAULT_ENCODING,
FLOW_TOOLS_JSON,
FLOW_TOOLS_JSON_GEN_TIMEOUT,
HOME_PROMPT_FLOW_DIR,
KEYRING_ENCRYPTION_KEY_NAME,
KEYRING_ENCRYPTION_LOCK_PATH,
KEYRING_SYSTEM,
NODE,
NODE_VARIANTS,
NODES,
PROMPT_FLOW_DIR_NAME,
REFRESH_CONNECTIONS_DIR_LOCK_PATH,
REGISTRY_URI_PREFIX,
REMOTE_URI_PREFIX,
USE_VARIANTS,
VARIANTS,
CommonYamlFields,
ConnectionProvider,
)
from promptflow._sdk._errors import (
DecryptConnectionError,
GenerateFlowToolsJsonError,
StoreConnectionEncryptionKeyError,
UnsecureConnectionError,
)
from promptflow._sdk._vendor import IgnoreFile, get_ignore_file, get_upload_files_from_folder
from promptflow._utils.context_utils import _change_working_dir, inject_sys_path
from promptflow._utils.dataclass_serializer import serialize
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import dump_yaml, load_yaml, load_yaml_string
from promptflow.contracts.tool import ToolType
from promptflow.exceptions import ErrorTarget, UserErrorException
logger = get_cli_sdk_logger()
def snake_to_camel(name):
return re.sub(r"(?:^|_)([a-z])", lambda x: x.group(1).upper(), name)
def find_type_in_override(params_override: Optional[list] = None) -> Optional[str]:
params_override = params_override or []
for override in params_override:
if CommonYamlFields.TYPE in override:
return override[CommonYamlFields.TYPE]
return None
# region Encryption
CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING = None
ENCRYPTION_KEY_IN_KEY_RING = None
@contextmanager
def use_customized_encryption_key(encryption_key: str):
global CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING
CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING = encryption_key
yield
CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING = None
def set_encryption_key(encryption_key: Union[str, bytes]):
if isinstance(encryption_key, bytes):
encryption_key = encryption_key.decode("utf-8")
keyring.set_password("promptflow", "encryption_key", encryption_key)
_encryption_key_lock = FileLock(KEYRING_ENCRYPTION_LOCK_PATH)
def get_encryption_key(generate_if_not_found: bool = False) -> str:
global CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING
global ENCRYPTION_KEY_IN_KEY_RING
if CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING is not None:
return CUSTOMIZED_ENCRYPTION_KEY_IN_KEY_RING
if ENCRYPTION_KEY_IN_KEY_RING is not None:
return ENCRYPTION_KEY_IN_KEY_RING
def _get_from_keyring():
try:
# Cache encryption key as mac will pop window to ask for permission when calling get_password
return keyring.get_password(KEYRING_SYSTEM, KEYRING_ENCRYPTION_KEY_NAME)
except NoKeyringError as e:
raise StoreConnectionEncryptionKeyError(
"System keyring backend service not found in your operating system. "
"See https://pypi.org/project/keyring/ to install requirement for different operating system, "
"or 'pip install keyrings.alt' to use the third-party backend. Reach more detail about this error at "
"https://microsoft.github.io/promptflow/how-to-guides/faq.html#connection-creation-failed-with-storeconnectionencryptionkeyerror" # noqa: E501
) from e
ENCRYPTION_KEY_IN_KEY_RING = _get_from_keyring()
if ENCRYPTION_KEY_IN_KEY_RING is not None or not generate_if_not_found:
return ENCRYPTION_KEY_IN_KEY_RING
_encryption_key_lock.acquire()
# Note: we access the keyring twice, as global var can't share across processes.
ENCRYPTION_KEY_IN_KEY_RING = _get_from_keyring()
if ENCRYPTION_KEY_IN_KEY_RING is not None:
return ENCRYPTION_KEY_IN_KEY_RING
try:
ENCRYPTION_KEY_IN_KEY_RING = Fernet.generate_key().decode("utf-8")
keyring.set_password(KEYRING_SYSTEM, KEYRING_ENCRYPTION_KEY_NAME, ENCRYPTION_KEY_IN_KEY_RING)
finally:
_encryption_key_lock.release()
return ENCRYPTION_KEY_IN_KEY_RING
def encrypt_secret_value(secret_value):
encryption_key = get_encryption_key(generate_if_not_found=True)
fernet_client = Fernet(encryption_key)
token = fernet_client.encrypt(secret_value.encode("utf-8"))
return token.decode("utf-8")
def decrypt_secret_value(connection_name, encrypted_secret_value):
encryption_key = get_encryption_key()
if encryption_key is None:
raise Exception("Encryption key not found in keyring.")
fernet_client = Fernet(encryption_key)
try:
return fernet_client.decrypt(encrypted_secret_value.encode("utf-8")).decode("utf-8")
except Exception as e:
if len(encrypted_secret_value) < 57:
# This is to workaround old custom secrets that are not encrypted with Fernet.
# Fernet token: https://github.com/fernet/spec/blob/master/Spec.md
# Format: Version ‖ Timestamp ‖ IV ‖ Ciphertext ‖ HMAC
# Version: 8 bits, Timestamp: 64 bits, IV: 128 bits, HMAC: 256 bits,
# Ciphertext variable length, multiple of 128 bits
# So the minimum length of a Fernet token is 57 bytes
raise UnsecureConnectionError(
f"Please delete and re-create connection {connection_name} "
f"due to a security issue in the old sdk version."
)
raise DecryptConnectionError(
f"Decrypt connection {connection_name} secret failed: {str(e)}. "
f"If you have ever changed your encryption key manually, "
f"please revert it back to the original one, or delete all connections and re-create them."
)
# endregion
def decorate_validation_error(schema: Any, pretty_error: str, additional_message: str = "") -> str:
return f"Validation for {schema.__name__} failed:\n\n {pretty_error} \n\n {additional_message}"
def load_from_dict(schema: Any, data: Dict, context: Dict, additional_message: str = "", **kwargs):
try:
return schema(context=context).load(data, **kwargs)
except ValidationError as e:
pretty_error = json.dumps(e.normalized_messages(), indent=2)
raise ValidationError(decorate_validation_error(schema, pretty_error, additional_message))
def strip_quotation(value):
"""
To avoid escaping chars in command args, args will be surrounded in quotas.
Need to remove the pair of quotation first.
"""
if value.startswith('"') and value.endswith('"'):
return value[1:-1]
elif value.startswith("'") and value.endswith("'"):
return value[1:-1]
else:
return value
def parse_variant(variant: str) -> Tuple[str, str]:
variant_regex = r"\${([^.]+).([^}]+)}"
match = re.match(variant_regex, strip_quotation(variant))
if match:
return match.group(1), match.group(2)
else:
error = ValueError(
f"Invalid variant format: {variant}, variant should be in format of ${{TUNING_NODE.VARIANT}}"
)
raise UserErrorException(
target=ErrorTarget.CONTROL_PLANE_SDK,
message=str(error),
error=error,
)
def _match_reference(env_val: str):
env_val = env_val.strip()
m = re.match(r"^\$\{([^.]+)\.([^.]+)}$", env_val)
if not m:
return None, None
name, key = m.groups()
return name, key
# !!! Attention!!!: Please make sure you have contact with PRS team before changing the interface.
def get_used_connection_names_from_environment_variables():
"""The function will get all potential related connection names from current environment variables.
for example, if part of env var is
{
"ENV_VAR_1": "${my_connection.key}",
"ENV_VAR_2": "${my_connection.key2}",
"ENV_VAR_3": "${my_connection2.key}",
}
The function will return {"my_connection", "my_connection2"}.
"""
return get_used_connection_names_from_dict(os.environ)
def get_used_connection_names_from_dict(connection_dict: dict):
connection_names = set()
for key, val in connection_dict.items():
connection_name, _ = _match_reference(val)
if connection_name:
connection_names.add(connection_name)
return connection_names
# !!! Attention!!!: Please make sure you have contact with PRS team before changing the interface.
def update_environment_variables_with_connections(built_connections):
"""The function will result env var value ${my_connection.key} to the real connection keys."""
return update_dict_value_with_connections(built_connections, os.environ)
def _match_env_reference(val: str):
try:
val = val.strip()
m = re.match(r"^\$\{env:(.+)}$", val)
if not m:
return None
name = m.groups()[0]
return name
except Exception:
# for exceptions when val is not a string, return
return None
def override_connection_config_with_environment_variable(connections: Dict[str, dict]):
"""
The function will use relevant environment variable to override connection configurations. For instance, if there
is a custom connection named 'custom_connection' with a configuration key called 'chat_deployment_name,' the
function will attempt to retrieve 'chat_deployment_name' from the environment variable
'CUSTOM_CONNECTION_CHAT_DEPLOYMENT_NAME' by default. If the environment variable is not set, it will use the
original value as a fallback.
"""
for connection_name, connection in connections.items():
values = connection.get("value", {})
for key, val in values.items():
connection_name = connection_name.replace(" ", "_")
env_name = f"{connection_name}_{key}".upper()
if env_name not in os.environ:
continue
values[key] = os.environ[env_name]
logger.info(f"Connection {connection_name}'s {key} is overridden with environment variable {env_name}")
return connections
def resolve_connections_environment_variable_reference(connections: Dict[str, dict]):
"""The function will resolve connection secrets env var reference like api_key: ${env:KEY}"""
for connection in connections.values():
values = connection.get("value", {})
for key, val in values.items():
if not _match_env_reference(val):
continue
env_name = _match_env_reference(val)
if env_name not in os.environ:
raise UserErrorException(f"Environment variable {env_name} is not found.")
values[key] = os.environ[env_name]
return connections
def update_dict_value_with_connections(built_connections, connection_dict: dict):
for key, val in connection_dict.items():
connection_name, connection_key = _match_reference(val)
if connection_name is None:
continue
if connection_name not in built_connections:
continue
if connection_key not in built_connections[connection_name]["value"]:
continue
connection_dict[key] = built_connections[connection_name]["value"][connection_key]
def in_jupyter_notebook() -> bool:
"""
Checks if user is using a Jupyter Notebook. This is necessary because logging is not allowed in
non-Jupyter contexts.
Adapted from https://stackoverflow.com/a/22424821
"""
try: # cspell:ignore ipython
from IPython import get_ipython
if "IPKernelApp" not in get_ipython().config:
return False
except ImportError:
return False
except AttributeError:
return False
return True
def render_jinja_template(template_path, *, trim_blocks=True, keep_trailing_newline=True, **kwargs):
with open(template_path, "r", encoding=DEFAULT_ENCODING) as f:
template = Template(f.read(), trim_blocks=trim_blocks, keep_trailing_newline=keep_trailing_newline)
return template.render(**kwargs)
def print_yellow_warning(message):
from colorama import Fore, init
init(autoreset=True)
print(Fore.YELLOW + message)
def print_red_error(message):
from colorama import Fore, init
init(autoreset=True)
print(Fore.RED + message)
def safe_parse_object_list(obj_list, parser, message_generator):
results = []
for obj in obj_list:
try:
results.append(parser(obj))
except Exception as e:
extended_message = f"{message_generator(obj)} Error: {type(e).__name__}, {str(e)}"
print_yellow_warning(extended_message)
return results
def _normalize_identifier_name(name):
normalized_name = name.lower()
normalized_name = re.sub(r"[\W_]", " ", normalized_name) # No non-word characters
normalized_name = re.sub(" +", " ", normalized_name).strip() # No double spaces, leading or trailing spaces
if re.match(r"\d", normalized_name):
normalized_name = "n" + normalized_name # No leading digits
return normalized_name
def _sanitize_python_variable_name(name: str):
return _normalize_identifier_name(name).replace(" ", "_")
def _get_additional_includes(yaml_path):
flow_dag = load_yaml(yaml_path)
return flow_dag.get("additional_includes", [])
def _is_folder_to_compress(path: Path) -> bool:
"""Check if the additional include needs to compress corresponding folder as a zip.
For example, given additional include /mnt/c/hello.zip
1) if a file named /mnt/c/hello.zip already exists, return False (simply copy)
2) if a folder named /mnt/c/hello exists, return True (compress as a zip and copy)
:param path: Given path in additional include.
:type path: Path
:return: If the path need to be compressed as a zip file.
:rtype: bool
"""
if path.suffix != ".zip":
return False
# if zip file exists, simply copy as other additional includes
if path.exists():
return False
# remove .zip suffix and check whether the folder exists
stem_path = path.parent / path.stem
return stem_path.is_dir()
def _resolve_folder_to_compress(base_path: Path, include: str, dst_path: Path) -> None:
"""resolve the zip additional include, need to compress corresponding folder."""
zip_additional_include = (base_path / include).resolve()
folder_to_zip = zip_additional_include.parent / zip_additional_include.stem
zip_file = dst_path / zip_additional_include.name
with zipfile.ZipFile(zip_file, "w") as zf:
zf.write(folder_to_zip, os.path.relpath(folder_to_zip, folder_to_zip.parent)) # write root in zip
for root, _, files in os.walk(folder_to_zip, followlinks=True):
for file in files:
file_path = os.path.join(folder_to_zip, file)
zf.write(file_path, os.path.relpath(file_path, folder_to_zip.parent))
@contextmanager
def _merge_local_code_and_additional_includes(code_path: Path):
# TODO: unify variable names: flow_dir_path, flow_dag_path, flow_path
def additional_includes_copy(src, relative_path, target_dir):
if src.is_file():
dst = Path(target_dir) / relative_path
dst.parent.mkdir(parents=True, exist_ok=True)
if dst.exists():
logger.warning(
"Found duplicate file in additional includes, "
f"additional include file {src} will overwrite {relative_path}"
)
shutil.copy2(src, dst)
else:
for name in src.glob("*"):
additional_includes_copy(name, Path(relative_path) / name.name, target_dir)
if code_path.is_dir():
yaml_path = (Path(code_path) / DAG_FILE_NAME).resolve()
code_path = code_path.resolve()
else:
yaml_path = code_path.resolve()
code_path = code_path.parent.resolve()
with tempfile.TemporaryDirectory() as temp_dir:
shutil.copytree(code_path.resolve().as_posix(), temp_dir, dirs_exist_ok=True)
for item in _get_additional_includes(yaml_path):
src_path = Path(item)
if not src_path.is_absolute():
src_path = (code_path / item).resolve()
if _is_folder_to_compress(src_path):
_resolve_folder_to_compress(code_path, item, Path(temp_dir))
# early continue as the folder is compressed as a zip file
continue
if not src_path.exists():
error = ValueError(f"Unable to find additional include {item}")
raise UserErrorException(
target=ErrorTarget.CONTROL_PLANE_SDK,
message=str(error),
error=error,
)
additional_includes_copy(src_path, relative_path=src_path.name, target_dir=temp_dir)
yield temp_dir
def incremental_print(log: str, printed: int, fileout) -> int:
count = 0
for line in log.splitlines():
if count >= printed:
fileout.write(line + "\n")
printed += 1
count += 1
return printed
def get_promptflow_sdk_version() -> str:
try:
return promptflow.__version__
except AttributeError:
# if promptflow is installed from source, it does not have __version__ attribute
return "0.0.1"
def print_pf_version():
print("promptflow\t\t\t {}".format(get_promptflow_sdk_version()))
print()
print("Executable '{}'".format(os.path.abspath(sys.executable)))
print("Python ({}) {}".format(platform.system(), sys.version))
class PromptflowIgnoreFile(IgnoreFile):
# TODO add more files to this list.
IGNORE_FILE = [".runs", "__pycache__"]
def __init__(self, prompt_flow_path: Union[Path, str]):
super(PromptflowIgnoreFile, self).__init__(prompt_flow_path)
self._path = Path(prompt_flow_path)
self._ignore_tools_json = False
@property
def base_path(self) -> Path:
return self._path
def _get_ignore_list(self):
"""Get ignore list from ignore file contents."""
if not self.exists():
return []
base_ignore = get_ignore_file(self.base_path)
result = self.IGNORE_FILE + base_ignore._get_ignore_list()
if self._ignore_tools_json:
result.append(f"{PROMPT_FLOW_DIR_NAME}/{FLOW_TOOLS_JSON}")
return result
def _generate_meta_from_files(
tools: List[Tuple[str, str]], flow_directory: Path, tools_dict: dict, exception_dict: dict
) -> None:
with _change_working_dir(flow_directory), inject_sys_path(flow_directory):
for source, tool_type in tools:
try:
tools_dict[source] = generate_tool_meta_dict_by_file(source, ToolType(tool_type))
except Exception as e:
exception_dict[source] = str(e)
def _generate_tool_meta(
flow_directory: Path,
tools: List[Tuple[str, str]],
raise_error: bool,
timeout: int,
*,
include_errors_in_output: bool = False,
load_in_subprocess: bool = True,
) -> Dict[str, dict]:
"""Generate tool meta from files.
:param flow_directory: flow directory
:param tools: tool list
:param raise_error: whether raise error when generate meta failed
:param timeout: timeout for generate meta
:param include_errors_in_output: whether include errors in output
:param load_in_subprocess: whether load tool meta with subprocess to prevent system path disturb. Default is True.
If set to False, will load tool meta in sync mode and timeout need to be handled outside current process.
:return: tool meta dict
"""
if load_in_subprocess:
# use multiprocess generate to avoid system path disturb
manager = multiprocessing.Manager()
tools_dict = manager.dict()
exception_dict = manager.dict()
p = multiprocessing.Process(
target=_generate_meta_from_files, args=(tools, flow_directory, tools_dict, exception_dict)
)
p.start()
p.join(timeout=timeout)
if p.is_alive():
logger.warning(f"Generate meta timeout after {timeout} seconds, terminate the process.")
p.terminate()
p.join()
else:
tools_dict, exception_dict = {}, {}
# There is no built-in method to forcefully stop a running thread/coroutine in Python
# because abruptly stopping a thread can cause issues like resource leaks,
# deadlocks, or inconsistent states.
# Caller needs to handle the timeout outside current process.
logger.warning(
"Generate meta in current process and timeout won't take effect. "
"Please handle timeout manually outside current process."
)
_generate_meta_from_files(tools, flow_directory, tools_dict, exception_dict)
res = {source: tool for source, tool in tools_dict.items()}
for source in res:
# remove name in tool meta
res[source].pop("name")
# convert string Enum to string
if isinstance(res[source]["type"], Enum):
res[source]["type"] = res[source]["type"].value
# not all tools have inputs, so check first
if "inputs" in res[source]:
for tool_input in res[source]["inputs"]:
tool_input_type = res[source]["inputs"][tool_input]["type"]
for i in range(len(tool_input_type)):
if isinstance(tool_input_type[i], Enum):
tool_input_type[i] = tool_input_type[i].value
# collect errors and print warnings
errors = {
source: exception for source, exception in exception_dict.items()
} # for not processed tools, regard as timeout error
for source, _ in tools:
if source not in res and source not in errors:
errors[source] = f"Generate meta timeout for source {source!r}."
for source in errors:
if include_errors_in_output:
res[source] = errors[source]
else:
logger.warning(f"Generate meta for source {source!r} failed: {errors[source]}.")
if raise_error and len(errors) > 0:
error_message = "Generate meta failed, detail error(s):\n" + json.dumps(errors, indent=4)
raise GenerateFlowToolsJsonError(error_message)
return res
def _retrieve_tool_func_result(func_call_scenario: str, function_config: Dict):
"""Retrieve tool func result according to func_call_scenario.
:param func_call_scenario: function call scenario
:param function_config: function config in tool meta. Should contain'func_path' and 'func_kwargs'.
:return: func call result according to func_call_scenario.
"""
func_path = function_config.get("func_path", "")
func_kwargs = function_config.get("func_kwargs", {})
# May call azure control plane api in the custom function to list Azure resources.
# which may need Azure workspace triple.
# TODO: move this method to a common place.
from promptflow._cli._utils import get_workspace_triad_from_local
workspace_triad = get_workspace_triad_from_local()
if workspace_triad.subscription_id and workspace_triad.resource_group_name and workspace_triad.workspace_name:
result = retrieve_tool_func_result(func_call_scenario, func_path, func_kwargs, workspace_triad._asdict())
# if no workspace triple available, just skip.
else:
result = retrieve_tool_func_result(func_call_scenario, func_path, func_kwargs)
result_with_log = {"result": result, "logs": {}}
return result_with_log
def _gen_dynamic_list(function_config: Dict) -> List:
"""Generate dynamic list for a tool input.
:param function_config: function config in tool meta. Should contain'func_path' and 'func_kwargs'.
:return: a list of tool input dynamic enums.
"""
func_path = function_config.get("func_path", "")
func_kwargs = function_config.get("func_kwargs", {})
# May call azure control plane api in the custom function to list Azure resources.
# which may need Azure workspace triple.
# TODO: move this method to a common place.
from promptflow._cli._utils import get_workspace_triad_from_local
workspace_triad = get_workspace_triad_from_local()
if workspace_triad.subscription_id and workspace_triad.resource_group_name and workspace_triad.workspace_name:
return gen_dynamic_list(func_path, func_kwargs, workspace_triad._asdict())
# if no workspace triple available, just skip.
else:
return gen_dynamic_list(func_path, func_kwargs)
def _generate_package_tools(keys: Optional[List[str]] = None) -> dict:
from promptflow._core.tools_manager import collect_package_tools
return collect_package_tools(keys=keys)
def _update_involved_tools_and_packages(
_node,
_node_path,
*,
tools: List,
used_packages: Set,
source_path_mapping: Dict[str, List[str]],
):
source, tool_type = pydash.get(_node, "source.path", None), _node.get("type", None)
used_packages.add(pydash.get(_node, "source.tool", None))
if source is None or tool_type is None:
return
# for custom LLM tool, its source points to the used prompt template so handle it as prompt tool
if tool_type == ToolType.CUSTOM_LLM:
tool_type = ToolType.PROMPT
if pydash.get(_node, "source.type") not in ["code", "package_with_prompt"]:
return
pair = (source, tool_type.lower())
if pair not in tools:
tools.append(pair)
source_path_mapping[source].append(f"{_node_path}.source.path")
def _get_involved_code_and_package(
data: dict,
) -> Tuple[List[Tuple[str, str]], Set[str], Dict[str, List[str]]]:
tools = [] # List[Tuple[source_file, tool_type]]
used_packages = set()
source_path_mapping = collections.defaultdict(list)
for node_i, node in enumerate(data[NODES]):
_update_involved_tools_and_packages(
node,
f"{NODES}.{node_i}",
tools=tools,
used_packages=used_packages,
source_path_mapping=source_path_mapping,
)
# understand DAG to parse variants
# TODO: should we allow source to appear both in node and node variants?
if node.get(USE_VARIANTS) is True:
node_variants = data[NODE_VARIANTS][node["name"]]
for variant_id in node_variants[VARIANTS]:
node_with_variant = node_variants[VARIANTS][variant_id][NODE]
_update_involved_tools_and_packages(
node_with_variant,
f"{NODE_VARIANTS}.{node['name']}.{VARIANTS}.{variant_id}.{NODE}",
tools=tools,
used_packages=used_packages,
source_path_mapping=source_path_mapping,
)
if None in used_packages:
used_packages.remove(None)
return tools, used_packages, source_path_mapping
def generate_flow_tools_json(
flow_directory: Union[str, Path],
dump: bool = True,
raise_error: bool = True,
timeout: int = FLOW_TOOLS_JSON_GEN_TIMEOUT,
*,
include_errors_in_output: bool = False,
target_source: str = None,
used_packages_only: bool = False,
source_path_mapping: Dict[str, List[str]] = None,
) -> dict:
"""Generate flow.tools.json for a flow directory.
:param flow_directory: path to flow directory.
:param dump: whether to dump to .promptflow/flow.tools.json, default value is True.
:param raise_error: whether to raise the error, default value is True.
:param timeout: timeout for generation, default value is 60 seconds.
:param include_errors_in_output: whether to include error messages in output, default value is False.
:param target_source: the source name to filter result, default value is None. Note that we will update system path
in coroutine if target_source is provided given it's expected to be from a specific cli call.
:param used_packages_only: whether to only include used packages, default value is False.
:param source_path_mapping: if specified, record yaml paths for each source.
"""
flow_directory = Path(flow_directory).resolve()
# parse flow DAG
data = load_yaml(flow_directory / DAG_FILE_NAME)
tools, used_packages, _source_path_mapping = _get_involved_code_and_package(data)
# update passed in source_path_mapping if specified
if source_path_mapping is not None:
source_path_mapping.update(_source_path_mapping)
# filter tools by target_source if specified
if target_source is not None:
tools = list(filter(lambda x: x[0] == target_source, tools))
# generate content
# TODO: remove type in tools (input) and code (output)
flow_tools = {
"code": _generate_tool_meta(
flow_directory,
tools,
raise_error=raise_error,
timeout=timeout,
include_errors_in_output=include_errors_in_output,
# we don't need to protect system path according to the target usage when target_source is specified
load_in_subprocess=target_source is None,
),
# specified source may only appear in code tools
"package": {}
if target_source is not None
else _generate_package_tools(keys=list(used_packages) if used_packages_only else None),
}
if dump:
# dump as flow.tools.json
promptflow_folder = flow_directory / PROMPT_FLOW_DIR_NAME
promptflow_folder.mkdir(exist_ok=True)
with open(promptflow_folder / FLOW_TOOLS_JSON, mode="w", encoding=DEFAULT_ENCODING) as f:
json.dump(flow_tools, f, indent=4)
return flow_tools
class ClientUserAgentUtil:
"""SDK/CLI side user agent utilities."""
@classmethod
def _get_context(cls):
from promptflow._core.operation_context import OperationContext
return OperationContext.get_instance()
@classmethod
def get_user_agent(cls):
from promptflow._core.operation_context import OperationContext
context = cls._get_context()
# directly get from context since client side won't need promptflow/xxx.
return context.get(OperationContext.USER_AGENT_KEY, "").strip()
@classmethod
def append_user_agent(cls, user_agent: Optional[str]):
if not user_agent:
return
context = cls._get_context()
context.append_user_agent(user_agent)
@classmethod
def update_user_agent_from_env_var(cls):
# this is for backward compatibility: we should use PF_USER_AGENT in newer versions.
for env_name in [USER_AGENT, PF_USER_AGENT]:
if env_name in os.environ:
cls.append_user_agent(os.environ[env_name])
@classmethod
def update_user_agent_from_config(cls):
"""Update user agent from config. 1p customer will set it. We'll add PFCustomer_ as prefix."""
from promptflow._sdk._configuration import Configuration
config = Configuration.get_instance()
user_agent = config.get_user_agent()
if user_agent:
cls.append_user_agent(user_agent)
def setup_user_agent_to_operation_context(user_agent):
"""Setup user agent to OperationContext.
For calls from extension, ua will be like: prompt-flow-extension/ promptflow-cli/ promptflow-sdk/
For calls from CLI, ua will be like: promptflow-cli/ promptflow-sdk/
For calls from SDK, ua will be like: promptflow-sdk/
For 1p customer call which set user agent in config, ua will be like: PFCustomer_XXX/
"""
# add user added UA after SDK/CLI
ClientUserAgentUtil.append_user_agent(user_agent)
ClientUserAgentUtil.update_user_agent_from_env_var()
ClientUserAgentUtil.update_user_agent_from_config()
return ClientUserAgentUtil.get_user_agent()
def call_from_extension() -> bool:
"""Return true if current request is from extension."""
ClientUserAgentUtil.update_user_agent_from_env_var()
user_agent = ClientUserAgentUtil.get_user_agent()
return EXTENSION_UA in user_agent
def generate_random_string(length: int = 6) -> str:
import random
import string
return "".join(random.choice(string.ascii_lowercase) for _ in range(length))
def copy_tree_respect_template_and_ignore_file(source: Path, target: Path, render_context: dict = None):
def is_template(path: str):
return path.endswith(".jinja2")
for source_path, target_path in get_upload_files_from_folder(
path=source,
ignore_file=PromptflowIgnoreFile(prompt_flow_path=source),
):
(target / target_path).parent.mkdir(parents=True, exist_ok=True)
if render_context is None or not is_template(source_path):
shutil.copy(source_path, target / target_path)
else:
(target / target_path[: -len(".jinja2")]).write_bytes(
# always use unix line ending
render_jinja_template(source_path, **render_context)
.encode("utf-8")
.replace(b"\r\n", b"\n"),
)
def get_local_connections_from_executable(
executable, client, connections_to_ignore: List[str] = None, connections_to_add: List[str] = None
):
"""Get local connections from executable.
executable: The executable flow object.
client: Local client to get connections.
connections_to_ignore: The connection names to ignore when getting connections.
connections_to_add: The connection names to add when getting connections.
"""
connection_names = executable.get_connection_names()
if connections_to_add:
connection_names.update(connections_to_add)
connections_to_ignore = connections_to_ignore or []
result = {}
for n in connection_names:
if n not in connections_to_ignore:
conn = client.connections.get(name=n, with_secrets=True)
result[n] = conn._to_execution_connection_dict()
return result
def _generate_connections_dir():
# Get Python executable path
python_path = sys.executable
# Hash the Python executable path
hash_object = hashlib.sha1(python_path.encode())
hex_dig = hash_object.hexdigest()
# Generate the connections system path using the hash
connections_dir = (HOME_PROMPT_FLOW_DIR / "envs" / hex_dig / "connections").resolve()
return connections_dir
_refresh_connection_dir_lock = FileLock(REFRESH_CONNECTIONS_DIR_LOCK_PATH)
# This function is used by extension to generate the connection files every time collect tools.
def refresh_connections_dir(connection_spec_files, connection_template_yamls):
connections_dir = _generate_connections_dir()
# Use lock to prevent concurrent access
with _refresh_connection_dir_lock:
if os.path.isdir(connections_dir):
shutil.rmtree(connections_dir)
os.makedirs(connections_dir)
if connection_spec_files and connection_template_yamls:
for connection_name, content in connection_spec_files.items():
file_name = connection_name + ".spec.json"
with open(connections_dir / file_name, "w", encoding=DEFAULT_ENCODING) as f:
json.dump(content, f, indent=2)
# use YAML to dump template file in order to keep the comments
for connection_name, content in connection_template_yamls.items():
yaml_data = load_yaml_string(content)
file_name = connection_name + ".template.yaml"
with open(connections_dir / file_name, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml(yaml_data, f)
def dump_flow_result(flow_folder, prefix, flow_result=None, node_result=None, custom_path=None):
"""Dump flow result for extension.
:param flow_folder: The flow folder.
:param prefix: The file prefix.
:param flow_result: The flow result returned by exec_line.
:param node_result: The node result when test node returned by load_and_exec_node.
:param custom_path: The custom path to dump flow result.
"""
if flow_result:
flow_serialize_result = {
"flow_runs": [serialize(flow_result.run_info)],
"node_runs": [serialize(run) for run in flow_result.node_run_infos.values()],
}
else:
flow_serialize_result = {
"flow_runs": [],
"node_runs": [serialize(node_result)],
}
dump_folder = Path(flow_folder) / PROMPT_FLOW_DIR_NAME if custom_path is None else Path(custom_path)
dump_folder.mkdir(parents=True, exist_ok=True)
with open(dump_folder / f"{prefix}.detail.json", "w", encoding=DEFAULT_ENCODING) as f:
json.dump(flow_serialize_result, f, indent=2, ensure_ascii=False)
if node_result:
metrics = flow_serialize_result["node_runs"][0]["metrics"]
output = flow_serialize_result["node_runs"][0]["output"]
else:
metrics = flow_serialize_result["flow_runs"][0]["metrics"]
output = flow_serialize_result["flow_runs"][0]["output"]
if metrics:
with open(dump_folder / f"{prefix}.metrics.json", "w", encoding=DEFAULT_ENCODING) as f:
json.dump(metrics, f, indent=2, ensure_ascii=False)
if output:
with open(dump_folder / f"{prefix}.output.json", "w", encoding=DEFAULT_ENCODING) as f:
json.dump(output, f, indent=2, ensure_ascii=False)
def read_write_by_user():
return stat.S_IRUSR | stat.S_IWUSR
def remove_empty_element_from_dict(obj: dict) -> dict:
"""Remove empty element from dict, e.g. {"a": 1, "b": {}} -> {"a": 1}"""
new_dict = {}
for key, value in obj.items():
if isinstance(value, dict):
value = remove_empty_element_from_dict(value)
if value is not None:
new_dict[key] = value
return new_dict
def is_github_codespaces():
# Ref:
# https://docs.github.com/en/codespaces/developing-in-a-codespace/default-environment-variables-for-your-codespace
return os.environ.get("CODESPACES", None) == "true"
def interactive_credential_disabled():
return os.environ.get(PF_NO_INTERACTIVE_LOGIN, "false").lower() == "true"
def is_from_cli():
from promptflow._cli._user_agent import USER_AGENT as CLI_UA
return CLI_UA in ClientUserAgentUtil.get_user_agent()
def is_url(value: Union[PathLike, str]) -> bool:
try:
result = urlparse(str(value))
return all([result.scheme, result.netloc])
except ValueError:
return False
def is_remote_uri(obj) -> bool:
# return True if it's supported remote uri
if isinstance(obj, str):
if obj.startswith(REMOTE_URI_PREFIX):
# azureml: started, azureml:name:version, azureml://xxx
return True
elif is_url(obj):
return True
return False
def parse_remote_flow_pattern(flow: object) -> str:
# Check if the input matches the correct pattern
flow_name = None
error_message = (
f"Invalid remote flow pattern, got {flow!r} while expecting "
f"a remote workspace flow like '{REMOTE_URI_PREFIX}<flow-name>', or a remote registry flow like "
f"'{REMOTE_URI_PREFIX}//registries/<registry-name>/models/<flow-name>/versions/<version>'"
)
if not isinstance(flow, str) or not flow.startswith(REMOTE_URI_PREFIX):
raise UserErrorException(error_message)
# check for registry flow pattern
if flow.startswith(REGISTRY_URI_PREFIX):
pattern = r"azureml://registries/.*?/models/(?P<name>.*?)/versions/(?P<version>.*?)$"
match = re.match(pattern, flow)
if not match or len(match.groups()) != 2:
raise UserErrorException(error_message)
flow_name, _ = match.groups()
# check for workspace flow pattern
elif flow.startswith(REMOTE_URI_PREFIX):
pattern = r"azureml:(?P<name>.*?)$"
match = re.match(pattern, flow)
if not match or len(match.groups()) != 1:
raise UserErrorException(error_message)
flow_name = match.groups()[0]
return flow_name
def get_connection_operation(connection_provider: str, credential=None, user_agent: str = None):
"""
Get connection operation based on connection provider.
This function will be called by PFClient, so please do not refer to PFClient in this function.
:param connection_provider: Connection provider, e.g. local, azureml, azureml://subscriptions..., etc.
:type connection_provider: str
:param credential: Credential when remote provider, default to chained credential DefaultAzureCredential.
:type credential: object
:param user_agent: User Agent
:type user_agent: str
"""
if connection_provider == ConnectionProvider.LOCAL.value:
from promptflow._sdk.operations._connection_operations import ConnectionOperations
logger.debug("PFClient using local connection operations.")
connection_operation = ConnectionOperations()
elif connection_provider.startswith(ConnectionProvider.AZUREML.value):
from promptflow._sdk.operations._local_azure_connection_operations import LocalAzureConnectionOperations
logger.debug(f"PFClient using local azure connection operations with credential {credential}.")
if user_agent is None:
connection_operation = LocalAzureConnectionOperations(connection_provider, credential=credential)
else:
connection_operation = LocalAzureConnectionOperations(connection_provider, user_agent=user_agent)
else:
error = ValueError(f"Unsupported connection provider: {connection_provider}")
raise UserErrorException(
target=ErrorTarget.CONTROL_PLANE_SDK,
message=str(error),
error=error,
)
return connection_operation
# extract open read/write as partial to centralize the encoding
read_open = partial(open, mode="r", encoding=DEFAULT_ENCODING)
write_open = partial(open, mode="w", encoding=DEFAULT_ENCODING)
# extract some file operations inside this file
def json_load(file) -> str:
with read_open(file) as f:
return json.load(f)
def json_dump(obj, file) -> None:
with write_open(file) as f:
json.dump(obj, f, ensure_ascii=False)
def pd_read_json(file) -> "DataFrame":
import pandas as pd
with read_open(file) as f:
return pd.read_json(f, orient="records", lines=True)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_visualize_functions.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
import tempfile
import webbrowser
from dataclasses import asdict
from pathlib import Path
from typing import Optional
from promptflow._sdk._constants import VIS_HTML_TMPL, VIS_JS_BUNDLE_FILENAME
from promptflow._sdk._utils import render_jinja_template
from promptflow.contracts._run_management import VisualizationRender
def generate_html_string(data: dict) -> str:
visualization_render = VisualizationRender(data=data)
return render_jinja_template(VIS_HTML_TMPL, **asdict(visualization_render))
def try_to_open_html(html_path: str) -> None:
print(f"The HTML file is generated at {str(Path(html_path).resolve().absolute())!r}.")
print("Trying to view the result in a web browser...")
web_browser_opened = False
web_browser_opened = webbrowser.open(f"file://{html_path}")
if not web_browser_opened:
print(
f"Failed to visualize from the web browser, the HTML file locates at {html_path!r}.\n"
"You can manually open it with your web browser, or try SDK to visualize it."
)
else:
print("Successfully visualized from the web browser.")
def dump_js_bundle(html_path: str) -> None:
js_bundle_src_path = Path(__file__).parent / "data" / VIS_JS_BUNDLE_FILENAME
js_bundle_dst_path = Path(html_path).parent / VIS_JS_BUNDLE_FILENAME
shutil.copy(js_bundle_src_path, js_bundle_dst_path)
def dump_html(html_string: str, html_path: Optional[str] = None, open_html: bool = True) -> None:
if html_path is not None:
with open(html_path, "w") as f:
f.write(html_string)
else:
with tempfile.NamedTemporaryFile(prefix="pf-visualize-detail-", suffix=".html", delete=False) as f:
f.write(html_string.encode("utf-8"))
html_path = f.name
dump_js_bundle(html_path)
if open_html:
try_to_open_html(html_path)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_configuration.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import os.path
import uuid
from itertools import product
from os import PathLike
from pathlib import Path
from typing import Optional, Union
import pydash
from promptflow._sdk._constants import (
DEFAULT_ENCODING,
FLOW_DIRECTORY_MACRO_IN_CONFIG,
HOME_PROMPT_FLOW_DIR,
SERVICE_CONFIG_FILE,
ConnectionProvider,
)
from promptflow._sdk._utils import call_from_extension, read_write_by_user
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import dump_yaml, load_yaml
from promptflow.exceptions import ErrorTarget, ValidationException
logger = get_cli_sdk_logger()
class ConfigFileNotFound(ValidationException):
pass
class InvalidConfigFile(ValidationException):
pass
class InvalidConfigValue(ValidationException):
pass
class Configuration(object):
CONFIG_PATH = Path(HOME_PROMPT_FLOW_DIR) / SERVICE_CONFIG_FILE
COLLECT_TELEMETRY = "telemetry.enabled"
EXTENSION_COLLECT_TELEMETRY = "extension.telemetry_enabled"
INSTALLATION_ID = "cli.installation_id"
CONNECTION_PROVIDER = "connection.provider"
RUN_OUTPUT_PATH = "run.output_path"
USER_AGENT = "user_agent"
ENABLE_INTERNAL_FEATURES = "enable_internal_features"
_instance = None
def __init__(self, overrides=None):
if not os.path.exists(self.CONFIG_PATH.parent):
os.makedirs(self.CONFIG_PATH.parent, exist_ok=True)
if not os.path.exists(self.CONFIG_PATH):
self.CONFIG_PATH.touch(mode=read_write_by_user(), exist_ok=True)
with open(self.CONFIG_PATH, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml({}, f)
self._config = load_yaml(self.CONFIG_PATH)
if not self._config:
self._config = {}
# Allow config override by kwargs
overrides = overrides or {}
for key, value in overrides.items():
self._validate(key, value)
pydash.set_(self._config, key, value)
@property
def config(self):
return self._config
@classmethod
def get_instance(cls):
"""Use this to get instance to avoid multiple copies of same global config."""
if cls._instance is None:
cls._instance = Configuration()
return cls._instance
def set_config(self, key, value):
"""Store config in file to avoid concurrent write."""
self._validate(key, value)
pydash.set_(self._config, key, value)
with open(self.CONFIG_PATH, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml(self._config, f)
def get_config(self, key):
try:
return pydash.get(self._config, key, None)
except Exception: # pylint: disable=broad-except
return None
def get_all(self):
return self._config
@classmethod
def _get_workspace_from_config(
cls,
*,
path: Union[PathLike, str] = None,
) -> str:
"""Return a workspace arm id from an existing Azure Machine Learning Workspace.
Reads workspace configuration from a file. Throws an exception if the config file can't be found.
:param path: The path to the config file or starting directory to search.
The parameter defaults to starting the search in the current directory.
:type path: str
:return: The workspace arm id for an existing Azure ML Workspace.
:rtype: ~str
"""
from azure.ai.ml import MLClient
from azure.ai.ml._file_utils.file_utils import traverse_up_path_and_find_file
from azure.ai.ml.constants._common import AZUREML_RESOURCE_PROVIDER, RESOURCE_ID_FORMAT
path = Path(".") if path is None else Path(path)
if path.is_file():
found_path = path
else:
# Based on priority
# Look in config dirs like .azureml or plain directory
# with None
directories_to_look = [".azureml", None]
files_to_look = ["config.json"]
found_path = None
for curr_dir, curr_file in product(directories_to_look, files_to_look):
logging.debug(
"No config file directly found, starting search from %s "
"directory, for %s file name to be present in "
"%s subdirectory",
path,
curr_file,
curr_dir,
)
found_path = traverse_up_path_and_find_file(
path=path,
file_name=curr_file,
directory_name=curr_dir,
num_levels=20,
)
if found_path:
break
if not found_path:
msg = (
"We could not find config.json in: {} or in its parent directories. "
"Please provide the full path to the config file or ensure that "
"config.json exists in the parent directories."
)
raise ConfigFileNotFound(
message=msg.format(path),
no_personal_data_message=msg.format("[path]"),
target=ErrorTarget.CONTROL_PLANE_SDK,
)
subscription_id, resource_group, workspace_name = MLClient._get_workspace_info(found_path)
if not (subscription_id and resource_group and workspace_name):
raise InvalidConfigFile(
"The subscription_id, resource_group and workspace_name can not be empty. Got: "
f"subscription_id: {subscription_id}, resource_group: {resource_group}, "
f"workspace_name: {workspace_name} from file {found_path}."
)
return RESOURCE_ID_FORMAT.format(subscription_id, resource_group, AZUREML_RESOURCE_PROVIDER, workspace_name)
def get_connection_provider(self, path=None) -> Optional[str]:
"""Get the current connection provider. Default to local if not configured."""
provider = self.get_config(key=self.CONNECTION_PROVIDER)
return self.resolve_connection_provider(provider, path=path)
@classmethod
def resolve_connection_provider(cls, provider, path=None) -> Optional[str]:
if provider is None:
return ConnectionProvider.LOCAL
if provider == ConnectionProvider.AZUREML.value:
# Note: The below function has azure-ai-ml dependency.
return "azureml:" + cls._get_workspace_from_config(path=path)
# If provider not None and not Azure, return it directly.
# It can be the full path of a workspace.
return provider
def get_telemetry_consent(self) -> Optional[bool]:
"""Get the current telemetry consent value. Return None if not configured."""
if call_from_extension():
return self.get_config(key=self.EXTENSION_COLLECT_TELEMETRY)
return self.get_config(key=self.COLLECT_TELEMETRY)
def set_telemetry_consent(self, value):
"""Set the telemetry consent value and store in local."""
self.set_config(key=self.COLLECT_TELEMETRY, value=value)
def get_or_set_installation_id(self):
"""Get user id if exists, otherwise set installation id and return it."""
user_id = self.get_config(key=self.INSTALLATION_ID)
if user_id:
return user_id
else:
user_id = str(uuid.uuid4())
self.set_config(key=self.INSTALLATION_ID, value=user_id)
return user_id
def get_run_output_path(self) -> Optional[str]:
"""Get the run output path in local."""
return self.get_config(key=self.RUN_OUTPUT_PATH)
def _to_dict(self):
return self._config
@staticmethod
def _validate(key: str, value: str) -> None:
if key == Configuration.RUN_OUTPUT_PATH:
if value.rstrip("/").endswith(FLOW_DIRECTORY_MACRO_IN_CONFIG):
raise InvalidConfigValue(
"Cannot specify flow directory as run output path; "
"if you want to specify run output path under flow directory, "
"please use its child folder, e.g. '${flow_directory}/.runs'."
)
return
def get_user_agent(self) -> Optional[str]:
"""Get customer set user agent. If set, will add prefix `PFCustomer_`"""
user_agent = self.get_config(key=self.USER_AGENT)
if user_agent:
return f"PFCustomer_{user_agent}"
return user_agent
def is_internal_features_enabled(self) -> Optional[bool]:
"""Get enable_preview_features"""
result = self.get_config(key=self.ENABLE_INTERNAL_FEATURES)
if isinstance(result, str):
return result.lower() == "true"
return result is True
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_pf_client.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Union
from .._utils.logger_utils import get_cli_sdk_logger
from ._configuration import Configuration
from ._constants import MAX_SHOW_DETAILS_RESULTS
from ._load_functions import load_flow
from ._user_agent import USER_AGENT
from ._utils import ClientUserAgentUtil, get_connection_operation, setup_user_agent_to_operation_context
from .entities import Run
from .entities._eager_flow import EagerFlow
from .operations import RunOperations
from .operations._connection_operations import ConnectionOperations
from .operations._experiment_operations import ExperimentOperations
from .operations._flow_operations import FlowOperations
from .operations._tool_operations import ToolOperations
logger = get_cli_sdk_logger()
def _create_run(run: Run, **kwargs):
client = PFClient()
return client.runs.create_or_update(run=run, **kwargs)
class PFClient:
"""A client class to interact with prompt flow entities."""
def __init__(self, **kwargs):
logger.debug("PFClient init with kwargs: %s", kwargs)
self._runs = RunOperations()
self._connection_provider = kwargs.pop("connection_provider", None)
self._config = kwargs.get("config", None) or {}
# The credential is used as an option to override
# DefaultAzureCredential when using workspace connection provider
self._credential = kwargs.get("credential", None)
# Lazy init to avoid azure credential requires too early
self._connections = None
self._flows = FlowOperations(client=self)
self._tools = ToolOperations()
# add user agent from kwargs if any
if isinstance(kwargs.get("user_agent"), str):
ClientUserAgentUtil.append_user_agent(kwargs["user_agent"])
self._experiments = ExperimentOperations(self)
setup_user_agent_to_operation_context(USER_AGENT)
def run(
self,
flow: Union[str, PathLike],
*,
data: Union[str, PathLike] = None,
run: Union[str, Run] = None,
column_mapping: dict = None,
variant: str = None,
connections: dict = None,
environment_variables: dict = None,
name: str = None,
display_name: str = None,
tags: Dict[str, str] = None,
**kwargs,
) -> Run:
"""Run flow against provided data or run.
.. note::
At least one of the ``data`` or ``run`` parameters must be provided.
.. admonition:: Column_mapping
Column mapping is a mapping from flow input name to specified values.
If specified, the flow will be executed with provided value for specified inputs.
The value can be:
- from data:
- ``data.col1``
- from run:
- ``run.inputs.col1``: if need reference run's inputs
- ``run.output.col1``: if need reference run's outputs
- Example:
- ``{"ground_truth": "${data.answer}", "prediction": "${run.outputs.answer}"}``
:param flow: Path to the flow directory to run evaluation.
:type flow: Union[str, PathLike]
:param data: Pointer to the test data (of variant bulk runs) for eval runs.
:type data: Union[str, PathLike]
:param run: Flow run ID or flow run. This parameter helps keep lineage between
the current run and variant runs. Batch outputs can be
referenced as ``${run.outputs.col_name}`` in inputs_mapping.
:type run: Union[str, ~promptflow.entities.Run]
:param column_mapping: Define a data flow logic to map input data.
:type column_mapping: Dict[str, str]
:param variant: Node & variant name in the format of ``${node_name.variant_name}``.
The default variant will be used if not specified.
:type variant: str
:param connections: Overwrite node level connections with provided values.
Example: ``{"node1": {"connection": "new_connection", "deployment_name": "gpt-35-turbo"}}``
:type connections: Dict[str, Dict[str, str]]
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: ``{"key1": "${my_connection.api_key}", "key2"="value2"}``
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: Dict[str, str]
:param name: Name of the run.
:type name: str
:param display_name: Display name of the run.
:type display_name: str
:param tags: Tags of the run.
:type tags: Dict[str, str]
:return: Flow run info.
:rtype: ~promptflow.entities.Run
"""
if not os.path.exists(flow):
raise FileNotFoundError(f"flow path {flow} does not exist")
if data and not os.path.exists(data):
raise FileNotFoundError(f"data path {data} does not exist")
if not run and not data:
raise ValueError("at least one of data or run must be provided")
# TODO(2901096): Support pf run with python file, maybe create a temp flow.dag.yaml in this case
# load flow object for validation and early failure
flow_obj = load_flow(source=flow)
# validate param conflicts
if isinstance(flow_obj, EagerFlow):
if variant or connections:
logger.warning("variant and connections are not supported for eager flow, will be ignored")
variant, connections = None, None
run = Run(
name=name,
display_name=display_name,
tags=tags,
data=data,
column_mapping=column_mapping,
run=run,
variant=variant,
flow=Path(flow),
connections=connections,
environment_variables=environment_variables,
config=Configuration(overrides=self._config),
)
return self.runs.create_or_update(run=run, **kwargs)
def stream(self, run: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream run logs to the console.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: flow run info.
:rtype: ~promptflow.sdk.entities.Run
"""
return self.runs.stream(run, raise_on_error)
def get_details(
self, run: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False
) -> "DataFrame":
"""Get the details from the run including inputs and outputs.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param run: The run name or run object
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
return self.runs.get_details(name=run, max_results=max_results, all_results=all_results)
def get_metrics(self, run: Union[str, Run]) -> Dict[str, Any]:
"""Get run metrics.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:return: Run metrics.
:rtype: Dict[str, Any]
"""
return self.runs.get_metrics(run)
def visualize(self, runs: Union[List[str], List[Run]]) -> None:
"""Visualize run(s).
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
"""
self.runs.visualize(runs)
@property
def runs(self) -> RunOperations:
"""Run operations that can manage runs."""
return self._runs
@property
def tools(self) -> ToolOperations:
"""Tool operations that can manage tools."""
return self._tools
def _ensure_connection_provider(self) -> str:
if not self._connection_provider:
# Get a copy with config override instead of the config instance
self._connection_provider = Configuration(overrides=self._config).get_connection_provider()
logger.debug("PFClient connection provider: %s", self._connection_provider)
return self._connection_provider
@property
def connections(self) -> ConnectionOperations:
"""Connection operations that can manage connections."""
if not self._connections:
self._ensure_connection_provider()
self._connections = get_connection_operation(self._connection_provider, self._credential)
return self._connections
@property
def flows(self) -> FlowOperations:
"""Operations on the flow that can manage flows."""
return self._flows
def test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
) -> dict:
"""Test flow or node.
:param flow: path to flow directory to test
:type flow: Union[str, PathLike]
:param inputs: Input data for the flow test
:type inputs: dict
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:type variant: str
:param node: If specified it will only test this node, else it will test the flow.
:type node: str
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: dict
:return: The result of flow or node
:rtype: dict
"""
return self.flows.test(
flow=flow, inputs=inputs, variant=variant, environment_variables=environment_variables, node=node
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_run_functions.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from os import PathLike
from typing import IO, AnyStr, Union
from promptflow._sdk._load_functions import load_run
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.entities._run import Run
def _create_run(run: Run, **kwargs):
client = PFClient()
return client.runs.create_or_update(run=run, **kwargs)
def create_yaml_run(source: Union[str, PathLike, IO[AnyStr]], params_override: list = None, **kwargs):
"""Create a run from a yaml file. Should only call from CLI."""
run = load_run(source, params_override=params_override, **kwargs)
return _create_run(run=run, **kwargs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_load_functions.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from os import PathLike
from pathlib import Path
from typing import IO, AnyStr, Optional, Union
from dotenv import dotenv_values
from .._utils.logger_utils import get_cli_sdk_logger
from .._utils.yaml_utils import load_yaml
from .entities import Run
from .entities._connection import CustomConnection, _Connection
from .entities._flow import Flow
logger = get_cli_sdk_logger()
def load_common(
cls,
source: Union[str, PathLike, IO[AnyStr]],
relative_origin: str = None,
params_override: Optional[list] = None,
**kwargs,
):
"""Private function to load a yaml file to an entity object.
:param cls: The entity class type.
:type cls: type[Resource]
:param source: A source of yaml.
:type source: Union[str, PathLike, IO[AnyStr]]
:param relative_origin: The origin of to be used when deducing
the relative locations of files referenced in the parsed yaml.
Must be provided, and is assumed to be assigned by other internal
functions that call this.
:type relative_origin: str
:param params_override: _description_, defaults to None
:type params_override: list, optional
"""
if relative_origin is None:
if isinstance(source, (str, PathLike)):
relative_origin = source
else:
try:
relative_origin = source.name
except AttributeError: # input is a stream or something
relative_origin = "./"
params_override = params_override or []
yaml_dict = load_yaml(source)
logger.debug(f"Resolve cls and type with {yaml_dict}, params_override {params_override}.")
# pylint: disable=protected-access
cls, type_str = cls._resolve_cls_and_type(data=yaml_dict, params_override=params_override)
try:
return cls._load(
data=yaml_dict,
yaml_path=relative_origin,
params_override=params_override,
**kwargs,
)
except Exception as e:
raise Exception(f"Load entity error: {e}") from e
def load_flow(
source: Union[str, PathLike, IO[AnyStr]],
*,
entry: str = None,
**kwargs,
) -> Flow:
"""Load flow from YAML file.
:param source: The local yaml source of a flow. Must be a path to a local file.
If the source is a path, it will be open and read.
An exception is raised if the file does not exist.
:type source: Union[PathLike, str]
:param entry: The entry function, only works when source is a code file.
:type entry: str
:return: A Flow object
:rtype: Flow
"""
return Flow.load(source, entry=entry, **kwargs)
def load_run(
source: Union[str, PathLike, IO[AnyStr]],
params_override: Optional[list] = None,
**kwargs,
) -> Run:
"""Load run from YAML file.
:param source: The local yaml source of a run. Must be a path to a local file.
If the source is a path, it will be open and read.
An exception is raised if the file does not exist.
:type source: Union[PathLike, str]
:param params_override: Fields to overwrite on top of the yaml file.
Format is [{"field1": "value1"}, {"field2": "value2"}]
:type params_override: List[Dict]
:return: A Run object
:rtype: Run
"""
data = load_yaml(source=source)
return Run._load(data=data, yaml_path=source, params_override=params_override, **kwargs)
def load_connection(
source: Union[str, PathLike, IO[AnyStr]],
**kwargs,
):
if Path(source).name.endswith(".env"):
return _load_env_to_connection(source, **kwargs)
return load_common(_Connection, source, **kwargs)
def _load_env_to_connection(
source,
params_override: Optional[list] = None,
**kwargs,
):
source = Path(source)
name = next((_dct["name"] for _dct in params_override if "name" in _dct), None)
if not name:
raise Exception("Please specify --name when creating connection from .env.")
if not source.exists():
raise FileNotFoundError(f"File {source.absolute().as_posix()!r} not found.")
try:
data = dict(dotenv_values(source))
if not data:
# Handle some special case dotenv returns empty with no exception raised.
raise ValueError(
f"Load nothing from dotenv file {source.absolute().as_posix()!r}, "
"please make sure the file is not empty and readable."
)
return CustomConnection(name=name, secrets=data)
except Exception as e:
raise Exception(f"Load entity error: {e}") from e
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_user_agent.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._version import VERSION
USER_AGENT = "{}/{}".format("promptflow-sdk", VERSION)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_constants.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
from enum import Enum
from pathlib import Path
LOGGER_NAME = "promptflow"
PROMPT_FLOW_HOME_DIR_ENV_VAR = "PF_HOME_DIRECTORY"
PROMPT_FLOW_DIR_NAME = ".promptflow"
def _prepare_home_dir() -> Path:
"""Prepare prompt flow home directory.
User can configure it by setting environment variable: `PF_HOME_DIRECTORY`;
if not configured, or configured value is not valid, use default value: "~/.promptflow/".
"""
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
if PROMPT_FLOW_HOME_DIR_ENV_VAR in os.environ:
logger.debug(
f"environment variable {PROMPT_FLOW_HOME_DIR_ENV_VAR!r} is set, honor it preparing home directory."
)
try:
pf_home_dir = Path(os.getenv(PROMPT_FLOW_HOME_DIR_ENV_VAR)).resolve()
pf_home_dir.mkdir(parents=True, exist_ok=True)
return pf_home_dir
except Exception as e: # pylint: disable=broad-except
_warning_message = (
"Invalid configuration for prompt flow home directory: "
f"{os.getenv(PROMPT_FLOW_HOME_DIR_ENV_VAR)!r}: {str(e)!r}.\n"
'Fall back to use default value: "~/.promptflow/".'
)
logger.warning(_warning_message)
try:
logger.debug("preparing home directory with default value.")
pf_home_dir = (Path.home() / PROMPT_FLOW_DIR_NAME).resolve()
pf_home_dir.mkdir(parents=True, exist_ok=True)
return pf_home_dir
except Exception as e: # pylint: disable=broad-except
_error_message = (
f"Cannot create prompt flow home directory: {str(e)!r}.\n"
"Please check if you have proper permission to operate the directory "
f"{HOME_PROMPT_FLOW_DIR.as_posix()!r}; or configure it via "
f"environment variable {PROMPT_FLOW_HOME_DIR_ENV_VAR!r}.\n"
)
logger.error(_error_message)
raise Exception(_error_message)
HOME_PROMPT_FLOW_DIR = _prepare_home_dir()
DAG_FILE_NAME = "flow.dag.yaml"
NODE_VARIANTS = "node_variants"
VARIANTS = "variants"
NODES = "nodes"
NODE = "node"
INPUTS = "inputs"
USE_VARIANTS = "use_variants"
DEFAULT_VAR_ID = "default_variant_id"
FLOW_TOOLS_JSON = "flow.tools.json"
FLOW_TOOLS_JSON_GEN_TIMEOUT = 60
PROMPT_FLOW_RUNS_DIR_NAME = ".runs"
PROMPT_FLOW_EXP_DIR_NAME = ".exps"
SERVICE_CONFIG_FILE = "pf.yaml"
PF_SERVICE_PORT_FILE = "pfs.port"
PF_SERVICE_LOG_FILE = "pfs.log"
LOCAL_MGMT_DB_PATH = (HOME_PROMPT_FLOW_DIR / "pf.sqlite").resolve()
LOCAL_MGMT_DB_SESSION_ACQUIRE_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "pf.sqlite.lock").resolve()
SCHEMA_INFO_TABLENAME = "schema_info"
RUN_INFO_TABLENAME = "run_info"
RUN_INFO_CREATED_ON_INDEX_NAME = "idx_run_info_created_on"
CONNECTION_TABLE_NAME = "connection"
EXPERIMENT_TABLE_NAME = "experiment"
EXPERIMENT_CREATED_ON_INDEX_NAME = "idx_experiment_created_on"
BASE_PATH_CONTEXT_KEY = "base_path"
SCHEMA_KEYS_CONTEXT_CONFIG_KEY = "schema_configs_keys"
SCHEMA_KEYS_CONTEXT_SECRET_KEY = "schema_secrets_keys"
PARAMS_OVERRIDE_KEY = "params_override"
FILE_PREFIX = "file:"
KEYRING_SYSTEM = "promptflow"
KEYRING_ENCRYPTION_KEY_NAME = "encryption_key"
KEYRING_ENCRYPTION_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "encryption_key.lock").resolve()
REFRESH_CONNECTIONS_DIR_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "refresh_connections_dir.lock").resolve()
# Note: Use this only for show. Reading input should regard all '*' string as scrubbed, no matter the length.
SCRUBBED_VALUE = "******"
SCRUBBED_VALUE_NO_CHANGE = "<no-change>"
SCRUBBED_VALUE_USER_INPUT = "<user-input>"
CHAT_HISTORY = "chat_history"
WORKSPACE_LINKED_DATASTORE_NAME = "workspaceblobstore"
LINE_NUMBER = "line_number"
AZUREML_PF_RUN_PROPERTIES_LINEAGE = "azureml.promptflow.input_run_id"
AZURE_WORKSPACE_REGEX_FORMAT = (
"^azureml:[/]{1,2}subscriptions/([^/]+)/resource(groups|Groups)/([^/]+)"
"(/providers/Microsoft.MachineLearningServices)?/workspaces/([^/]+)$"
)
DEFAULT_ENCODING = "utf-8"
LOCAL_STORAGE_BATCH_SIZE = 1
LOCAL_SERVICE_PORT = 5000
BULK_RUN_ERRORS = "BulkRunErrors"
RUN_MACRO = "${run}"
VARIANT_ID_MACRO = "${variant_id}"
TIMESTAMP_MACRO = "${timestamp}"
DEFAULT_VARIANT = "variant_0"
# run visualize constants
VIS_HTML_TMPL = Path(__file__).parent / "data" / "visualize.j2"
VIS_JS_BUNDLE_FILENAME = "bulkTestDetails.min.js"
VIS_PORTAL_URL_TMPL = (
"https://ml.azure.com/prompts/flow/bulkrun/runs/outputs"
"?wsid=/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}"
"/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}&runId={names}"
)
REMOTE_URI_PREFIX = "azureml:"
REGISTRY_URI_PREFIX = "azureml://registries/"
FLOW_RESOURCE_ID_PREFIX = "azureml://locations/"
FLOW_DIRECTORY_MACRO_IN_CONFIG = "${flow_directory}"
# Tool meta info
UIONLY_HIDDEN = "uionly_hidden"
SKIP_FUNC_PARAMS = ["subscription_id", "resource_group_name", "workspace_name"]
ICON_DARK = "icon_dark"
ICON_LIGHT = "icon_light"
ICON = "icon"
TOOL_SCHEMA = Path(__file__).parent / "data" / "tool.schema.json"
class CustomStrongTypeConnectionConfigs:
PREFIX = "promptflow.connection."
TYPE = "custom_type"
MODULE = "module"
PACKAGE = "package"
PACKAGE_VERSION = "package_version"
PROMPTFLOW_TYPE_KEY = PREFIX + TYPE
PROMPTFLOW_MODULE_KEY = PREFIX + MODULE
PROMPTFLOW_PACKAGE_KEY = PREFIX + PACKAGE
PROMPTFLOW_PACKAGE_VERSION_KEY = PREFIX + PACKAGE_VERSION
@staticmethod
def is_custom_key(key):
return key not in [
CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_VERSION_KEY,
]
class RunTypes:
BATCH = "batch"
EVALUATION = "evaluation"
PAIRWISE_EVALUATE = "pairwise_evaluate"
COMMAND = "command"
class AzureRunTypes:
"""Run types for run entity from index service."""
BATCH = "azureml.promptflow.FlowRun"
EVALUATION = "azureml.promptflow.EvaluationRun"
PAIRWISE_EVALUATE = "azureml.promptflow.PairwiseEvaluationRun"
class RestRunTypes:
"""Run types for run entity from MT service."""
BATCH = "FlowRun"
EVALUATION = "EvaluationRun"
PAIRWISE_EVALUATE = "PairwiseEvaluationRun"
# run document statuses
class RunStatus(object):
# Ordered by transition order
QUEUED = "Queued"
NOT_STARTED = "NotStarted"
PREPARING = "Preparing"
PROVISIONING = "Provisioning"
STARTING = "Starting"
RUNNING = "Running"
CANCEL_REQUESTED = "CancelRequested"
CANCELED = "Canceled"
FINALIZING = "Finalizing"
COMPLETED = "Completed"
FAILED = "Failed"
UNAPPROVED = "Unapproved"
NOTRESPONDING = "NotResponding"
PAUSING = "Pausing"
PAUSED = "Paused"
@classmethod
def list(cls):
"""Return the list of supported run statuses."""
return [
cls.QUEUED,
cls.PREPARING,
cls.PROVISIONING,
cls.STARTING,
cls.RUNNING,
cls.CANCEL_REQUESTED,
cls.CANCELED,
cls.FINALIZING,
cls.COMPLETED,
cls.FAILED,
cls.NOT_STARTED,
cls.UNAPPROVED,
cls.NOTRESPONDING,
cls.PAUSING,
cls.PAUSED,
]
@classmethod
def get_running_statuses(cls):
"""Return the list of running statuses."""
return [
cls.NOT_STARTED,
cls.QUEUED,
cls.PREPARING,
cls.PROVISIONING,
cls.STARTING,
cls.RUNNING,
cls.UNAPPROVED,
cls.NOTRESPONDING,
cls.PAUSING,
cls.PAUSED,
]
@classmethod
def get_post_processing_statuses(cls):
"""Return the list of running statuses."""
return [cls.CANCEL_REQUESTED, cls.FINALIZING]
class FlowRunProperties:
FLOW_PATH = "flow_path"
OUTPUT_PATH = "output_path"
NODE_VARIANT = "node_variant"
RUN = "run"
SYSTEM_METRICS = "system_metrics"
# Experiment command node fields only
COMMAND = "command"
OUTPUTS = "outputs"
class CommonYamlFields:
"""Common yaml fields.
Common yaml fields are used to define the common fields in yaml files. It can be one of the following values: type,
name, $schema.
"""
TYPE = "type"
"""Type."""
NAME = "name"
"""Name."""
SCHEMA = "$schema"
"""Schema."""
MAX_LIST_CLI_RESULTS = 50 # general list
MAX_RUN_LIST_RESULTS = 50 # run list
MAX_SHOW_DETAILS_RESULTS = 100 # show details
class CLIListOutputFormat:
JSON = "json"
TABLE = "table"
class LocalStorageFilenames:
SNAPSHOT_FOLDER = "snapshot"
DAG = DAG_FILE_NAME
FLOW_TOOLS_JSON = FLOW_TOOLS_JSON
INPUTS = "inputs.jsonl"
OUTPUTS = "outputs.jsonl"
DETAIL = "detail.json"
METRICS = "metrics.json"
LOG = "logs.txt"
EXCEPTION = "error.json"
META = "meta.json"
class ListViewType(str, Enum):
ACTIVE_ONLY = "ActiveOnly"
ARCHIVED_ONLY = "ArchivedOnly"
ALL = "All"
def get_list_view_type(archived_only: bool, include_archived: bool) -> ListViewType:
if archived_only and include_archived:
raise Exception("Cannot provide both archived-only and include-archived.")
if include_archived:
return ListViewType.ALL
elif archived_only:
return ListViewType.ARCHIVED_ONLY
else:
return ListViewType.ACTIVE_ONLY
class RunInfoSources(str, Enum):
"""Run sources."""
LOCAL = "local"
INDEX_SERVICE = "index_service"
RUN_HISTORY = "run_history"
MT_SERVICE = "mt_service"
EXISTING_RUN = "existing_run"
class ConfigValueType(str, Enum):
STRING = "String"
SECRET = "Secret"
class ConnectionType(str, Enum):
_NOT_SET = "NotSet"
AZURE_OPEN_AI = "AzureOpenAI"
OPEN_AI = "OpenAI"
QDRANT = "Qdrant"
COGNITIVE_SEARCH = "CognitiveSearch"
SERP = "Serp"
AZURE_CONTENT_SAFETY = "AzureContentSafety"
FORM_RECOGNIZER = "FormRecognizer"
WEAVIATE = "Weaviate"
CUSTOM = "Custom"
ALL_CONNECTION_TYPES = set(
map(lambda x: f"{x.value}Connection", filter(lambda x: x != ConnectionType._NOT_SET, ConnectionType))
)
class ConnectionFields(str, Enum):
CONNECTION = "connection"
DEPLOYMENT_NAME = "deployment_name"
MODEL = "model"
SUPPORTED_CONNECTION_FIELDS = {
ConnectionFields.CONNECTION.value,
ConnectionFields.DEPLOYMENT_NAME.value,
ConnectionFields.MODEL.value,
}
class RunDataKeys:
PORTAL_URL = "portal_url"
DATA = "data"
RUN = "run"
OUTPUT = "output"
class RunHistoryKeys:
RunMetaData = "runMetadata"
HIDDEN = "hidden"
class ConnectionProvider(str, Enum):
LOCAL = "local"
AZUREML = "azureml"
class FlowType:
STANDARD = "standard"
EVALUATION = "evaluation"
CHAT = "chat"
@staticmethod
def get_all_values():
values = [value for key, value in vars(FlowType).items() if isinstance(value, str) and key.isupper()]
return values
CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE = {
FlowType.STANDARD: "default",
FlowType.EVALUATION: "evaluation",
FlowType.CHAT: "chat",
}
SERVICE_FLOW_TYPE_2_CLIENT_FLOW_TYPE = {value: key for key, value in CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE.items()}
class AzureFlowSource:
LOCAL = "local"
PF_SERVICE = "pf_service"
INDEX = "index"
class DownloadedRun:
SNAPSHOT_FOLDER = LocalStorageFilenames.SNAPSHOT_FOLDER
METRICS_FILE_NAME = LocalStorageFilenames.METRICS
LOGS_FILE_NAME = LocalStorageFilenames.LOG
RUN_METADATA_FILE_NAME = "run_metadata.json"
class ExperimentNodeType(object):
FLOW = "flow"
COMMAND = "command"
class ExperimentStatus(object):
NOT_STARTED = "NotStarted"
IN_PROGRESS = "InProgress"
TERMINATED = "Terminated"
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_run_inputs.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from os import PathLike
from typing import Union
# TODO(2528165): remove this file when we deprecate Flow.run_bulk
class BaseInputs(object):
def __init__(self, data: Union[str, PathLike], inputs_mapping: dict = None, **kwargs):
self.data = data
self.inputs_mapping = inputs_mapping
class BulkInputs(BaseInputs):
"""Bulk run inputs.
data: pointer to test data for standard runs
inputs_mapping: define a data flow logic to map input data, support:
from data: data.col1:
Example:
{"question": "${data.question}", "context": "${data.context}"}
"""
# TODO: support inputs_mapping for bulk run
pass
class EvalInputs(BaseInputs):
"""Evaluation flow run inputs.
data: pointer to test data (of variant bulk runs) for eval runs
variant:
variant run id or variant run
keep lineage between current run and variant runs
variant outputs can be referenced as ${batch_run.outputs.col_name} in inputs_mapping
baseline:
baseline run id or baseline run
baseline bulk run for eval runs for pairwise comparison
inputs_mapping: define a data flow logic to map input data, support:
from data: data.col1:
from variant:
[0].col1, [1].col2: if need different col from variant run data
variant.output.col1: if all upstream runs has col1
Example:
{"ground_truth": "${data.answer}", "prediction": "${batch_run.outputs.answer}"}
"""
def __init__(
self,
data: Union[str, PathLike],
variant: Union[str, "BulkRun"] = None, # noqa: F821
baseline: Union[str, "BulkRun"] = None, # noqa: F821
inputs_mapping: dict = None,
**kwargs
):
super().__init__(data=data, inputs_mapping=inputs_mapping, **kwargs)
self.variant = variant
self.baseline = baseline
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_experiment.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import datetime
import json
import shutil
import uuid
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from marshmallow import Schema
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
PARAMS_OVERRIDE_KEY,
PROMPT_FLOW_DIR_NAME,
PROMPT_FLOW_EXP_DIR_NAME,
ExperimentNodeType,
ExperimentStatus,
)
from promptflow._sdk._errors import ExperimentValidationError, ExperimentValueError
from promptflow._sdk._orm.experiment import Experiment as ORMExperiment
from promptflow._sdk._submitter import remove_additional_includes
from promptflow._sdk._utils import _merge_local_code_and_additional_includes, _sanitize_python_variable_name
from promptflow._sdk.entities import Run
from promptflow._sdk.entities._validation import MutableValidationResult, SchemaValidatableMixin
from promptflow._sdk.entities._yaml_translatable import YAMLTranslatableMixin
from promptflow._sdk.schemas._experiment import (
CommandNodeSchema,
ExperimentDataSchema,
ExperimentInputSchema,
ExperimentSchema,
ExperimentTemplateSchema,
FlowNodeSchema,
)
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.contracts.tool import ValueType
logger = get_cli_sdk_logger()
class ExperimentData(YAMLTranslatableMixin):
def __init__(self, name, path, **kwargs):
self.name = name
self.path = path
@classmethod
def _get_schema_cls(cls):
return ExperimentDataSchema
class ExperimentInput(YAMLTranslatableMixin):
def __init__(self, name, default, type, **kwargs):
self.name = name
self.type, self.default = self._resolve_type_and_default(type, default)
@classmethod
def _get_schema_cls(cls):
return ExperimentInputSchema
def _resolve_type_and_default(self, typ, default):
supported_types = [
ValueType.INT,
ValueType.STRING,
ValueType.DOUBLE,
ValueType.LIST,
ValueType.OBJECT,
ValueType.BOOL,
]
value_type: ValueType = next((i for i in supported_types if typ.lower() == i.value.lower()), None)
if value_type is None:
raise ExperimentValueError(f"Unknown experiment input type {typ!r}, supported are {supported_types}.")
return value_type.value, value_type.parse(default) if default is not None else None
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
# Override this to avoid 'type' got pop out
schema_cls = cls._get_schema_cls()
try:
loaded_data = schema_cls(context=context).load(data, **kwargs)
except Exception as e:
raise Exception(f"Load experiment input failed with {str(e)}. f{(additional_message or '')}.")
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
class FlowNode(YAMLTranslatableMixin):
def __init__(
self,
path: Union[Path, str],
name: str,
# input fields are optional since it's not stored in DB
data: Optional[str] = None,
variant: Optional[str] = None,
run: Optional[Union["Run", str]] = None,
inputs: Optional[dict] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[List[Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict]] = None,
properties: Optional[Dict[str, Any]] = None,
**kwargs,
):
self.type = ExperimentNodeType.FLOW
self.data = data
self.inputs = inputs
self.display_name = display_name
self.description = description
self.tags = tags
self.variant = variant
self.run = run
self.environment_variables = environment_variables or {}
self.connections = connections or {}
self._properties = properties or {}
# init here to make sure those fields initialized in all branches.
self.path = path
# default run name: flow directory name + timestamp
self.name = name
self._runtime = kwargs.get("runtime", None)
self._resources = kwargs.get("resources", None)
@classmethod
def _get_schema_cls(cls):
return FlowNodeSchema
def _save_snapshot(self, target):
"""Save flow source to experiment snapshot."""
# Resolve additional includes in flow
from promptflow import load_flow
Path(target).mkdir(parents=True, exist_ok=True)
flow = load_flow(source=self.path)
saved_flow_path = Path(target) / self.name
with _merge_local_code_and_additional_includes(code_path=flow.code) as resolved_flow_dir:
remove_additional_includes(Path(resolved_flow_dir))
shutil.copytree(src=resolved_flow_dir, dst=saved_flow_path)
logger.debug(f"Flow source saved to {saved_flow_path}.")
self.path = saved_flow_path.resolve().absolute().as_posix()
class CommandNode(YAMLTranslatableMixin):
def __init__(
self,
command,
name,
inputs=None,
outputs=None,
runtime=None,
environment_variables=None,
code=None,
display_name=None,
**kwargs,
):
self.type = ExperimentNodeType.COMMAND
self.name = name
self.display_name = display_name
self.code = code
self.command = command
self.inputs = inputs or {}
self.outputs = outputs or {}
self.runtime = runtime
self.environment_variables = environment_variables or {}
@classmethod
def _get_schema_cls(cls):
return CommandNodeSchema
def _save_snapshot(self, target):
"""Save command source to experiment snapshot."""
Path(target).mkdir(parents=True, exist_ok=True)
saved_path = Path(target) / self.name
if not self.code:
# Create an empty folder
saved_path.mkdir(parents=True, exist_ok=True)
self.code = saved_path.resolve().absolute().as_posix()
return
code = Path(self.code)
if not code.exists():
raise ExperimentValueError(f"Command node code {code} does not exist.")
if code.is_dir():
shutil.copytree(src=self.code, dst=saved_path)
else:
saved_path.mkdir(parents=True, exist_ok=True)
shutil.copy(src=self.code, dst=saved_path)
logger.debug(f"Command node source saved to {saved_path}.")
self.code = saved_path.resolve().absolute().as_posix()
class ExperimentTemplate(YAMLTranslatableMixin, SchemaValidatableMixin):
def __init__(self, nodes, name=None, description=None, data=None, inputs=None, **kwargs):
self._base_path = kwargs.get(BASE_PATH_CONTEXT_KEY, Path("."))
self.name = name or self._generate_name()
self.description = description
self.nodes = nodes
self.data = data or []
self.inputs = inputs or []
self._source_path = None
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, **kwargs):
return cls, "experiment_template"
@classmethod
def _get_schema_cls(cls):
return ExperimentTemplateSchema
@classmethod
def _load(
cls,
data: Optional[Dict] = None,
yaml_path: Optional[Union[PathLike, str]] = None,
params_override: Optional[list] = None,
**kwargs,
):
data = data or {}
params_override = params_override or []
context = {
BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
PARAMS_OVERRIDE_KEY: params_override,
}
logger.debug(f"Loading class object with data {data}, params_override {params_override}, context {context}.")
exp = cls._load_from_dict(
data=data,
context=context,
additional_message="Failed to load experiment",
**kwargs,
)
if yaml_path:
exp._source_path = yaml_path
return exp
def _generate_name(self) -> str:
"""Generate a template name."""
try:
folder_name = Path(self._base_path).resolve().absolute().name
return _sanitize_python_variable_name(folder_name)
except Exception as e:
logger.debug(f"Failed to generate template name, error: {e}, use uuid.")
return str(uuid.uuid4())
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
schema_cls = cls._get_schema_cls()
try:
loaded_data = schema_cls(context=context).load(data, **kwargs)
except Exception as e:
raise Exception(f"Load experiment template failed with {str(e)}. f{(additional_message or '')}.")
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
@classmethod
def _create_schema_for_validation(cls, context) -> Schema:
return cls._get_schema_cls()(context=context)
def _default_context(self) -> dict:
return {BASE_PATH_CONTEXT_KEY: self._base_path}
@classmethod
def _create_validation_error(cls, message: str, no_personal_data_message: str) -> Exception:
return ExperimentValidationError(
message=message,
no_personal_data_message=no_personal_data_message,
)
def _customized_validate(self) -> MutableValidationResult:
"""Validate the resource with customized logic.
Override this method to add customized validation logic.
:return: The customized validation result
:rtype: MutableValidationResult
"""
pass
class Experiment(ExperimentTemplate):
def __init__(
self,
nodes,
name=None,
data=None,
inputs=None,
status=ExperimentStatus.NOT_STARTED,
node_runs=None,
properties=None,
**kwargs,
):
self.name = name or self._generate_name()
self.status = status
self.node_runs = node_runs or {}
self.properties = properties or {}
self.created_on = kwargs.get("created_on", datetime.datetime.now().isoformat())
self.last_start_time = kwargs.get("last_start_time", None)
self.last_end_time = kwargs.get("last_end_time", None)
self.is_archived = kwargs.get("is_archived", False)
self._output_dir = Path.home() / PROMPT_FLOW_DIR_NAME / PROMPT_FLOW_EXP_DIR_NAME / self.name
super().__init__(nodes, name=self.name, data=data, inputs=inputs, **kwargs)
@classmethod
def _get_schema_cls(cls):
return ExperimentSchema
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, **kwargs):
return cls, "experiment"
def _generate_name(self) -> str:
"""Generate a experiment name."""
try:
folder_name = Path(self._base_path).resolve().absolute().name
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
exp_name = f"{folder_name}_{timestamp}"
return _sanitize_python_variable_name(exp_name)
except Exception as e:
logger.debug(f"Failed to generate experiment name, error: {e}, use uuid.")
return str(uuid.uuid4())
def _save_snapshot_and_update_node(
self,
):
"""Save node source to experiment snapshot, update node path."""
snapshot_dir = self._output_dir / "snapshots"
for node in self.nodes:
node._save_snapshot(snapshot_dir)
def _append_node_run(self, node_name, run: Run):
"""Append node run to experiment."""
if node_name not in self.node_runs or not isinstance(self.node_runs[node_name], list):
self.node_runs[node_name] = []
# TODO: Review this
self.node_runs[node_name].append({"name": run.name, "status": run.status})
def _to_orm_object(self):
"""Convert to ORM object."""
result = ORMExperiment(
name=self.name,
description=self.description,
status=self.status,
created_on=self.created_on,
archived=self.is_archived,
last_start_time=self.last_start_time,
last_end_time=self.last_end_time,
properties=json.dumps(self.properties),
data=json.dumps([item._to_dict() for item in self.data]),
inputs=json.dumps([input._to_dict() for input in self.inputs]),
nodes=json.dumps([node._to_dict() for node in self.nodes]),
node_runs=json.dumps(self.node_runs),
)
logger.debug(f"Experiment to ORM object: {result.__dict__}")
return result
@classmethod
def _from_orm_object(cls, obj: ORMExperiment) -> "Experiment":
"""Create a experiment object from ORM object."""
nodes = []
context = {BASE_PATH_CONTEXT_KEY: "./"}
for node_dict in json.loads(obj.nodes):
if node_dict["type"] == ExperimentNodeType.FLOW:
nodes.append(
FlowNode._load_from_dict(node_dict, context=context, additional_message="Failed to load node.")
)
elif node_dict["type"] == ExperimentNodeType.COMMAND:
nodes.append(
CommandNode._load_from_dict(node_dict, context=context, additional_message="Failed to load node.")
)
else:
raise Exception(f"Unknown node type {node_dict['type']}")
data = [
ExperimentData._load_from_dict(item, context=context, additional_message="Failed to load experiment data")
for item in json.loads(obj.data)
]
inputs = [
ExperimentInput._load_from_dict(
item, context=context, additional_message="Failed to load experiment inputs"
)
for item in json.loads(obj.inputs)
]
return cls(
name=obj.name,
description=obj.description,
status=obj.status,
created_on=obj.created_on,
last_start_time=obj.last_start_time,
last_end_time=obj.last_end_time,
is_archived=obj.archived,
properties=json.loads(obj.properties),
data=data,
inputs=inputs,
nodes=nodes,
node_runs=json.loads(obj.node_runs),
)
@classmethod
def from_template(cls, template: ExperimentTemplate, name=None):
"""Create a experiment object from template."""
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
exp_name = name or f"{template.name}_{timestamp}"
experiment = cls(
name=exp_name,
description=template.description,
data=copy.deepcopy(template.data),
inputs=copy.deepcopy(template.inputs),
nodes=copy.deepcopy(template.nodes),
base_path=template._base_path,
)
logger.debug("Start saving snapshot and update node.")
experiment._save_snapshot_and_update_node()
return experiment
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_run.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import datetime
import functools
import json
import uuid
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from dateutil import parser as date_parser
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
DEFAULT_ENCODING,
DEFAULT_VARIANT,
FLOW_DIRECTORY_MACRO_IN_CONFIG,
FLOW_RESOURCE_ID_PREFIX,
PARAMS_OVERRIDE_KEY,
PROMPT_FLOW_DIR_NAME,
REGISTRY_URI_PREFIX,
REMOTE_URI_PREFIX,
RUN_MACRO,
TIMESTAMP_MACRO,
VARIANT_ID_MACRO,
AzureRunTypes,
DownloadedRun,
FlowRunProperties,
RestRunTypes,
RunDataKeys,
RunInfoSources,
RunStatus,
RunTypes,
)
from promptflow._sdk._errors import InvalidRunError, InvalidRunStatusError
from promptflow._sdk._orm import RunInfo as ORMRun
from promptflow._sdk._utils import (
_sanitize_python_variable_name,
is_remote_uri,
parse_remote_flow_pattern,
parse_variant,
)
from promptflow._sdk.entities._yaml_translatable import YAMLTranslatableMixin
from promptflow._sdk.schemas._run import RunSchema
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.exceptions import UserErrorException
AZURE_RUN_TYPE_2_RUN_TYPE = {
AzureRunTypes.BATCH: RunTypes.BATCH,
AzureRunTypes.EVALUATION: RunTypes.EVALUATION,
AzureRunTypes.PAIRWISE_EVALUATE: RunTypes.PAIRWISE_EVALUATE,
}
REST_RUN_TYPE_2_RUN_TYPE = {
RestRunTypes.BATCH: RunTypes.BATCH,
RestRunTypes.EVALUATION: RunTypes.EVALUATION,
RestRunTypes.PAIRWISE_EVALUATE: RunTypes.PAIRWISE_EVALUATE,
}
logger = get_cli_sdk_logger()
class Run(YAMLTranslatableMixin):
"""Flow run entity.
:param flow: Path of the flow directory.
:type flow: Path
:param name: Name of the run.
:type name: str
:param data: Input data for the run. Local path or remote uri(starts with azureml: or public URL) are supported. Note: remote uri is only supported for cloud run. # noqa: E501
:type data: Optional[str]
:param variant: Variant of the run.
:type variant: Optional[str]
:param run: Parent run or run ID.
:type run: Optional[Union[Run, str]]
:param column_mapping: Column mapping for the run. Optional since it's not stored in the database.
:type column_mapping: Optional[dict]
:param display_name: Display name of the run.
:type display_name: Optional[str]
:param description: Description of the run.
:type description: Optional[str]
:param tags: Tags of the run.
:type tags: Optional[List[Dict[str, str]]]
:param created_on: Date and time the run was created.
:type created_on: Optional[datetime.datetime]
:param start_time: Date and time the run started.
:type start_time: Optional[datetime.datetime]
:param end_time: Date and time the run ended.
:type end_time: Optional[datetime.datetime]
:param status: Status of the run.
:type status: Optional[str]
:param environment_variables: Environment variables for the run.
:type environment_variables: Optional[Dict[str, str]]
:param connections: Connections for the run.
:type connections: Optional[Dict[str, Dict]]
:param properties: Properties of the run.
:type properties: Optional[Dict[str, Any]]
:param kwargs: Additional keyword arguments.
:type kwargs: Optional[dict]
"""
def __init__(
self,
flow: Optional[Union[Path, str]] = None,
name: Optional[str] = None,
# input fields are optional since it's not stored in DB
data: Optional[str] = None,
variant: Optional[str] = None,
run: Optional[Union["Run", str]] = None,
column_mapping: Optional[dict] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[List[Dict[str, str]]] = None,
*,
created_on: Optional[datetime.datetime] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
status: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict]] = None,
properties: Optional[Dict[str, Any]] = None,
source: Optional[Union[Path, str]] = None,
**kwargs,
):
# TODO: remove when RUN CRUD don't depend on this
self.type = kwargs.get("type", RunTypes.BATCH)
self.data = data
self.column_mapping = column_mapping
self.display_name = display_name
self.description = description
self.tags = tags
self.variant = variant
self.run = run
self._created_on = created_on or datetime.datetime.now()
self._status = status or RunStatus.NOT_STARTED
self.environment_variables = environment_variables or {}
self.connections = connections or {}
self._properties = properties or {}
self.source = source
self._is_archived = kwargs.get("is_archived", False)
self._run_source = kwargs.get("run_source", RunInfoSources.LOCAL)
self._start_time = start_time
self._end_time = end_time
self._duration = kwargs.get("duration", None)
self._portal_url = kwargs.get(RunDataKeys.PORTAL_URL, None)
self._creation_context = kwargs.get("creation_context", None)
# init here to make sure those fields initialized in all branches.
self.flow = flow
self._use_remote_flow = is_remote_uri(flow)
self._experiment_name = None
self._lineage_id = None
if self._use_remote_flow:
self._flow_name = parse_remote_flow_pattern(flow)
self._lineage_id = self._flow_name
# default run name: flow directory name + timestamp
self.name = name or self._generate_run_name()
experiment_name = kwargs.get("experiment_name", None)
if self._run_source == RunInfoSources.LOCAL and not self._use_remote_flow:
self.flow = Path(flow).resolve().absolute()
flow_dir = self._get_flow_dir()
# sanitize flow_dir to avoid invalid experiment name
self._experiment_name = _sanitize_python_variable_name(flow_dir.name)
self._lineage_id = get_flow_lineage_id(flow_dir=flow_dir)
self._output_path = Path(
kwargs.get("output_path", self._generate_output_path(config=kwargs.get("config", None)))
)
self._flow_name = flow_dir.name
elif self._run_source == RunInfoSources.INDEX_SERVICE:
self._metrics = kwargs.get("metrics", {})
self._experiment_name = experiment_name
elif self._run_source == RunInfoSources.RUN_HISTORY:
self._error = kwargs.get("error", None)
self._output = kwargs.get("output", None)
elif self._run_source == RunInfoSources.EXISTING_RUN:
# when the run is created from an existing run folder, the output path is also the source path
self._output_path = Path(source)
self._runtime = kwargs.get("runtime", None)
self._resources = kwargs.get("resources", None)
self._outputs = kwargs.get("outputs", None)
self._command = kwargs.get("command", None)
@property
def created_on(self) -> str:
return self._created_on.isoformat()
@property
def status(self) -> str:
return self._status
@property
def properties(self) -> Dict[str, str]:
result = {}
if self._run_source == RunInfoSources.LOCAL:
# show posix path to avoid windows path escaping
result = {
FlowRunProperties.FLOW_PATH: Path(self.flow).as_posix() if not self._use_remote_flow else self.flow,
FlowRunProperties.OUTPUT_PATH: self._output_path.as_posix(),
}
if self.run:
run_name = self.run.name if isinstance(self.run, Run) else self.run
result[FlowRunProperties.RUN] = run_name
if self.variant:
result[FlowRunProperties.NODE_VARIANT] = self.variant
if self._command:
result[FlowRunProperties.COMMAND] = self._command
if self._outputs:
result[FlowRunProperties.OUTPUTS] = self._outputs
elif self._run_source == RunInfoSources.EXISTING_RUN:
result = {
FlowRunProperties.OUTPUT_PATH: Path(self.source).resolve().as_posix(),
}
return {
**result,
**self._properties,
}
@classmethod
def _from_orm_object(cls, obj: ORMRun) -> "Run":
properties_json = json.loads(str(obj.properties))
flow = properties_json.get(FlowRunProperties.FLOW_PATH, None)
# there can be two sources for orm run object:
# 1. LOCAL: Created when run is created from local flow
# 2. EXISTING_RUN: Created when run is created from existing run folder
source = None
if getattr(obj, "run_source", None) == RunInfoSources.EXISTING_RUN:
source = properties_json[FlowRunProperties.OUTPUT_PATH]
return Run(
name=str(obj.name),
flow=Path(flow) if flow else None,
source=Path(source) if source else None,
output_path=properties_json[FlowRunProperties.OUTPUT_PATH],
run=properties_json.get(FlowRunProperties.RUN, None),
variant=properties_json.get(FlowRunProperties.NODE_VARIANT, None),
display_name=obj.display_name,
description=str(obj.description) if obj.description else None,
tags=json.loads(str(obj.tags)) if obj.tags else None,
# keyword arguments
created_on=datetime.datetime.fromisoformat(str(obj.created_on)),
start_time=datetime.datetime.fromisoformat(str(obj.start_time)) if obj.start_time else None,
end_time=datetime.datetime.fromisoformat(str(obj.end_time)) if obj.end_time else None,
status=str(obj.status),
data=Path(obj.data).resolve().absolute().as_posix() if obj.data else None,
properties={FlowRunProperties.SYSTEM_METRICS: properties_json.get(FlowRunProperties.SYSTEM_METRICS, {})},
# compatible with old runs, their run_source is empty, treat them as local
run_source=obj.run_source or RunInfoSources.LOCAL,
# experiment command node only fields
command=properties_json.get(FlowRunProperties.COMMAND, None),
outputs=properties_json.get(FlowRunProperties.OUTPUTS, None),
)
@classmethod
def _from_index_service_entity(cls, run_entity: dict) -> "Run":
"""Convert run entity from index service to run object."""
# TODO(2887134): support cloud eager Run CRUD
start_time = run_entity["properties"].get("startTime", None)
end_time = run_entity["properties"].get("endTime", None)
duration = run_entity["properties"].get("duration", None)
return Run(
name=run_entity["properties"]["runId"],
flow=Path(f"azureml://flows/{run_entity['properties']['experimentName']}"),
type=AZURE_RUN_TYPE_2_RUN_TYPE[run_entity["properties"]["runType"]],
created_on=date_parser.parse(run_entity["properties"]["creationContext"]["createdTime"]),
status=run_entity["annotations"]["status"],
display_name=run_entity["annotations"]["displayName"],
description=run_entity["annotations"]["description"],
tags=run_entity["annotations"]["tags"],
properties=run_entity["properties"]["userProperties"],
is_archived=run_entity["annotations"]["archived"],
run_source=RunInfoSources.INDEX_SERVICE,
metrics=run_entity["annotations"]["metrics"],
start_time=date_parser.parse(start_time) if start_time else None,
end_time=date_parser.parse(end_time) if end_time else None,
duration=duration,
creation_context=run_entity["properties"]["creationContext"],
experiment_name=run_entity["properties"]["experimentName"],
)
@classmethod
def _from_run_history_entity(cls, run_entity: dict) -> "Run":
"""Convert run entity from run history service to run object."""
# TODO(2887134): support cloud eager Run CRUD
flow_name = run_entity["properties"].get("azureml.promptflow.flow_name", None)
start_time = run_entity.get("startTimeUtc", None)
end_time = run_entity.get("endTimeUtc", None)
duration = run_entity.get("duration", None)
return Run(
name=run_entity["runId"],
flow=Path(f"azureml://flows/{flow_name}"),
type=AZURE_RUN_TYPE_2_RUN_TYPE[run_entity["runType"]],
created_on=date_parser.parse(run_entity["createdUtc"]),
start_time=date_parser.parse(start_time) if start_time else None,
end_time=date_parser.parse(end_time) if end_time else None,
duration=duration,
status=run_entity["status"],
display_name=run_entity["displayName"],
description=run_entity["description"],
tags=run_entity["tags"],
properties=run_entity["properties"],
is_archived=run_entity.get("archived", False), # TODO: Get archived status, depends on run history team
error=run_entity.get("error", None),
run_source=RunInfoSources.RUN_HISTORY,
portal_url=run_entity[RunDataKeys.PORTAL_URL],
creation_context=run_entity["createdBy"],
data=run_entity[RunDataKeys.DATA],
run=run_entity[RunDataKeys.RUN],
output=run_entity[RunDataKeys.OUTPUT],
)
@classmethod
def _from_mt_service_entity(cls, run_entity) -> "Run":
"""Convert run object from MT service to run object."""
flow_run_id = run_entity.flow_run_resource_id.split("/")[-1]
return cls(
name=flow_run_id,
flow=Path(f"azureml://flows/{run_entity.flow_name}"),
display_name=run_entity.flow_run_display_name,
description="",
tags=[],
created_on=date_parser.parse(run_entity.created_on),
status="",
run_source=RunInfoSources.MT_SERVICE,
)
def _to_orm_object(self) -> ORMRun:
"""Convert current run entity to ORM object."""
display_name = self._format_display_name()
return ORMRun(
name=self.name,
created_on=self.created_on,
status=self.status,
start_time=self._start_time.isoformat() if self._start_time else None,
end_time=self._end_time.isoformat() if self._end_time else None,
display_name=display_name,
description=self.description,
tags=json.dumps(self.tags) if self.tags else None,
properties=json.dumps(self.properties),
data=Path(self.data).resolve().absolute().as_posix() if self.data else None,
run_source=self._run_source,
)
def _dump(self) -> None:
"""Dump current run entity to local DB."""
self._to_orm_object().dump()
def _to_dict(
self,
*,
exclude_additional_info: bool = False,
exclude_debug_info: bool = False,
exclude_properties: bool = False,
):
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
properties = self.properties
result = {
"name": self.name,
"created_on": self.created_on,
"status": self.status,
"display_name": self.display_name,
"description": self.description,
"tags": self.tags,
"properties": properties,
}
if self._run_source == RunInfoSources.LOCAL:
result["flow_name"] = self._flow_name
local_storage = LocalStorageOperations(run=self)
result[RunDataKeys.DATA] = (
local_storage._data_path.resolve().absolute().as_posix()
if local_storage._data_path is not None
else None
)
result[RunDataKeys.OUTPUT] = local_storage.outputs_folder.as_posix()
if self.run:
run_name = self.run.name if isinstance(self.run, Run) else self.run
result[RunDataKeys.RUN] = properties.pop(FlowRunProperties.RUN, run_name)
# add exception part if any
exception_dict = local_storage.load_exception()
if exception_dict:
if exclude_additional_info:
exception_dict.pop("additionalInfo", None)
if exclude_debug_info:
exception_dict.pop("debugInfo", None)
result["error"] = exception_dict
elif self._run_source == RunInfoSources.INDEX_SERVICE:
result["creation_context"] = self._creation_context
result["flow_name"] = self._experiment_name
result["is_archived"] = self._is_archived
result["start_time"] = self._start_time.isoformat() if self._start_time else None
result["end_time"] = self._end_time.isoformat() if self._end_time else None
result["duration"] = self._duration
elif self._run_source == RunInfoSources.RUN_HISTORY:
result["creation_context"] = self._creation_context
result["start_time"] = self._start_time.isoformat() if self._start_time else None
result["end_time"] = self._end_time.isoformat() if self._end_time else None
result["duration"] = self._duration
result[RunDataKeys.PORTAL_URL] = self._portal_url
result[RunDataKeys.DATA] = self.data
result[RunDataKeys.OUTPUT] = self._output
if self.run:
result[RunDataKeys.RUN] = self.run
if self._error:
result["error"] = self._error
if exclude_additional_info:
result["error"]["error"].pop("additionalInfo", None)
if exclude_debug_info:
result["error"]["error"].pop("debugInfo", None)
# hide properties when needed (e.g. list remote runs)
if exclude_properties is True:
result.pop("properties", None)
return result
@classmethod
def _load(
cls,
data: Optional[Dict] = None,
yaml_path: Optional[Union[PathLike, str]] = None,
params_override: Optional[list] = None,
**kwargs,
):
from marshmallow import INCLUDE
data = data or {}
params_override = params_override or []
context = {
BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
PARAMS_OVERRIDE_KEY: params_override,
}
run = cls._load_from_dict(
data=data,
context=context,
additional_message="Failed to load flow run",
unknown=INCLUDE,
**kwargs,
)
if yaml_path:
run._source_path = yaml_path
return run
def _generate_run_name(self) -> str:
"""Generate a run name with flow_name_variant_timestamp format."""
try:
flow_name = self._get_flow_dir().name if not self._use_remote_flow else self._flow_name
variant = self.variant
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
variant = parse_variant(variant)[1] if variant else DEFAULT_VARIANT
run_name_prefix = f"{flow_name}_{variant}"
# TODO(2562996): limit run name to avoid it become too long
run_name = f"{run_name_prefix}_{timestamp}"
return _sanitize_python_variable_name(run_name)
except Exception:
return str(uuid.uuid4())
def _get_default_display_name(self) -> str:
display_name = self.display_name or self.name
return display_name
def _format_display_name(self) -> str:
"""
Format display name. Replace macros in display name with actual values.
The following macros are supported: ${variant_id}, ${run}, ${timestamp}
For example,
if the display name is "run-${variant_id}-${timestamp}"
it will be formatted to "run-variant_1-20210901123456"
"""
display_name = self._get_default_display_name()
time_stamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
if self.run:
display_name = display_name.replace(RUN_MACRO, self._validate_and_return_run_name(self.run))
display_name = display_name.replace(TIMESTAMP_MACRO, time_stamp)
variant = self.variant
variant = parse_variant(variant)[1] if variant else DEFAULT_VARIANT
display_name = display_name.replace(VARIANT_ID_MACRO, variant)
return display_name
def _get_flow_dir(self) -> Path:
if not self._use_remote_flow:
flow = Path(self.flow)
if flow.is_dir():
return flow
return flow.parent
raise UserErrorException("Cannot get flow directory for remote flow.")
@classmethod
def _get_schema_cls(self):
return RunSchema
def _to_rest_object(self):
from azure.ai.ml._utils._storage_utils import AzureMLDatastorePathUri
from promptflow.azure._restclient.flow.models import (
BatchDataInput,
RunDisplayNameGenerationType,
SessionSetupModeEnum,
SubmitBulkRunRequest,
)
if self.run is not None:
if isinstance(self.run, Run):
variant = self.run.name
elif isinstance(self.run, str):
variant = self.run
else:
raise UserErrorException(f"Invalid run type: {type(self.run)}")
else:
variant = None
if not variant and not self.data:
raise UserErrorException("Either run or data should be provided")
# parse inputs mapping
inputs_mapping = {}
if self.column_mapping and not isinstance(self.column_mapping, dict):
raise UserErrorException(f"column_mapping should be a dictionary, got {type(self.column_mapping)} instead.")
if self.column_mapping:
for k, v in self.column_mapping.items():
if isinstance(v, (int, float, str, bool)):
inputs_mapping[k] = v
else:
try:
val = json.dumps(v)
except Exception as e:
raise UserErrorException(
f"Invalid input mapping value: {v}, "
f"only primitive or json serializable value is supported, got {type(v)}",
error=e,
)
inputs_mapping[k] = val
# parse resources
if self._resources is not None:
if not isinstance(self._resources, dict):
raise TypeError(f"resources should be a dict, got {type(self._resources)} for {self._resources}")
vm_size = self._resources.get("instance_type", None)
max_idle_time_minutes = self._resources.get("idle_time_before_shutdown_minutes", None)
# change to seconds
max_idle_time_seconds = max_idle_time_minutes * 60 if max_idle_time_minutes else None
else:
vm_size = None
max_idle_time_seconds = None
# use functools.partial to avoid too many arguments that have the same values
common_submit_bulk_run_request = functools.partial(
SubmitBulkRunRequest,
run_id=self.name,
# will use user provided display name since PFS will have special logic to update it.
run_display_name=self._get_default_display_name(),
description=self.description,
tags=self.tags,
node_variant=self.variant,
variant_run_id=variant,
batch_data_input=BatchDataInput(
data_uri=self.data,
),
inputs_mapping=inputs_mapping,
run_experiment_name=self._experiment_name,
environment_variables=self.environment_variables,
connections=self.connections,
flow_lineage_id=self._lineage_id,
run_display_name_generation_type=RunDisplayNameGenerationType.USER_PROVIDED_MACRO,
vm_size=vm_size,
max_idle_time_seconds=max_idle_time_seconds,
session_setup_mode=SessionSetupModeEnum.SYSTEM_WAIT,
)
if str(self.flow).startswith(REMOTE_URI_PREFIX):
if not self._use_remote_flow:
# in normal case, we will upload local flow to datastore and resolve the self.flow to be remote uri
# upload via _check_and_upload_path
# submit with params FlowDefinitionDataStoreName and FlowDefinitionBlobPath
path_uri = AzureMLDatastorePathUri(str(self.flow))
return common_submit_bulk_run_request(
flow_definition_data_store_name=path_uri.datastore,
flow_definition_blob_path=path_uri.path,
)
else:
# if the flow is a remote flow in the beginning, we will submit with params FlowDefinitionResourceID
# submit with params flow_definition_resource_id which will be resolved in pfazure run create operation
# the flow resource id looks like: "azureml://locations/<region>/workspaces/<ws-name>/flows/<flow-name>"
if not isinstance(self.flow, str) or (
not self.flow.startswith(FLOW_RESOURCE_ID_PREFIX) and not self.flow.startswith(REGISTRY_URI_PREFIX)
):
raise UserErrorException(
f"Invalid flow value when transforming to rest object: {self.flow!r}. "
f"Expecting a flow definition resource id starts with '{FLOW_RESOURCE_ID_PREFIX}' "
f"or a flow registry uri starts with '{REGISTRY_URI_PREFIX}'"
)
return common_submit_bulk_run_request(
flow_definition_resource_id=self.flow,
)
else:
# upload via CodeOperations.create_or_update
# submit with param FlowDefinitionDataUri
return common_submit_bulk_run_request(
flow_definition_data_uri=str(self.flow),
)
def _check_run_status_is_completed(self) -> None:
if self.status != RunStatus.COMPLETED:
error_message = f"Run {self.name!r} is not completed, the status is {self.status!r}."
if self.status != RunStatus.FAILED:
error_message += " Please wait for its completion, or select other completed run(s)."
raise InvalidRunStatusError(error_message)
@staticmethod
def _validate_and_return_run_name(run: Union[str, "Run"]) -> str:
"""Check if run name is valid."""
if isinstance(run, Run):
return run.name
elif isinstance(run, str):
return run
raise InvalidRunError(f"Invalid run {run!r}, expected 'str' or 'Run' object but got {type(run)!r}.")
def _validate_for_run_create_operation(self):
"""Validate run object for create operation."""
# check flow value
if Path(self.flow).is_dir():
# local flow
pass
elif isinstance(self.flow, str) and self.flow.startswith(REMOTE_URI_PREFIX):
# remote flow
pass
else:
raise UserErrorException(
f"Invalid flow value: {self.flow!r}. Expecting a local flow folder path or a remote flow pattern "
f"like '{REMOTE_URI_PREFIX}<flow-name>'"
)
if is_remote_uri(self.data):
# Pass through ARM id or remote url, the error will happen in runtime if format is not correct currently.
pass
else:
if self.data and not Path(self.data).exists():
raise UserErrorException(f"data path {self.data} does not exist")
if not self.run and not self.data:
raise UserErrorException("at least one of data or run must be provided")
def _generate_output_path(self, config: Optional[Configuration]) -> Path:
config = config or Configuration.get_instance()
path = config.get_run_output_path()
if path is None:
path = Path.home() / PROMPT_FLOW_DIR_NAME / ".runs"
else:
try:
flow_posix_path = self.flow.resolve().as_posix()
path = Path(path.replace(FLOW_DIRECTORY_MACRO_IN_CONFIG, self.flow.resolve().as_posix())).resolve()
# in case user manually modifies ~/.promptflow/pf.yaml
# fall back to default run output path
if path.as_posix() == flow_posix_path:
raise Exception(f"{FLOW_DIRECTORY_MACRO_IN_CONFIG!r} is not a valid value.")
path.mkdir(parents=True, exist_ok=True)
except Exception: # pylint: disable=broad-except
path = Path.home() / PROMPT_FLOW_DIR_NAME / ".runs"
warning_message = (
"Got unexpected error when parsing specified output path: "
f"{config.get_run_output_path()!r}; "
f"will use default output path: {path!r} instead."
)
logger.warning(warning_message)
return (path / str(self.name)).resolve()
@classmethod
def _load_from_source(cls, source: Union[str, Path], params_override: Optional[Dict] = None, **kwargs) -> "Run":
"""Load run from run record source folder."""
source = Path(source)
params_override = params_override or {}
run_metadata_file = source / DownloadedRun.RUN_METADATA_FILE_NAME
if not run_metadata_file.exists():
raise UserErrorException(
f"Invalid run source: {source!r}. Expecting a valid run source folder with {run_metadata_file!r}. "
f"Please make sure the run source is downloaded by 'pfazure run download' command."
)
# extract run info from source folder
with open(source / DownloadedRun.RUN_METADATA_FILE_NAME, encoding=DEFAULT_ENCODING) as f:
run_info = json.load(f)
return cls(
name=run_info["name"],
source=source,
run_source=RunInfoSources.EXISTING_RUN,
status=run_info["status"], # currently only support completed run
display_name=params_override.get("display_name", run_info.get("display_name", source.name)),
description=params_override.get("description", run_info.get("description", "")),
tags=params_override.get("tags", run_info.get("tags", {})),
created_on=datetime.datetime.fromisoformat(run_info["created_on"]),
start_time=datetime.datetime.fromisoformat(run_info["start_time"]),
end_time=datetime.datetime.fromisoformat(run_info["end_time"]),
**kwargs,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_eager_flow.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from os import PathLike
from pathlib import Path
from typing import Union
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY
from promptflow._sdk.entities._flow import FlowBase
from promptflow.exceptions import UserErrorException
class EagerFlow(FlowBase):
"""This class is used to represent an eager flow."""
def __init__(
self,
path: Union[str, PathLike],
entry: str,
data: dict,
**kwargs,
):
self.path = Path(path)
self.code = self.path.parent
self.entry = entry
self._data = data
super().__init__(**kwargs)
@property
def language(self) -> str:
return self._data.get(LANGUAGE_KEY, FlowLanguage.Python)
@classmethod
def _create_schema_for_validation(cls, context):
# import here to avoid circular import
from ..schemas._flow import EagerFlowSchema
return EagerFlowSchema(context=context)
@classmethod
def _load(cls, path: Path, entry: str = None, data: dict = None, **kwargs):
data = data or {}
# schema validation on unknown fields
if path.suffix in [".yaml", ".yml"]:
data = cls._create_schema_for_validation(context={BASE_PATH_CONTEXT_KEY: path.parent}).load(data)
path = data["path"]
if entry:
raise UserErrorException("Specifying entry function is not allowed when YAML file is provided.")
else:
entry = data["entry"]
if entry is None:
raise UserErrorException(f"Entry function is not specified for flow {path}")
return cls(path=path, entry=entry, data=data, **kwargs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_yaml_translatable.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import abc
from typing import Dict, Optional
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY, CommonYamlFields
from promptflow._sdk._utils import load_from_dict
from promptflow._utils.yaml_utils import dump_yaml
class YAMLTranslatableMixin(abc.ABC):
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, data, params_override: Optional[list]):
"""Resolve the class to use for deserializing the data. Return current class if no override is provided.
:param data: Data to deserialize.
:type data: dict
:param params_override: Parameters to override, defaults to None
:type params_override: typing.Optional[list]
:return: Class to use for deserializing the data & its "type". Type will be None if no override is provided.
:rtype: tuple[class, typing.Optional[str]]
"""
@classmethod
def _get_schema_cls(self):
pass
def _to_dict(self) -> Dict:
schema_cls = self._get_schema_cls()
return schema_cls(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
def _to_yaml(self) -> str:
return dump_yaml(self._to_dict())
def __str__(self):
try:
return self._to_yaml()
except BaseException: # pylint: disable=broad-except
return super(YAMLTranslatableMixin, self).__str__()
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs):
schema_cls = cls._get_schema_cls()
loaded_data = load_from_dict(schema_cls, data, context, additional_message, **kwargs)
# pop the type field since it already exists in class init
loaded_data.pop(CommonYamlFields.TYPE, None)
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_connection.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import abc
import importlib
import json
import types
from os import PathLike
from pathlib import Path
from typing import Dict, List, Union
from promptflow._core.token_provider import AzureTokenProvider, TokenProviderABC
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
PARAMS_OVERRIDE_KEY,
SCHEMA_KEYS_CONTEXT_CONFIG_KEY,
SCHEMA_KEYS_CONTEXT_SECRET_KEY,
SCRUBBED_VALUE,
SCRUBBED_VALUE_NO_CHANGE,
SCRUBBED_VALUE_USER_INPUT,
ConfigValueType,
ConnectionType,
CustomStrongTypeConnectionConfigs,
)
from promptflow._sdk._errors import UnsecureConnectionError, SDKError
from promptflow._sdk._orm.connection import Connection as ORMConnection
from promptflow._sdk._utils import (
decrypt_secret_value,
encrypt_secret_value,
find_type_in_override,
in_jupyter_notebook,
print_yellow_warning,
snake_to_camel,
)
from promptflow._sdk.entities._yaml_translatable import YAMLTranslatableMixin
from promptflow._sdk.schemas._connection import (
AzureContentSafetyConnectionSchema,
AzureOpenAIConnectionSchema,
CognitiveSearchConnectionSchema,
CustomConnectionSchema,
CustomStrongTypeConnectionSchema,
FormRecognizerConnectionSchema,
OpenAIConnectionSchema,
QdrantConnectionSchema,
SerpConnectionSchema,
WeaviateConnectionSchema,
)
from promptflow._utils.logger_utils import LoggerFactory
from promptflow.contracts.types import Secret
from promptflow.exceptions import ValidationException, UserErrorException
logger = LoggerFactory.get_logger(name=__name__)
PROMPTFLOW_CONNECTIONS = "promptflow.connections"
class _Connection(YAMLTranslatableMixin):
"""A connection entity that stores the connection information.
:param name: Connection name
:type name: str
:param type: Possible values include: "OpenAI", "AzureOpenAI", "Custom".
:type type: str
:param module: The module of connection class, used for execution.
:type module: str
:param configs: The configs kv pairs.
:type configs: Dict[str, str]
:param secrets: The secrets kv pairs.
:type secrets: Dict[str, str]
"""
TYPE = ConnectionType._NOT_SET
def __init__(
self,
name: str = "default_connection",
module: str = "promptflow.connections",
configs: Dict[str, str] = None,
secrets: Dict[str, str] = None,
**kwargs,
):
self.name = name
self.type = self.TYPE
self.class_name = f"{self.TYPE.value}Connection" # The type in executor connection dict
self.configs = configs or {}
self.module = module
# Note the connection secrets value behaviors:
# --------------------------------------------------------------------------------
# | secret value | CLI create | CLI update | SDK create_or_update |
# --------------------------------------------------------------------------------
# | empty or all "*" | prompt input | use existing values | use existing values |
# | <no-change> | prompt input | use existing values | use existing values |
# | <user-input> | prompt input | prompt input | raise error |
# --------------------------------------------------------------------------------
self.secrets = secrets or {}
self._secrets = {**self.secrets} # Un-scrubbed secrets
self.expiry_time = kwargs.get("expiry_time", None)
self.created_date = kwargs.get("created_date", None)
self.last_modified_date = kwargs.get("last_modified_date", None)
# Conditional assignment to prevent entity bloat when unused.
print_as_yaml = kwargs.pop("print_as_yaml", in_jupyter_notebook())
if print_as_yaml:
self.print_as_yaml = True
@classmethod
def _casting_type(cls, typ):
type_dict = {
"azure_open_ai": ConnectionType.AZURE_OPEN_AI.value,
"open_ai": ConnectionType.OPEN_AI.value,
}
if typ in type_dict:
return type_dict.get(typ)
return snake_to_camel(typ)
def keys(self) -> List:
"""Return keys of the connection properties."""
return list(self.configs.keys()) + list(self.secrets.keys())
def __getitem__(self, item):
# Note: This is added to allow usage **connection().
if item in self.secrets:
return self.secrets[item]
if item in self.configs:
return self.configs[item]
# raise UserErrorException(error=KeyError(f"Key {item!r} not found in connection {self.name!r}."))
# Cant't raise UserErrorException due to the code exit(1) of promptflow._cli._utils.py line 368.
raise KeyError(f"Key {item!r} not found in connection {self.name!r}.")
@classmethod
def _is_scrubbed_value(cls, value):
"""For scrubbed value, cli will get original for update, and prompt user to input for create."""
if value is None or not value:
return True
if all([v == "*" for v in value]):
return True
return value == SCRUBBED_VALUE_NO_CHANGE
@classmethod
def _is_user_input_value(cls, value):
"""The value will prompt user to input in cli for both create and update."""
return value == SCRUBBED_VALUE_USER_INPUT
def _validate_and_encrypt_secrets(self):
encrypt_secrets = {}
invalid_secrets = []
for k, v in self.secrets.items():
# In sdk experience, if v is not scrubbed, use it.
# If v is scrubbed, try to use the value in _secrets.
# If v is <user-input>, raise error.
if self._is_scrubbed_value(v):
# Try to get the value not scrubbed.
v = self._secrets.get(k)
if self._is_scrubbed_value(v) or self._is_user_input_value(v):
# Can't find the original value or is <user-input>, raise error.
invalid_secrets.append(k)
continue
encrypt_secrets[k] = encrypt_secret_value(v)
if invalid_secrets:
raise ValidationException(
f"Connection {self.name!r} secrets {invalid_secrets} value invalid, please fill them."
)
return encrypt_secrets
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
schema_cls = cls._get_schema_cls()
try:
loaded_data = schema_cls(context=context).load(data, **kwargs)
except Exception as e:
raise SDKError(f"Load connection failed with {str(e)}. f{(additional_message or '')}.")
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
def _to_dict(self) -> Dict:
schema_cls = self._get_schema_cls()
return schema_cls(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, data, params_override=None):
type_in_override = find_type_in_override(params_override)
type_str = type_in_override or data.get("type")
if type_str is None:
raise ValidationException("type is required for connection.")
type_str = cls._casting_type(type_str)
type_cls = _supported_types.get(type_str)
if type_cls is None:
raise ValidationException(
f"connection_type {type_str!r} is not supported. Supported types are: {list(_supported_types.keys())}"
)
return type_cls, type_str
@abc.abstractmethod
def _to_orm_object(self) -> ORMConnection:
pass
@classmethod
def _from_mt_rest_object(cls, mt_rest_obj) -> "_Connection":
type_cls, _ = cls._resolve_cls_and_type(data={"type": mt_rest_obj.connection_type})
obj = type_cls._from_mt_rest_object(mt_rest_obj)
return obj
@classmethod
def _from_orm_object_with_secrets(cls, orm_object: ORMConnection):
# !!! Attention !!!: Do not use this function to user facing api, use _from_orm_object to remove secrets.
type_cls, _ = cls._resolve_cls_and_type(data={"type": orm_object.connectionType})
obj = type_cls._from_orm_object_with_secrets(orm_object)
return obj
@classmethod
def _from_orm_object(cls, orm_object: ORMConnection):
"""This function will create a connection object then scrub secrets."""
type_cls, _ = cls._resolve_cls_and_type(data={"type": orm_object.connectionType})
obj = type_cls._from_orm_object_with_secrets(orm_object)
# Note: we may can't get secret keys for custom connection from MT
obj.secrets = {k: SCRUBBED_VALUE for k in obj.secrets}
return obj
@classmethod
def _load(
cls,
data: Dict = None,
yaml_path: Union[PathLike, str] = None,
params_override: list = None,
**kwargs,
) -> "_Connection":
"""Load a job object from a yaml file.
:param cls: Indicates that this is a class method.
:type cls: class
:param data: Data Dictionary, defaults to None
:type data: Dict, optional
:param yaml_path: YAML Path, defaults to None
:type yaml_path: Union[PathLike, str], optional
:param params_override: Fields to overwrite on top of the yaml file.
Format is [{"field1": "value1"}, {"field2": "value2"}], defaults to None
:type params_override: List[Dict], optional
:param kwargs: A dictionary of additional configuration parameters.
:type kwargs: dict
:raises Exception: An exception
:return: Loaded job object.
:rtype: Job
"""
data = data or {}
params_override = params_override or []
context = {
BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("../../azure/_entities/"),
PARAMS_OVERRIDE_KEY: params_override,
}
connection_type, type_str = cls._resolve_cls_and_type(data, params_override)
connection = connection_type._load_from_dict(
data=data,
context=context,
additional_message=f"If you are trying to configure a job that is not of type {type_str}, please specify "
f"the correct connection type in the 'type' property.",
**kwargs,
)
return connection
def _to_execution_connection_dict(self) -> dict:
value = {**self.configs, **self.secrets}
secret_keys = list(self.secrets.keys())
return {
"type": self.class_name, # Required class name for connection in executor
"module": self.module,
"value": {k: v for k, v in value.items() if v is not None}, # Filter None value out
"secret_keys": secret_keys,
}
@classmethod
def _from_execution_connection_dict(cls, name, data) -> "_Connection":
type_cls, _ = cls._resolve_cls_and_type(data={"type": data.get("type")[: -len("Connection")]})
value_dict = data.get("value", {})
if type_cls == CustomConnection:
secrets = {k: v for k, v in value_dict.items() if k in data.get("secret_keys", [])}
configs = {k: v for k, v in value_dict.items() if k not in secrets}
return CustomConnection(name=name, configs=configs, secrets=secrets)
return type_cls(name=name, **value_dict)
def _get_scrubbed_secrets(self):
"""Return the scrubbed secrets of connection."""
return {key: val for key, val in self.secrets.items() if self._is_scrubbed_value(val)}
class _StrongTypeConnection(_Connection):
def _to_orm_object(self):
# Both keys & secrets will be stored in configs for strong type connection.
secrets = self._validate_and_encrypt_secrets()
return ORMConnection(
connectionName=self.name,
connectionType=self.type.value,
configs=json.dumps({**self.configs, **secrets}),
customConfigs="{}",
expiryTime=self.expiry_time,
createdDate=self.created_date,
lastModifiedDate=self.last_modified_date,
)
@classmethod
def _from_orm_object_with_secrets(cls, orm_object: ORMConnection):
# !!! Attention !!!: Do not use this function to user facing api, use _from_orm_object to remove secrets.
# Both keys & secrets will be stored in configs for strong type connection.
type_cls, _ = cls._resolve_cls_and_type(data={"type": orm_object.connectionType})
obj = type_cls(
name=orm_object.connectionName,
expiry_time=orm_object.expiryTime,
created_date=orm_object.createdDate,
last_modified_date=orm_object.lastModifiedDate,
**json.loads(orm_object.configs),
)
obj.secrets = {k: decrypt_secret_value(obj.name, v) for k, v in obj.secrets.items()}
obj._secrets = {**obj.secrets}
return obj
@classmethod
def _from_mt_rest_object(cls, mt_rest_obj):
type_cls, _ = cls._resolve_cls_and_type(data={"type": mt_rest_obj.connection_type})
configs = mt_rest_obj.configs or {}
# For not ARM strong type connection, e.g. OpenAI, api_key will not be returned, but is required argument.
# For ARM strong type connection, api_key will be None and missing when conn._to_dict(), so set a scrubbed one.
configs.update({"api_key": SCRUBBED_VALUE})
obj = type_cls(
name=mt_rest_obj.connection_name,
expiry_time=mt_rest_obj.expiry_time,
created_date=mt_rest_obj.created_date,
last_modified_date=mt_rest_obj.last_modified_date,
**configs,
)
return obj
@property
def api_key(self):
"""Return the api key."""
return self.secrets.get("api_key", SCRUBBED_VALUE)
@api_key.setter
def api_key(self, value):
"""Set the api key."""
self.secrets["api_key"] = value
class AzureOpenAIConnection(_StrongTypeConnection):
"""Azure Open AI connection.
:param api_key: The api key.
:type api_key: str
:param api_base: The api base.
:type api_base: str
:param api_type: The api type, default "azure".
:type api_type: str
:param api_version: The api version, default "2023-07-01-preview".
:type api_version: str
:param token_provider: The token provider.
:type token_provider: promptflow._core.token_provider.TokenProviderABC
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.AZURE_OPEN_AI
def __init__(
self,
api_key: str,
api_base: str,
api_type: str = "azure",
api_version: str = "2023-07-01-preview",
token_provider: TokenProviderABC = None,
**kwargs,
):
configs = {"api_base": api_base, "api_type": api_type, "api_version": api_version}
secrets = {"api_key": api_key}
self._token_provider = token_provider
super().__init__(configs=configs, secrets=secrets, **kwargs)
@classmethod
def _get_schema_cls(cls):
return AzureOpenAIConnectionSchema
@property
def api_base(self):
"""Return the connection api base."""
return self.configs.get("api_base")
@api_base.setter
def api_base(self, value):
"""Set the connection api base."""
self.configs["api_base"] = value
@property
def api_type(self):
"""Return the connection api type."""
return self.configs.get("api_type")
@api_type.setter
def api_type(self, value):
"""Set the connection api type."""
self.configs["api_type"] = value
@property
def api_version(self):
"""Return the connection api version."""
return self.configs.get("api_version")
@api_version.setter
def api_version(self, value):
"""Set the connection api version."""
self.configs["api_version"] = value
def get_token(self):
"""Return the connection token."""
if not self._token_provider:
self._token_provider = AzureTokenProvider()
return self._token_provider.get_token()
class OpenAIConnection(_StrongTypeConnection):
"""Open AI connection.
:param api_key: The api key.
:type api_key: str
:param organization: Optional. The unique identifier for your organization which can be used in API requests.
:type organization: str
:param base_url: Optional. Specify when use customized api base, leave None to use open ai default api base.
:type base_url: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.OPEN_AI
def __init__(self, api_key: str, organization: str = None, base_url=None, **kwargs):
if base_url == "":
# Keep empty as None to avoid disturbing openai pick the default api base.
base_url = None
configs = {"organization": organization, "base_url": base_url}
secrets = {"api_key": api_key}
super().__init__(configs=configs, secrets=secrets, **kwargs)
@classmethod
def _get_schema_cls(cls):
return OpenAIConnectionSchema
@property
def organization(self):
"""Return the connection organization."""
return self.configs.get("organization")
@organization.setter
def organization(self, value):
"""Set the connection organization."""
self.configs["organization"] = value
@property
def base_url(self):
"""Return the connection api base."""
return self.configs.get("base_url")
@base_url.setter
def base_url(self, value):
"""Set the connection api base."""
self.configs["base_url"] = value
class SerpConnection(_StrongTypeConnection):
"""Serp connection.
:param api_key: The api key.
:type api_key: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.SERP
def __init__(self, api_key: str, **kwargs):
secrets = {"api_key": api_key}
super().__init__(secrets=secrets, **kwargs)
@classmethod
def _get_schema_cls(cls):
return SerpConnectionSchema
class _EmbeddingStoreConnection(_StrongTypeConnection):
TYPE = ConnectionType._NOT_SET
def __init__(self, api_key: str, api_base: str, **kwargs):
configs = {"api_base": api_base}
secrets = {"api_key": api_key}
super().__init__(module="promptflow_vectordb.connections", configs=configs, secrets=secrets, **kwargs)
@property
def api_base(self):
return self.configs.get("api_base")
@api_base.setter
def api_base(self, value):
self.configs["api_base"] = value
class QdrantConnection(_EmbeddingStoreConnection):
"""Qdrant connection.
:param api_key: The api key.
:type api_key: str
:param api_base: The api base.
:type api_base: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.QDRANT
@classmethod
def _get_schema_cls(cls):
return QdrantConnectionSchema
class WeaviateConnection(_EmbeddingStoreConnection):
"""Weaviate connection.
:param api_key: The api key.
:type api_key: str
:param api_base: The api base.
:type api_base: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.WEAVIATE
@classmethod
def _get_schema_cls(cls):
return WeaviateConnectionSchema
class CognitiveSearchConnection(_StrongTypeConnection):
"""Cognitive Search connection.
:param api_key: The api key.
:type api_key: str
:param api_base: The api base.
:type api_base: str
:param api_version: The api version, default "2023-07-01-Preview".
:type api_version: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.COGNITIVE_SEARCH
def __init__(self, api_key: str, api_base: str, api_version: str = "2023-07-01-Preview", **kwargs):
configs = {"api_base": api_base, "api_version": api_version}
secrets = {"api_key": api_key}
super().__init__(configs=configs, secrets=secrets, **kwargs)
@classmethod
def _get_schema_cls(cls):
return CognitiveSearchConnectionSchema
@property
def api_base(self):
"""Return the connection api base."""
return self.configs.get("api_base")
@api_base.setter
def api_base(self, value):
"""Set the connection api base."""
self.configs["api_base"] = value
@property
def api_version(self):
"""Return the connection api version."""
return self.configs.get("api_version")
@api_version.setter
def api_version(self, value):
"""Set the connection api version."""
self.configs["api_version"] = value
class AzureContentSafetyConnection(_StrongTypeConnection):
"""Azure Content Safety connection.
:param api_key: The api key.
:type api_key: str
:param endpoint: The api endpoint.
:type endpoint: str
:param api_version: The api version, default "2023-04-30-preview".
:type api_version: str
:param api_type: The api type, default "Content Safety".
:type api_type: str
:param name: Connection name.
:type name: str
"""
TYPE = ConnectionType.AZURE_CONTENT_SAFETY
def __init__(
self,
api_key: str,
endpoint: str,
api_version: str = "2023-10-01",
api_type: str = "Content Safety",
**kwargs,
):
configs = {"endpoint": endpoint, "api_version": api_version, "api_type": api_type}
secrets = {"api_key": api_key}
super().__init__(configs=configs, secrets=secrets, **kwargs)
@classmethod
def _get_schema_cls(cls):
return AzureContentSafetyConnectionSchema
@property
def endpoint(self):
"""Return the connection endpoint."""
return self.configs.get("endpoint")
@endpoint.setter
def endpoint(self, value):
"""Set the connection endpoint."""
self.configs["endpoint"] = value
@property
def api_version(self):
"""Return the connection api version."""
return self.configs.get("api_version")
@api_version.setter
def api_version(self, value):
"""Set the connection api version."""
self.configs["api_version"] = value
@property
def api_type(self):
"""Return the connection api type."""
return self.configs.get("api_type")
@api_type.setter
def api_type(self, value):
"""Set the connection api type."""
self.configs["api_type"] = value
class FormRecognizerConnection(AzureContentSafetyConnection):
"""Form Recognizer connection.
:param api_key: The api key.
:type api_key: str
:param endpoint: The api endpoint.
:type endpoint: str
:param api_version: The api version, default "2023-07-31".
:type api_version: str
:param api_type: The api type, default "Form Recognizer".
:type api_type: str
:param name: Connection name.
:type name: str
"""
# Note: FormRecognizer and ContentSafety are using CognitiveService type in ARM, so keys are the same.
TYPE = ConnectionType.FORM_RECOGNIZER
def __init__(
self, api_key: str, endpoint: str, api_version: str = "2023-07-31", api_type: str = "Form Recognizer", **kwargs
):
super().__init__(api_key=api_key, endpoint=endpoint, api_version=api_version, api_type=api_type, **kwargs)
@classmethod
def _get_schema_cls(cls):
return FormRecognizerConnectionSchema
class CustomStrongTypeConnection(_Connection):
"""Custom strong type connection.
.. note::
This connection type should not be used directly. Below is an example of how to use CustomStrongTypeConnection:
.. code-block:: python
class MyCustomConnection(CustomStrongTypeConnection):
api_key: Secret
api_base: str
:param configs: The configs kv pairs.
:type configs: Dict[str, str]
:param secrets: The secrets kv pairs.
:type secrets: Dict[str, str]
:param name: Connection name
:type name: str
"""
def __init__(
self,
secrets: Dict[str, str],
configs: Dict[str, str] = None,
**kwargs,
):
# There are two cases to init a Custom strong type connection:
# 1. The connection is created through SDK PFClient, custom_type and custom_module are not in the kwargs.
# 2. The connection is loaded from template file, custom_type and custom_module are in the kwargs.
custom_type = kwargs.get(CustomStrongTypeConnectionConfigs.TYPE, None)
custom_module = kwargs.get(CustomStrongTypeConnectionConfigs.MODULE, None)
if custom_type:
configs.update({CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY: custom_type})
if custom_module:
configs.update({CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY: custom_module})
self.kwargs = kwargs
super().__init__(configs=configs, secrets=secrets, **kwargs)
self.module = kwargs.get("module", self.__class__.__module__)
self.custom_type = custom_type or self.__class__.__name__
self.package = kwargs.get(CustomStrongTypeConnectionConfigs.PACKAGE, None)
self.package_version = kwargs.get(CustomStrongTypeConnectionConfigs.PACKAGE_VERSION, None)
def __getattribute__(self, item):
# Note: The reason to overwrite __getattribute__ instead of __getattr__ is as follows:
# Custom strong type connection is written this way:
# class MyCustomConnection(CustomStrongTypeConnection):
# api_key: Secret
# api_base: str = "This is a default value"
# api_base has a default value, my_custom_connection_instance.api_base would not trigger __getattr__.
# The default value will be returned directly instead of the real value in configs.
annotations = getattr(super().__getattribute__("__class__"), "__annotations__", {})
if item in annotations:
if annotations[item] == Secret:
return self.secrets[item]
else:
return self.configs[item]
return super().__getattribute__(item)
def __setattr__(self, key, value):
annotations = getattr(super().__getattribute__("__class__"), "__annotations__", {})
if key in annotations:
if annotations[key] == Secret:
self.secrets[key] = value
else:
self.configs[key] = value
return super().__setattr__(key, value)
def _to_orm_object(self) -> ORMConnection:
custom_connection = self._convert_to_custom()
return custom_connection._to_orm_object()
def _convert_to_custom(self):
# update configs
self.configs.update({CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY: self.custom_type})
self.configs.update({CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY: self.module})
if self.package and self.package_version:
self.configs.update({CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_KEY: self.package})
self.configs.update(
{CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_VERSION_KEY: self.package_version}
)
custom_connection = CustomConnection(configs=self.configs, secrets=self.secrets, **self.kwargs)
return custom_connection
@classmethod
def _get_custom_keys(cls, data: Dict):
# The data could be either from yaml or from DB.
# If from yaml, 'custom_type' and 'module' are outside the configs of data.
# If from DB, 'custom_type' and 'module' are within the configs of data.
if not data.get(CustomStrongTypeConnectionConfigs.TYPE) or not data.get(
CustomStrongTypeConnectionConfigs.MODULE
):
if (
not data["configs"][CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY]
or not data["configs"][CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY]
):
error = ValueError("custom_type and module are required for custom strong type connections.")
raise UserErrorException(message=str(error), error=error)
else:
m = data["configs"][CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY]
custom_cls = data["configs"][CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY]
else:
m = data[CustomStrongTypeConnectionConfigs.MODULE]
custom_cls = data[CustomStrongTypeConnectionConfigs.TYPE]
try:
module = importlib.import_module(m)
cls = getattr(module, custom_cls)
except ImportError:
error = ValueError(
f"Can't find module {m} in current environment. Please check the module is correctly configured."
)
raise UserErrorException(message=str(error), error=error)
except AttributeError:
error = ValueError(
f"Can't find class {custom_cls} in module {m}. "
f"Please check the custom_type is correctly configured."
)
raise UserErrorException(message=str(error), error=error)
schema_configs = {}
schema_secrets = {}
for k, v in cls.__annotations__.items():
if v == Secret:
schema_secrets[k] = v
else:
schema_configs[k] = v
return schema_configs, schema_secrets
@classmethod
def _get_schema_cls(cls):
return CustomStrongTypeConnectionSchema
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
schema_config_keys, schema_secret_keys = cls._get_custom_keys(data)
context[SCHEMA_KEYS_CONTEXT_CONFIG_KEY] = schema_config_keys
context[SCHEMA_KEYS_CONTEXT_SECRET_KEY] = schema_secret_keys
return (super()._load_from_dict(data, context, additional_message, **kwargs))._convert_to_custom()
class CustomConnection(_Connection):
"""Custom connection.
:param configs: The configs kv pairs.
:type configs: Dict[str, str]
:param secrets: The secrets kv pairs.
:type secrets: Dict[str, str]
:param name: Connection name
:type name: str
"""
TYPE = ConnectionType.CUSTOM
def __init__(
self,
secrets: Dict[str, str],
configs: Dict[str, str] = None,
**kwargs,
):
super().__init__(secrets=secrets, configs=configs, **kwargs)
@classmethod
def _get_schema_cls(cls):
return CustomConnectionSchema
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
# If context has params_override, it means the data would be updated by overridden values.
# Provide CustomStrongTypeConnectionSchema if 'custom_type' in params_override, else CustomConnectionSchema.
# For example:
# If a user updates an existing connection by re-upserting a connection file,
# the 'data' from DB is CustomConnection,
# but 'params_override' would actually contain custom strong type connection data.
is_custom_strong_type = data.get(CustomStrongTypeConnectionConfigs.TYPE) or any(
CustomStrongTypeConnectionConfigs.TYPE in d for d in context.get(PARAMS_OVERRIDE_KEY, [])
)
if is_custom_strong_type:
return CustomStrongTypeConnection._load_from_dict(data, context, additional_message, **kwargs)
return super()._load_from_dict(data, context, additional_message, **kwargs)
def __getattr__(self, item):
# Note: This is added for compatibility with promptflow.connections custom connection usage.
if item == "secrets":
# Usually obj.secrets will not reach here
# This is added to handle copy.deepcopy loop issue
return super().__getattribute__("secrets")
if item == "configs":
# Usually obj.configs will not reach here
# This is added to handle copy.deepcopy loop issue
return super().__getattribute__("configs")
if item in self.secrets:
logger.warning("Please use connection.secrets[key] to access secrets.")
return self.secrets[item]
if item in self.configs:
logger.warning("Please use connection.configs[key] to access configs.")
return self.configs[item]
return super().__getattribute__(item)
def is_secret(self, item):
"""Check if item is a secret."""
# Note: This is added for compatibility with promptflow.connections custom connection usage.
return item in self.secrets
def _to_orm_object(self):
# Both keys & secrets will be set in custom configs with value type specified for custom connection.
if not self.secrets:
error = ValueError(
"Secrets is required for custom connection, "
"please use CustomConnection(configs={key1: val1}, secrets={key2: val2}) "
"to initialize custom connection."
)
raise UserErrorException(message=str(error), error=error)
custom_configs = {
k: {"configValueType": ConfigValueType.STRING.value, "value": v} for k, v in self.configs.items()
}
encrypted_secrets = self._validate_and_encrypt_secrets()
custom_configs.update(
{k: {"configValueType": ConfigValueType.SECRET.value, "value": v} for k, v in encrypted_secrets.items()}
)
return ORMConnection(
connectionName=self.name,
connectionType=self.type.value,
configs="{}",
customConfigs=json.dumps(custom_configs),
expiryTime=self.expiry_time,
createdDate=self.created_date,
lastModifiedDate=self.last_modified_date,
)
@classmethod
def _from_orm_object_with_secrets(cls, orm_object: ORMConnection):
# !!! Attention !!!: Do not use this function to user facing api, use _from_orm_object to remove secrets.
# Both keys & secrets will be set in custom configs with value type specified for custom connection.
configs = {
k: v["value"]
for k, v in json.loads(orm_object.customConfigs).items()
if v["configValueType"] == ConfigValueType.STRING.value
}
secrets = {}
unsecure_connection = False
custom_type = None
for k, v in json.loads(orm_object.customConfigs).items():
if k == CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY:
custom_type = v["value"]
continue
if not v["configValueType"] == ConfigValueType.SECRET.value:
continue
try:
secrets[k] = decrypt_secret_value(orm_object.connectionName, v["value"])
except UnsecureConnectionError:
# This is to workaround old custom secrets that are not encrypted with Fernet.
unsecure_connection = True
secrets[k] = v["value"]
if unsecure_connection:
print_yellow_warning(
f"Warning: Please delete and re-create connection {orm_object.connectionName} "
"due to a security issue in the old sdk version."
)
return cls(
name=orm_object.connectionName,
configs=configs,
secrets=secrets,
custom_type=custom_type,
expiry_time=orm_object.expiryTime,
created_date=orm_object.createdDate,
last_modified_date=orm_object.lastModifiedDate,
)
@classmethod
def _from_mt_rest_object(cls, mt_rest_obj):
type_cls, _ = cls._resolve_cls_and_type(data={"type": mt_rest_obj.connection_type})
if not mt_rest_obj.custom_configs:
mt_rest_obj.custom_configs = {}
configs = {
k: v.value
for k, v in mt_rest_obj.custom_configs.items()
if v.config_value_type == ConfigValueType.STRING.value
}
secrets = {
k: v.value
for k, v in mt_rest_obj.custom_configs.items()
if v.config_value_type == ConfigValueType.SECRET.value
}
return cls(
name=mt_rest_obj.connection_name,
configs=configs,
secrets=secrets,
expiry_time=mt_rest_obj.expiry_time,
created_date=mt_rest_obj.created_date,
last_modified_date=mt_rest_obj.last_modified_date,
)
def _is_custom_strong_type(self):
return (
CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY in self.configs
and self.configs[CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY]
)
def _convert_to_custom_strong_type(self, module=None, to_class=None) -> CustomStrongTypeConnection:
# There are two scenarios to convert a custom connection to custom strong type connection:
# 1. The connection is created from a custom strong type connection template file.
# Custom type and module name are present in the configs.
# 2. The connection is created through SDK PFClient or a custom connection template file.
# Custom type and module name are not present in the configs. Module and class must be passed for conversion.
if to_class == self.__class__.__name__:
# No need to convert.
return self
import importlib
if (
CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY in self.configs
and CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY in self.configs
):
module_name = self.configs.get(CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY)
module = importlib.import_module(module_name)
custom_conn_name = self.configs.get(CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY)
elif isinstance(module, str) and isinstance(to_class, str):
module_name = module
module = importlib.import_module(module_name)
custom_conn_name = to_class
elif isinstance(module, types.ModuleType) and isinstance(to_class, str):
custom_conn_name = to_class
else:
error = ValueError(
f"Failed to convert to custom strong type connection because of "
f"invalid module or class: {module}, {to_class}"
)
raise UserErrorException(message=str(error), error=error)
custom_defined_connection_class = getattr(module, custom_conn_name)
connection_instance = custom_defined_connection_class(configs=self.configs, secrets=self.secrets)
return connection_instance
_supported_types = {
v.TYPE.value: v
for v in globals().values()
if isinstance(v, type) and issubclass(v, _Connection) and not v.__name__.startswith("_")
}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_flow.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import abc
import json
from os import PathLike
from pathlib import Path
from typing import Dict, Optional, Tuple, Union
from marshmallow import Schema
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
DAG_FILE_NAME,
DEFAULT_ENCODING,
FLOW_TOOLS_JSON,
PROMPT_FLOW_DIR_NAME,
)
from promptflow._sdk.entities._connection import _Connection
from promptflow._sdk.entities._validation import SchemaValidatableMixin
from promptflow._utils.flow_utils import resolve_flow_path
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import load_yaml, load_yaml_string
from promptflow.exceptions import ErrorTarget, UserErrorException
logger = get_cli_sdk_logger()
class FlowContext:
"""Flow context entity. the settings on this context will be applied to the flow when executing.
:param connections: Connections for the flow.
:type connections: Optional[Dict[str, Dict]]
:param variant: Variant of the flow.
:type variant: Optional[str]
:param variant: Overrides of the flow.
:type variant: Optional[Dict[str, Dict]]
:param streaming: Whether the flow's output need to be return in streaming mode.
:type streaming: Optional[bool]
"""
def __init__(
self,
*,
connections=None,
variant=None,
overrides=None,
streaming=None,
):
self.connections, self._connection_objs = connections or {}, {}
self.variant = variant
self.overrides = overrides or {}
self.streaming = streaming
# TODO: introduce connection provider support
def _resolve_connections(self):
# resolve connections and create placeholder for connection objects
for _, v in self.connections.items():
if isinstance(v, dict):
for k, conn in v.items():
if isinstance(conn, _Connection):
name = self._get_connection_obj_name(conn)
v[k] = name
self._connection_objs[name] = conn
@classmethod
def _get_connection_obj_name(cls, connection: _Connection):
# create a unique connection name for connection obj
# will generate same name if connection has same content
connection_dict = connection._to_dict()
connection_name = f"connection_{hash(json.dumps(connection_dict, sort_keys=True))}"
return connection_name
def _to_dict(self):
return {
"connections": self.connections,
"variant": self.variant,
"overrides": self.overrides,
"streaming": self.streaming,
}
def __eq__(self, other):
if isinstance(other, FlowContext):
return self._to_dict() == other._to_dict()
return False
def __hash__(self):
self._resolve_connections()
return hash(json.dumps(self._to_dict(), sort_keys=True))
class FlowBase(abc.ABC):
def __init__(self, **kwargs):
self._context = FlowContext()
self._content_hash = kwargs.pop("content_hash", None)
super().__init__(**kwargs)
@property
def context(self) -> FlowContext:
return self._context
@context.setter
def context(self, val):
if not isinstance(val, FlowContext):
raise UserErrorException("context must be a FlowContext object, got {type(val)} instead.")
self._context = val
@property
@abc.abstractmethod
def language(self) -> str:
"""Language of the flow."""
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, data, params_override):
"""Resolve the class to use for deserializing the data. Return current class if no override is provided.
:param data: Data to deserialize.
:type data: dict
:param params_override: Parameters to override, defaults to None
:type params_override: typing.Optional[list]
:return: Class to use for deserializing the data & its "type". Type will be None if no override is provided.
:rtype: tuple[class, typing.Optional[str]]
"""
return cls, "flow"
class Flow(FlowBase):
"""This class is used to represent a flow."""
def __init__(
self,
code: Union[str, PathLike],
dag: dict,
**kwargs,
):
self._code = Path(code)
path = kwargs.pop("path", None)
self._path = Path(path) if path else None
self.variant = kwargs.pop("variant", None) or {}
self.dag = dag
super().__init__(**kwargs)
@property
def code(self) -> Path:
return self._code
@code.setter
def code(self, value: Union[str, PathLike, Path]):
self._code = value
@property
def path(self) -> Path:
flow_file = self._path or self.code / DAG_FILE_NAME
if not flow_file.is_file():
raise UserErrorException(
"The directory does not contain a valid flow.",
target=ErrorTarget.CONTROL_PLANE_SDK,
)
return flow_file
@property
def language(self) -> str:
return self.dag.get(LANGUAGE_KEY, FlowLanguage.Python)
@classmethod
def _is_eager_flow(cls, data: dict):
"""Check if the flow is an eager flow. Use field 'entry' to determine."""
# If entry specified, it's an eager flow.
return data.get("entry")
@classmethod
def load(
cls,
source: Union[str, PathLike],
entry: str = None,
**kwargs,
):
from promptflow._sdk.entities._eager_flow import EagerFlow
source_path = Path(source)
if not source_path.exists():
raise UserErrorException(f"Source {source_path.absolute().as_posix()} does not exist")
flow_path = resolve_flow_path(source_path)
if not flow_path.exists():
raise UserErrorException(f"Flow file {flow_path.absolute().as_posix()} does not exist")
if flow_path.suffix in [".yaml", ".yml"]:
# read flow file to get hash
with open(flow_path, "r", encoding=DEFAULT_ENCODING) as f:
flow_content = f.read()
data = load_yaml_string(flow_content)
kwargs["content_hash"] = hash(flow_content)
is_eager_flow = cls._is_eager_flow(data)
if is_eager_flow:
return EagerFlow._load(path=flow_path, entry=entry, data=data, **kwargs)
else:
# TODO: schema validation and warning on unknown fields
return ProtectedFlow._load(path=flow_path, dag=data, **kwargs)
# if non-YAML file is provided, treat is as eager flow
return EagerFlow._load(path=flow_path, entry=entry, **kwargs)
def _init_executable(self, tuning_node=None, variant=None):
from promptflow._sdk._submitter import variant_overwrite_context
# TODO: check if there is potential bug here
# this is a little wired:
# 1. the executable is created from a temp folder when there is additional includes
# 2. after the executable is returned, the temp folder is deleted
with variant_overwrite_context(self.code, tuning_node, variant) as flow:
from promptflow.contracts.flow import Flow as ExecutableFlow
return ExecutableFlow.from_yaml(flow_file=flow.path, working_dir=flow.code)
def __eq__(self, other):
if isinstance(other, Flow):
return self._content_hash == other._content_hash and self.context == other.context
return False
def __hash__(self):
return hash(self.context) ^ self._content_hash
class ProtectedFlow(Flow, SchemaValidatableMixin):
"""This class is used to hide internal interfaces from user.
User interface should be carefully designed to avoid breaking changes, while developers may need to change internal
interfaces to improve the code quality. On the other hand, making all internal interfaces private will make it
strange to use them everywhere inside this package.
Ideally, developers should always initialize ProtectedFlow object instead of Flow object.
"""
def __init__(
self,
code: str,
params_override: Optional[Dict] = None,
**kwargs,
):
super().__init__(code=code, **kwargs)
self._flow_dir, self._dag_file_name = self._get_flow_definition(self.code)
self._executable = None
self._params_override = params_override
@classmethod
def _load(cls, path: Path, dag: dict, **kwargs):
return cls(code=path.parent.absolute().as_posix(), dag=dag, **kwargs)
@property
def flow_dag_path(self) -> Path:
return self._flow_dir / self._dag_file_name
@property
def name(self) -> str:
return self._flow_dir.name
@property
def display_name(self) -> str:
return self.dag.get("display_name", self.name)
@property
def tools_meta_path(self) -> Path:
target_path = self._flow_dir / PROMPT_FLOW_DIR_NAME / FLOW_TOOLS_JSON
target_path.parent.mkdir(parents=True, exist_ok=True)
return target_path
@classmethod
def _get_flow_definition(cls, flow, base_path=None) -> Tuple[Path, str]:
if base_path:
flow_path = Path(base_path) / flow
else:
flow_path = Path(flow)
if flow_path.is_dir() and (flow_path / DAG_FILE_NAME).is_file():
return flow_path, DAG_FILE_NAME
elif flow_path.is_file():
return flow_path.parent, flow_path.name
raise ValueError(f"Can't find flow with path {flow_path.as_posix()}.")
# region SchemaValidatableMixin
@classmethod
def _create_schema_for_validation(cls, context) -> Schema:
# import here to avoid circular import
from ..schemas._flow import FlowSchema
return FlowSchema(context=context)
def _default_context(self) -> dict:
return {BASE_PATH_CONTEXT_KEY: self._flow_dir}
def _create_validation_error(self, message, no_personal_data_message=None):
return UserErrorException(
message=message,
target=ErrorTarget.CONTROL_PLANE_SDK,
no_personal_data_message=no_personal_data_message,
)
def _dump_for_validation(self) -> Dict:
# Flow is read-only in control plane, so we always dump the flow from file
data = load_yaml(self.flow_dag_path)
if isinstance(self._params_override, dict):
data.update(self._params_override)
return data
# endregion
# region MLFlow model requirements
@property
def inputs(self):
# This is used for build mlflow model signature.
if not self._executable:
self._executable = self._init_executable()
return {k: v.type.value for k, v in self._executable.inputs.items()}
@property
def outputs(self):
# This is used for build mlflow model signature.
if not self._executable:
self._executable = self._init_executable()
return {k: v.type.value for k, v in self._executable.outputs.items()}
# endregion
def __call__(self, *args, **kwargs):
"""Calling flow as a function, the inputs should be provided with key word arguments.
Returns the output of the flow.
The function call throws UserErrorException: if the flow is not valid or the inputs are not valid.
SystemErrorException: if the flow execution failed due to unexpected executor error.
:param args: positional arguments are not supported.
:param kwargs: flow inputs with key word arguments.
:return:
"""
if args:
raise UserErrorException("Flow can only be called with keyword arguments.")
result = self.invoke(inputs=kwargs)
return result.output
def invoke(self, inputs: dict) -> "LineResult":
"""Invoke a flow and get a LineResult object."""
from promptflow._sdk._submitter.test_submitter import TestSubmitterViaProxy
from promptflow._sdk.operations._flow_context_resolver import FlowContextResolver
if self.dag.get(LANGUAGE_KEY, FlowLanguage.Python) == FlowLanguage.CSharp:
with TestSubmitterViaProxy(flow=self, flow_context=self.context).init() as submitter:
result = submitter.exec_with_inputs(
inputs=inputs,
)
return result
else:
invoker = FlowContextResolver.resolve(flow=self)
result = invoker._invoke(
data=inputs,
)
return result
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# isort: skip_file
# skip to avoid circular import
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from ._connection import (
AzureContentSafetyConnection,
AzureOpenAIConnection,
CognitiveSearchConnection,
CustomConnection,
OpenAIConnection,
SerpConnection,
QdrantConnection,
WeaviateConnection,
FormRecognizerConnection,
CustomStrongTypeConnection,
)
from ._run import Run
from ._validation import ValidationResult
from ._flow import FlowContext
__all__ = [
# region: Connection
"AzureContentSafetyConnection",
"AzureOpenAIConnection",
"OpenAIConnection",
"CustomConnection",
"CustomStrongTypeConnection",
"CognitiveSearchConnection",
"SerpConnection",
"QdrantConnection",
"WeaviateConnection",
"FormRecognizerConnection",
# endregion
# region Run
"Run",
"ValidationResult",
# endregion
# region Flow
"FlowContext",
# endregion
]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_validation/core.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=protected-access
import copy
import json
import os.path
import typing
from pathlib import Path
from typing import Dict, List, Optional
import pydash
import strictyaml
from marshmallow import ValidationError
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
class _ValidationStatus:
"""Validation status class.
Validation status is used to indicate the status of an validation result. It can be one of the following values:
Succeeded, Failed.
"""
SUCCEEDED = "Succeeded"
"""Succeeded."""
FAILED = "Failed"
"""Failed."""
class Diagnostic(object):
"""Represents a diagnostic of an asset validation error with the location info."""
def __init__(self, yaml_path: str, message: str, error_code: str, **kwargs) -> None:
"""Init Diagnostic.
:keyword yaml_path: A dash path from root to the target element of the diagnostic.
:paramtype yaml_path: str
:keyword message: Error message of diagnostic.
:paramtype message: str
:keyword error_code: Error code of diagnostic.
:paramtype error_code: str
"""
self.yaml_path = yaml_path
self.message = message
self.error_code = error_code
self.local_path, self.value = None, None
self._key = kwargs.pop("key", "yaml_path")
# Set extra info to attribute
for k, v in kwargs.items():
if not k.startswith("_"):
setattr(self, k, v)
def __repr__(self) -> str:
"""The asset friendly name and error message.
:return: The formatted diagnostic
:rtype: str
"""
return "{}: {}".format(getattr(self, self._key), self.message)
@classmethod
def create_instance(
cls,
yaml_path: str,
message: Optional[str] = None,
error_code: Optional[str] = None,
**kwargs,
):
"""Create a diagnostic instance.
:param yaml_path: A dash path from root to the target element of the diagnostic.
:type yaml_path: str
:param message: Error message of diagnostic.
:type message: str
:param error_code: Error code of diagnostic.
:type error_code: str
:return: The created instance
:rtype: Diagnostic
"""
return cls(
yaml_path=yaml_path,
message=message,
error_code=error_code,
**kwargs,
)
class ValidationResult(object):
"""Represents the result of validation.
This class is used to organize and parse diagnostics from both client & server side before expose them. The result
is immutable.
"""
def __init__(self) -> None:
self._target_obj = None
self._errors = []
self._warnings = []
self._kwargs = {}
def _set_extra_info(self, key, value):
self._kwargs[key] = value
def _get_extra_info(self, key, default=None):
return self._kwargs.get(key, default)
@property
def error_messages(self) -> Dict:
"""
Return all messages of errors in the validation result.
:return: A dictionary of error messages. The key is the yaml path of the error, and the value is the error
message.
:rtype: dict
"""
messages = {}
for diagnostic in self._errors:
message_key = getattr(diagnostic, diagnostic._key)
if message_key not in messages:
messages[message_key] = diagnostic.message
else:
messages[message_key] += "; " + diagnostic.message
return messages
@property
def passed(self) -> bool:
"""Returns boolean indicating whether any errors were found.
:return: True if the validation passed, False otherwise.
:rtype: bool
"""
return not self._errors
def _to_dict(self) -> typing.Dict[str, typing.Any]:
result = {
"result": _ValidationStatus.SUCCEEDED if self.passed else _ValidationStatus.FAILED,
}
result.update(self._kwargs)
for diagnostic_type, diagnostics in [
("errors", self._errors),
("warnings", self._warnings),
]:
messages = []
for diagnostic in diagnostics:
message = {
"message": diagnostic.message,
"path": diagnostic.yaml_path,
"value": pydash.get(self._target_obj, diagnostic.yaml_path, diagnostic.value),
}
if diagnostic.local_path:
message["location"] = str(diagnostic.local_path)
for attr in dir(diagnostic):
if attr not in message and not attr.startswith("_") and not callable(getattr(diagnostic, attr)):
message[attr] = getattr(diagnostic, attr)
message = {k: v for k, v in message.items() if v is not None}
messages.append(message)
if messages:
result[diagnostic_type] = messages
return result
def __repr__(self) -> str:
"""Get the string representation of the validation result.
:return: The string representation
:rtype: str
"""
return json.dumps(self._to_dict(), indent=2)
class MutableValidationResult(ValidationResult):
"""Used by the client side to construct a validation result.
The result is mutable and should not be exposed to the user.
"""
def __init__(self, target_obj: Optional[typing.Dict[str, typing.Any]] = None):
super().__init__()
self._target_obj = target_obj
def merge_with(
self,
target: ValidationResult,
field_name: Optional[str] = None,
condition_skip: Optional[typing.Callable] = None,
overwrite: bool = False,
):
"""Merge errors & warnings in another validation results into current one.
Will update current validation result.
If field_name is not None, then yaml_path in the other validation result will be updated accordingly.
* => field_name, a.b => field_name.a.b e.g.. If None, then no update.
:param target: Validation result to merge.
:type target: ValidationResult
:param field_name: The base field name for the target to merge.
:type field_name: str
:param condition_skip: A function to determine whether to skip the merge of a diagnostic in the target.
:type condition_skip: typing.Callable
:param overwrite: Whether to overwrite the current validation result. If False, all diagnostics will be kept;
if True, current diagnostics with the same yaml_path will be dropped.
:type overwrite: bool
:return: The current validation result.
:rtype: MutableValidationResult
"""
for source_diagnostics, target_diagnostics in [
(target._errors, self._errors),
(target._warnings, self._warnings),
]:
if overwrite:
keys_to_remove = set(map(lambda x: x.yaml_path, source_diagnostics))
target_diagnostics[:] = [
diagnostic for diagnostic in target_diagnostics if diagnostic.yaml_path not in keys_to_remove
]
for diagnostic in source_diagnostics:
if condition_skip and condition_skip(diagnostic):
continue
new_diagnostic = copy.deepcopy(diagnostic)
if field_name:
if new_diagnostic.yaml_path == "*":
new_diagnostic.yaml_path = field_name
else:
new_diagnostic.yaml_path = field_name + "." + new_diagnostic.yaml_path
target_diagnostics.append(new_diagnostic)
return self
def try_raise(
self,
raise_error: bool = True,
*,
error_func: typing.Callable[[str, str], Exception] = None,
) -> "MutableValidationResult":
"""Try to raise an error from the validation result.
If the validation is passed or raise_error is False, this method
will return the validation result.
:param raise_error: Whether to raise the error.
:type raise_error: bool
:keyword error_func: A function to create the error. If None, a marshmallow.ValidationError will be created.
The first parameter of the function is the string representation of the validation result,
and the second parameter is the error message without personal data.
:type error_func: typing.Callable[[str, str], Exception]
:return: The current validation result.
:rtype: MutableValidationResult
"""
# pylint: disable=logging-not-lazy
if raise_error is False:
return self
if self._warnings:
logger.warning("Schema validation warnings: %s" % str(self._warnings))
if not self.passed:
if error_func is None:
def error_func(msg, _):
return ValidationError(message=msg)
raise error_func(
self.__repr__(),
f"Schema validation failed: {self.error_messages}",
)
return self
def append_error(
self,
yaml_path: str = "*",
message: Optional[str] = None,
error_code: Optional[str] = None,
**kwargs,
):
"""Append an error to the validation result.
:param yaml_path: The yaml path of the error.
:type yaml_path: str
:param message: The message of the error.
:type message: str
:param error_code: The error code of the error.
:type error_code: str
:return: The current validation result.
:rtype: MutableValidationResult
"""
self._errors.append(
Diagnostic.create_instance(
yaml_path=yaml_path,
message=message,
error_code=error_code,
**kwargs,
)
)
return self
def resolve_location_for_diagnostics(self, source_path: str, resolve_value: bool = False):
"""Resolve location/value for diagnostics based on the source path where the validatable object is loaded.
Location includes local path of the exact file (can be different from the source path) & line number of the
invalid field. Value of a diagnostic is resolved from the validatable object in transfering to a dict by
default; however, when the validatable object is not available for the validation result, validation result is
created from marshmallow.ValidationError.messages e.g., it can be resolved from the source path.
:param source_path: The path of the source file.
:type source_path: str
:param resolve_value: Whether to resolve the value of the invalid field from source file.
:type resolve_value: bool
"""
resolver = _YamlLocationResolver(source_path)
for diagnostic in self._errors + self._warnings:
diagnostic.local_path, value = resolver.resolve(diagnostic.yaml_path)
if value is not None and resolve_value:
diagnostic.value = value
def append_warning(
self,
yaml_path: str = "*",
message: Optional[str] = None,
error_code: Optional[str] = None,
**kwargs,
):
"""Append a warning to the validation result.
:param yaml_path: The yaml path of the warning.
:type yaml_path: str
:param message: The message of the warning.
:type message: str
:param error_code: The error code of the warning.
:type error_code: str
:return: The current validation result.
:rtype: MutableValidationResult
"""
self._warnings.append(
Diagnostic.create_instance(
yaml_path=yaml_path,
message=message,
error_code=error_code,
**kwargs,
)
)
return self
class ValidationResultBuilder:
"""A helper class to create a validation result."""
UNKNOWN_MESSAGE = "Unknown field."
def __init__(self):
pass
@classmethod
def success(cls) -> MutableValidationResult:
"""Create a validation result with success status.
:return: A validation result
:rtype: MutableValidationResult
"""
return MutableValidationResult()
@classmethod
def from_single_message(
cls, singular_error_message: Optional[str] = None, yaml_path: str = "*", data: Optional[dict] = None
):
"""Create a validation result with only 1 diagnostic.
:param singular_error_message: diagnostic.message.
:type singular_error_message: Optional[str]
:param yaml_path: diagnostic.yaml_path.
:type yaml_path: str
:param data: serializedvalidation target.
:type data: Optional[Dict]
:return: The validation result
:rtype: MutableValidationResult
"""
obj = MutableValidationResult(target_obj=data)
if singular_error_message:
obj.append_error(message=singular_error_message, yaml_path=yaml_path)
return obj
@classmethod
def from_validation_error(
cls, error: ValidationError, *, source_path: Optional[str] = None, error_on_unknown_field=False
) -> MutableValidationResult:
"""Create a validation result from a ValidationError, which will be raised in marshmallow.Schema.load. Please
use this function only for exception in loading file.
:param error: ValidationError raised by marshmallow.Schema.load.
:type error: ValidationError
:keyword error_on_unknown_field: whether to raise error if there are unknown field diagnostics.
:paramtype error_on_unknown_field: bool
:return: The validation result
:rtype: MutableValidationResult
"""
obj = cls.from_validation_messages(
error.messages, data=error.data, error_on_unknown_field=error_on_unknown_field
)
if source_path:
obj.resolve_location_for_diagnostics(source_path, resolve_value=True)
return obj
@classmethod
def from_validation_messages(
cls, errors: typing.Dict, data: typing.Dict, *, error_on_unknown_field: bool = False
) -> MutableValidationResult:
"""Create a validation result from error messages, which will be returned by marshmallow.Schema.validate.
:param errors: error message returned by marshmallow.Schema.validate.
:type errors: dict
:param data: serialized data to validate
:type data: dict
:keyword error_on_unknown_field: whether to raise error if there are unknown field diagnostics.
:paramtype error_on_unknown_field: bool
:return: The validation result
:rtype: MutableValidationResult
"""
instance = MutableValidationResult(target_obj=data)
errors = copy.deepcopy(errors)
cls._from_validation_messages_recursively(errors, [], instance, error_on_unknown_field=error_on_unknown_field)
return instance
@classmethod
def _from_validation_messages_recursively(
cls,
errors: typing.Union[typing.Dict, typing.List, str],
path_stack: typing.List[str],
instance: MutableValidationResult,
error_on_unknown_field: bool,
):
cur_path = ".".join(path_stack) if path_stack else "*"
# single error message
if isinstance(errors, dict) and "_schema" in errors:
instance.append_error(
message=";".join(errors["_schema"]),
yaml_path=cur_path,
)
# errors on attributes
elif isinstance(errors, dict):
for field, msgs in errors.items():
# fields.Dict
if field in ["key", "value"]:
cls._from_validation_messages_recursively(msgs, path_stack, instance, error_on_unknown_field)
else:
# Todo: Add hack logic here to deal with error message in nested TypeSensitiveUnionField in
# DataTransfer: will be a nested dict with None field as dictionary key.
# open a item to track: https://msdata.visualstudio.com/Vienna/_workitems/edit/2244262/
if field is None:
cls._from_validation_messages_recursively(msgs, path_stack, instance, error_on_unknown_field)
else:
path_stack.append(field)
cls._from_validation_messages_recursively(msgs, path_stack, instance, error_on_unknown_field)
path_stack.pop()
# detailed error message
elif isinstance(errors, list) and all(isinstance(msg, str) for msg in errors):
if cls.UNKNOWN_MESSAGE in errors and not error_on_unknown_field:
# Unknown field is not a real error, so we should remove it and append a warning.
errors.remove(cls.UNKNOWN_MESSAGE)
instance.append_warning(message=cls.UNKNOWN_MESSAGE, yaml_path=cur_path)
if errors:
instance.append_error(message=";".join(errors), yaml_path=cur_path)
# union field
elif isinstance(errors, list):
def msg2str(msg):
if isinstance(msg, str):
return msg
if isinstance(msg, dict) and len(msg) == 1 and "_schema" in msg and len(msg["_schema"]) == 1:
return msg["_schema"][0]
return str(msg)
instance.append_error(message="; ".join([msg2str(x) for x in errors]), yaml_path=cur_path)
# unknown error
else:
instance.append_error(message=str(errors), yaml_path=cur_path)
class _YamlLocationResolver:
def __init__(self, source_path):
self._source_path = source_path
def resolve(self, yaml_path, source_path=None):
"""Resolve the location & value of a yaml path starting from source_path.
:param yaml_path: yaml path.
:type yaml_path: str
:param source_path: source path.
:type source_path: str
:return: the location & value of the yaml path based on source_path.
:rtype: Tuple[str, str]
"""
source_path = source_path or self._source_path
if source_path is None or not os.path.isfile(source_path):
return None, None
if yaml_path is None or yaml_path == "*":
return source_path, None
attrs = yaml_path.split(".")
attrs.reverse()
return self._resolve_recursively(attrs, Path(source_path))
def _resolve_recursively(self, attrs: List[str], source_path: Path):
with open(source_path, encoding="utf-8") as f:
try:
loaded_yaml = strictyaml.load(f.read())
except Exception as e: # pylint: disable=broad-except
msg = "Can't load source file %s as a strict yaml:\n%s" % (source_path, str(e))
logger.debug(msg)
return None, None
while attrs:
attr = attrs[-1]
if loaded_yaml.is_mapping() and attr in loaded_yaml:
loaded_yaml = loaded_yaml.get(attr)
attrs.pop()
elif loaded_yaml.is_sequence() and attr.isdigit() and 0 <= int(attr) < len(loaded_yaml):
loaded_yaml = loaded_yaml[int(attr)]
attrs.pop()
else:
try:
# if current object is a path of a valid yaml file, try to resolve location in new source file
next_path = Path(loaded_yaml.value)
if not next_path.is_absolute():
next_path = source_path.parent / next_path
if next_path.is_file():
return self._resolve_recursively(attrs, source_path=next_path)
except OSError:
pass
except TypeError:
pass
# if not, return current section
break
return (
f"{source_path.resolve().absolute()}#line {loaded_yaml.start_line}",
None if attrs else loaded_yaml.value,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_validation/schema.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=protected-access
import json
import typing
from marshmallow import Schema, ValidationError
from promptflow._utils.logger_utils import LoggerFactory
from .core import MutableValidationResult, ValidationResultBuilder
module_logger = LoggerFactory.get_logger(__name__)
class SchemaValidatableMixin:
"""The mixin class for schema validation."""
@classmethod
def _create_empty_validation_result(cls) -> MutableValidationResult:
"""Simply create an empty validation result
To reduce _ValidationResultBuilder importing, which is a private class.
:return: An empty validation result
:rtype: MutableValidationResult
"""
return ValidationResultBuilder.success()
@classmethod
def _load_with_schema(cls, data, *, context, raise_original_exception=False, **kwargs):
schema = cls._create_schema_for_validation(context=context)
try:
return schema.load(data, **kwargs)
except ValidationError as e:
if raise_original_exception:
raise e
msg = "Trying to load data with schema failed. Data:\n%s\nError: %s" % (
json.dumps(data, indent=4) if isinstance(data, dict) else data,
json.dumps(e.messages, indent=4),
)
raise cls._create_validation_error(
message=msg,
no_personal_data_message=str(e),
) from e
@classmethod
# pylint: disable-next=docstring-missing-param
def _create_schema_for_validation(cls, context) -> Schema:
"""Create a schema of the resource with specific context. Should be overridden by subclass.
:return: The schema of the resource.
:rtype: Schema.
"""
raise NotImplementedError()
def _default_context(self) -> dict:
"""Get the default context for schema validation. Should be overridden by subclass.
:return: The default context for schema validation
:rtype: dict
"""
raise NotImplementedError()
@property
def _schema_for_validation(self) -> Schema:
"""Return the schema of this Resource with default context. Do not override this method.
Override _create_schema_for_validation instead.
:return: The schema of the resource.
:rtype: Schema.
"""
return self._create_schema_for_validation(context=self._default_context())
def _dump_for_validation(self) -> typing.Dict:
"""Convert the resource to a dictionary.
:return: Converted dictionary
:rtype: typing.Dict
"""
return self._schema_for_validation.dump(self)
@classmethod
def _create_validation_error(cls, message: str, no_personal_data_message: str) -> Exception:
"""The function to create the validation exception to raise in _try_raise and _validate when
raise_error is True.
Should be overridden by subclass.
:param message: The error message containing detailed information
:type message: str
:param no_personal_data_message: The error message without personal data
:type no_personal_data_message: str
:return: The validation exception to raise
:rtype: Exception
"""
raise NotImplementedError()
@classmethod
def _try_raise(
cls, validation_result: MutableValidationResult, *, raise_error: bool = True
) -> MutableValidationResult:
return validation_result.try_raise(raise_error=raise_error, error_func=cls._create_validation_error)
def _validate(self, raise_error=False) -> MutableValidationResult:
"""Validate the resource. If raise_error is True, raise ValidationError if validation fails and log warnings if
applicable; Else, return the validation result.
:param raise_error: Whether to raise ValidationError if validation fails.
:type raise_error: bool
:return: The validation result
:rtype: MutableValidationResult
"""
result = self.__schema_validate()
result.merge_with(self._customized_validate())
return self._try_raise(result, raise_error=raise_error)
def _customized_validate(self) -> MutableValidationResult:
"""Validate the resource with customized logic.
Override this method to add customized validation logic.
:return: The customized validation result
:rtype: MutableValidationResult
"""
return self._create_empty_validation_result()
@classmethod
def _get_skip_fields_in_schema_validation(
cls,
) -> typing.List[str]:
"""Get the fields that should be skipped in schema validation.
Override this method to add customized validation logic.
:return: The fields to skip in schema validation
:rtype: typing.List[str]
"""
return []
def __schema_validate(self) -> MutableValidationResult:
"""Validate the resource with the schema.
:return: The validation result
:rtype: MutableValidationResult
"""
data = self._dump_for_validation()
messages = self._schema_for_validation.validate(data)
for skip_field in self._get_skip_fields_in_schema_validation():
if skip_field in messages:
del messages[skip_field]
return ValidationResultBuilder.from_validation_messages(messages, data=data)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/entities/_validation/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from .core import MutableValidationResult, ValidationResult, ValidationResultBuilder
from .schema import SchemaValidatableMixin
__all__ = [
"SchemaValidatableMixin",
"MutableValidationResult",
"ValidationResult",
"ValidationResultBuilder",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_experiment.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from marshmallow import fields, post_load, pre_load
from promptflow._sdk._constants import ExperimentNodeType
from promptflow._sdk.schemas._base import PatchedSchemaMeta, YamlFileSchema
from promptflow._sdk.schemas._fields import (
LocalPathField,
NestedField,
PrimitiveValueField,
StringTransformedEnum,
UnionField,
)
from promptflow._sdk.schemas._run import RunSchema
class CommandNodeSchema(YamlFileSchema):
# TODO: Not finalized now. Need to revisit.
name = fields.Str(required=True)
display_name = fields.Str()
type = StringTransformedEnum(allowed_values=ExperimentNodeType.COMMAND, required=True)
code = LocalPathField(default=".")
command = fields.Str(required=True)
inputs = fields.Dict(keys=fields.Str)
outputs = fields.Dict(keys=fields.Str, values=LocalPathField(allow_none=True))
environment_variables = fields.Dict(keys=fields.Str, values=fields.Str)
# runtime field, only available for cloud run
runtime = fields.Str() # TODO: Revisit the required fields
class FlowNodeSchema(RunSchema):
class Meta:
exclude = ["flow", "column_mapping", "data", "run"]
name = fields.Str(required=True)
type = StringTransformedEnum(allowed_values=ExperimentNodeType.FLOW, required=True)
inputs = fields.Dict(keys=fields.Str)
path = UnionField([LocalPathField(required=True), fields.Str(required=True)])
@pre_load
def warning_unknown_fields(self, data, **kwargs):
# Override to avoid warning here
return data
class ExperimentDataSchema(metaclass=PatchedSchemaMeta):
name = fields.Str(required=True)
path = LocalPathField(required=True)
class ExperimentInputSchema(metaclass=PatchedSchemaMeta):
name = fields.Str(required=True)
type = fields.Str(required=True)
default = PrimitiveValueField()
class ExperimentTemplateSchema(YamlFileSchema):
name = fields.Str()
description = fields.Str()
data = fields.List(NestedField(ExperimentDataSchema)) # Optional
inputs = fields.List(NestedField(ExperimentInputSchema)) # Optional
nodes = fields.List(
UnionField(
[
NestedField(CommandNodeSchema),
NestedField(FlowNodeSchema),
]
),
required=True,
)
@post_load
def resolve_nodes(self, data, **kwargs):
from promptflow._sdk.entities._experiment import CommandNode, FlowNode
nodes = data.get("nodes", [])
resolved_nodes = []
for node in nodes:
if not isinstance(node, dict):
continue
node_type = node.get("type", None)
if node_type == ExperimentNodeType.FLOW:
resolved_nodes.append(FlowNode._load_from_dict(data=node, context=self.context, additional_message=""))
elif node_type == ExperimentNodeType.COMMAND:
resolved_nodes.append(
CommandNode._load_from_dict(data=node, context=self.context, additional_message="")
)
else:
raise ValueError(f"Unknown node type {node_type} for node {node}.")
data["nodes"] = resolved_nodes
return data
@post_load
def resolve_data_and_inputs(self, data, **kwargs):
from promptflow._sdk.entities._experiment import ExperimentData, ExperimentInput
def resolve_resource(key, cls):
items = data.get(key, [])
resolved_result = []
for item in items:
if not isinstance(item, dict):
continue
resolved_result.append(
cls._load_from_dict(
data=item,
context=self.context,
additional_message=f"Failed to load {cls.__name__}",
)
)
return resolved_result
data["data"] = resolve_resource("data", ExperimentData)
data["inputs"] = resolve_resource("inputs", ExperimentInput)
return data
class ExperimentSchema(ExperimentTemplateSchema):
node_runs = fields.Dict(keys=fields.Str(), values=fields.Str()) # TODO: Revisit this
status = fields.Str(dump_only=True)
properties = fields.Dict(keys=fields.Str(), values=fields.Str(allow_none=True))
created_on = fields.Str(dump_only=True)
last_start_time = fields.Str(dump_only=True)
last_end_time = fields.Str(dump_only=True)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_run.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os.path
from dotenv import dotenv_values
from marshmallow import fields, post_load, pre_load
from promptflow._sdk._utils import is_remote_uri
from promptflow._sdk.schemas._base import PatchedSchemaMeta, YamlFileSchema
from promptflow._sdk.schemas._fields import LocalPathField, NestedField, UnionField
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
def _resolve_dot_env_file(data, **kwargs):
"""Resolve .env file to environment variables."""
env_var = data.get("environment_variables", None)
try:
if env_var and os.path.exists(env_var):
env_dict = dotenv_values(env_var)
data["environment_variables"] = env_dict
except TypeError:
pass
return data
class ResourcesSchema(metaclass=PatchedSchemaMeta):
"""Schema for resources."""
instance_type = fields.Str()
idle_time_before_shutdown_minutes = fields.Int()
class RemotePathStr(fields.Str):
default_error_messages = {
"invalid_path": "Invalid remote path. "
"Currently only azureml://xxx or public URL(e.g. https://xxx) are supported.",
}
def _validate(self, value):
# inherited validations like required, allow_none, etc.
super(RemotePathStr, self)._validate(value)
if value is None:
return
if not is_remote_uri(value):
raise self.make_error(
"invalid_path",
)
class RemoteFlowStr(fields.Str):
default_error_messages = {
"invalid_path": "Invalid remote flow path. Currently only azureml:<flow-name> is supported",
}
def _validate(self, value):
# inherited validations like required, allow_none, etc.
super(RemoteFlowStr, self)._validate(value)
if value is None:
return
if not isinstance(value, str) or not value.startswith("azureml:"):
raise self.make_error(
"invalid_path",
)
class RunSchema(YamlFileSchema):
"""Base schema for all run schemas."""
# TODO(2898455): support directly write path/flow + entry in run.yaml
# region: common fields
name = fields.Str()
display_name = fields.Str(required=False)
tags = fields.Dict(keys=fields.Str(), values=fields.Str(allow_none=True))
status = fields.Str(dump_only=True)
description = fields.Str(attribute="description")
properties = fields.Dict(keys=fields.Str(), values=fields.Str(allow_none=True))
# endregion: common fields
flow = UnionField([LocalPathField(required=True), RemoteFlowStr(required=True)])
# inputs field
data = UnionField([LocalPathField(), RemotePathStr()])
column_mapping = fields.Dict(keys=fields.Str)
# runtime field, only available for cloud run
runtime = fields.Str()
resources = NestedField(ResourcesSchema)
run = fields.Str()
# region: context
variant = fields.Str()
environment_variables = UnionField(
[
fields.Dict(keys=fields.Str(), values=fields.Str()),
# support load environment variables from .env file
LocalPathField(),
]
)
connections = fields.Dict(keys=fields.Str(), values=fields.Dict(keys=fields.Str()))
# endregion: context
# region: command node
command = fields.Str(dump_only=True)
outputs = fields.Dict(key=fields.Str(), dump_only=True)
# endregion: command node
@post_load
def resolve_dot_env_file(self, data, **kwargs):
return _resolve_dot_env_file(data, **kwargs)
@pre_load
def warning_unknown_fields(self, data, **kwargs):
# log warnings for unknown schema fields
unknown_fields = set(data) - set(self.fields)
if unknown_fields:
logger.warning("Run schema validation warnings. Unknown fields found: %s", unknown_fields)
return data
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_base.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=unused-argument,no-self-use
import copy
from pathlib import Path
from typing import Optional
from marshmallow import RAISE, fields, post_load, pre_load
from marshmallow.decorators import post_dump
from marshmallow.schema import Schema, SchemaMeta
from pydash import objects
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY, FILE_PREFIX, PARAMS_OVERRIDE_KEY
from promptflow._utils.logger_utils import LoggerFactory
from promptflow._utils.yaml_utils import load_yaml
module_logger = LoggerFactory.get_logger(__name__)
class PatchedMeta:
ordered = True
unknown = RAISE
class PatchedBaseSchema(Schema):
class Meta:
unknown = RAISE
ordered = True
@post_dump
def remove_none(self, data, **kwargs):
"""Prevents from dumping attributes that are None, thus making the dump more compact."""
return dict((key, value) for key, value in data.items() if value is not None)
class PatchedSchemaMeta(SchemaMeta):
"""Currently there is an open issue in marshmallow, that the "unknown" property is not inherited.
We use a metaclass to inject a Meta class into all our Schema classes.
"""
def __new__(cls, name, bases, dct):
meta = dct.get("Meta")
if meta is None:
dct["Meta"] = PatchedMeta
else:
if not hasattr(meta, "unknown"):
dct["Meta"].unknown = RAISE
if not hasattr(meta, "ordered"):
dct["Meta"].ordered = True
if PatchedBaseSchema not in bases:
bases = bases + (PatchedBaseSchema,)
return super().__new__(cls, name, bases, dct)
class PathAwareSchema(PatchedBaseSchema, metaclass=PatchedSchemaMeta):
schema_ignored = fields.Str(data_key="$schema", dump_only=True)
def __init__(self, *args, **kwargs):
# this will make context of all PathAwareSchema child class point to one object
self.context = kwargs.get("context", None)
if self.context is None or self.context.get(BASE_PATH_CONTEXT_KEY, None) is None:
raise Exception("Base path for reading files is required when building PathAwareSchema")
# set old base path, note it's an Path object and point to the same object with
# self.context.get(BASE_PATH_CONTEXT_KEY)
self.old_base_path = self.context.get(BASE_PATH_CONTEXT_KEY)
super().__init__(*args, **kwargs)
@pre_load
def add_param_overrides(self, data, **kwargs):
# Removing params override from context so that overriding is done once on the yaml
# child schema should not override the params.
params_override = self.context.pop(PARAMS_OVERRIDE_KEY, None)
if params_override is not None:
for override in params_override:
for param, val in override.items():
# Check that none of the intermediary levels are string references (azureml/file)
param_tokens = param.split(".")
test_layer = data
for layer in param_tokens:
if test_layer is None:
continue
if isinstance(test_layer, str):
raise Exception(
f"Cannot use '--set' on properties defined by reference strings: --set {param}"
)
test_layer = test_layer.get(layer, None)
objects.set_(data, param, val)
return data
@pre_load
def trim_dump_only(self, data, **kwargs):
"""Marshmallow raises if dump_only fields are present in the schema. This is not desirable for our use case,
where read-only properties can be present in the yaml, and should simply be ignored, while we should raise in.
the case an unknown field is present - to prevent typos.
"""
if isinstance(data, str) or data is None:
return data
for key, value in self.fields.items(): # pylint: disable=no-member
if value.dump_only:
schema_key = value.data_key or key
if data.get(schema_key, None) is not None:
data.pop(schema_key)
return data
class YamlFileSchema(PathAwareSchema):
"""Base class that allows derived classes to be built from paths to separate yaml files in place of inline yaml
definitions.
This will be transparent to any parent schema containing a nested schema of the derived class, it will not need a
union type for the schema, a YamlFile string will be resolved by the pre_load method into a dictionary. On loading
the child yaml, update the base path to use for loading sub-child files.
"""
def __init__(self, *args, **kwargs):
self._previous_base_path = None
super().__init__(*args, **kwargs)
@classmethod
def _resolve_path(cls, data, base_path) -> Optional[Path]:
if isinstance(data, str) and data.startswith(FILE_PREFIX):
# Use directly if absolute path
path = Path(data[len(FILE_PREFIX) :])
if not path.is_absolute():
path = Path(base_path) / path
path.resolve()
return path
return None
@pre_load
def load_from_file(self, data, **kwargs):
path = self._resolve_path(data, Path(self.context[BASE_PATH_CONTEXT_KEY]))
if path is not None:
self._previous_base_path = Path(self.context[BASE_PATH_CONTEXT_KEY])
# Push update
# deepcopy self.context[BASE_PATH_CONTEXT_KEY] to update old base path
self.old_base_path = copy.deepcopy(self.context[BASE_PATH_CONTEXT_KEY])
self.context[BASE_PATH_CONTEXT_KEY] = path.parent
data = load_yaml(path)
return data
return data
# Schemas are read depth-first, so push/pop to update current path
@post_load
def reset_base_path_post_load(self, data, **kwargs):
if self._previous_base_path is not None:
# pop state
self.context[BASE_PATH_CONTEXT_KEY] = self._previous_base_path
return data
class CreateBySchema(metaclass=PatchedSchemaMeta):
user_object_id = fields.Str(dump_only=True, attribute="userObjectId")
user_tenant_id = fields.Str(dump_only=True, attribute="userTenantId")
user_name = fields.Str(dump_only=True, attribute="userName")
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_fields.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import typing
from pathlib import Path
from marshmallow import fields
from marshmallow.exceptions import FieldInstanceResolutionError, ValidationError
from marshmallow.fields import _T, Field, Nested
from marshmallow.utils import RAISE, resolve_field_instance
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY
from promptflow._sdk.schemas._base import PathAwareSchema
from promptflow._utils.logger_utils import LoggerFactory
# pylint: disable=unused-argument,no-self-use,protected-access
module_logger = LoggerFactory.get_logger(__name__)
class StringTransformedEnum(Field):
def __init__(self, **kwargs):
# pop marshmallow unknown args to avoid warnings
self.allowed_values = kwargs.pop("allowed_values", None)
self.casing_transform = kwargs.pop("casing_transform", lambda x: x.lower())
self.pass_original = kwargs.pop("pass_original", False)
super().__init__(**kwargs)
if isinstance(self.allowed_values, str):
self.allowed_values = [self.allowed_values]
self.allowed_values = [self.casing_transform(x) for x in self.allowed_values]
def _jsonschema_type_mapping(self):
schema = {"type": "string", "enum": self.allowed_values}
if self.name is not None:
schema["title"] = self.name
if self.dump_only:
schema["readonly"] = True
return schema
def _serialize(self, value, attr, obj, **kwargs):
if not value:
return
if isinstance(value, str) and self.casing_transform(value) in self.allowed_values:
return value if self.pass_original else self.casing_transform(value)
raise ValidationError(f"Value {value!r} passed is not in set {self.allowed_values}")
def _deserialize(self, value, attr, data, **kwargs):
if isinstance(value, str) and self.casing_transform(value) in self.allowed_values:
return value if self.pass_original else self.casing_transform(value)
raise ValidationError(f"Value {value!r} passed is not in set {self.allowed_values}")
class LocalPathField(fields.Str):
"""A field that validates that the input is a local path.
Can only be used as fields of PathAwareSchema.
"""
default_error_messages = {
"invalid_path": "The filename, directory name, or volume label syntax is incorrect.",
"path_not_exist": "Can't find {allow_type} in resolved absolute path: {path}.",
}
def __init__(self, allow_dir=True, allow_file=True, **kwargs):
self._allow_dir = allow_dir
self._allow_file = allow_file
self._pattern = kwargs.get("pattern", None)
super().__init__(**kwargs)
def _resolve_path(self, value) -> Path:
"""Resolve path to absolute path based on base_path in context.
Will resolve the path if it's already an absolute path.
"""
try:
result = Path(value)
base_path = Path(self.context[BASE_PATH_CONTEXT_KEY])
if not result.is_absolute():
result = base_path / result
# for non-path string like "azureml:/xxx", OSError can be raised in either
# resolve() or is_dir() or is_file()
result = result.resolve()
if (self._allow_dir and result.is_dir()) or (self._allow_file and result.is_file()):
return result
except OSError:
raise self.make_error("invalid_path")
raise self.make_error("path_not_exist", path=result.as_posix(), allow_type=self.allowed_path_type)
@property
def allowed_path_type(self) -> str:
if self._allow_dir and self._allow_file:
return "directory or file"
if self._allow_dir:
return "directory"
return "file"
def _validate(self, value):
# inherited validations like required, allow_none, etc.
super(LocalPathField, self)._validate(value)
if value is None:
return
self._resolve_path(value)
def _serialize(self, value, attr, obj, **kwargs) -> typing.Optional[str]:
# do not block serializing None even if required or not allow_none.
if value is None:
return None
# always dump path as absolute path in string as base_path will be dropped after serialization
return super(LocalPathField, self)._serialize(self._resolve_path(value).as_posix(), attr, obj, **kwargs)
def _deserialize(self, value, attr, data, **kwargs):
# resolve to absolute path
if value is None:
return None
return super()._deserialize(self._resolve_path(value).as_posix(), attr, data, **kwargs)
# Note: Currently contains a bug where the order in which fields are inputted can potentially cause a bug
# Example, the first line below works, but the second one fails upon calling load_from_dict
# with the error " AttributeError: 'list' object has no attribute 'get'"
# inputs = UnionField([fields.List(NestedField(DataSchema)), NestedField(DataSchema)])
# inputs = UnionField([NestedField(DataSchema), fields.List(NestedField(DataSchema))])
class UnionField(fields.Field):
def __init__(self, union_fields: typing.List[fields.Field], is_strict=False, **kwargs):
super().__init__(**kwargs)
try:
# add the validation and make sure union_fields must be subclasses or instances of
# marshmallow.base.FieldABC
self._union_fields = [resolve_field_instance(cls_or_instance) for cls_or_instance in union_fields]
# TODO: make serialization/de-serialization work in the same way as json schema when is_strict is True
self.is_strict = is_strict # S\When True, combine fields with oneOf instead of anyOf at schema generation
except FieldInstanceResolutionError as error:
raise ValueError(
'Elements of "union_fields" must be subclasses or ' "instances of marshmallow.base.FieldABC."
) from error
@property
def union_fields(self):
return iter(self._union_fields)
def insert_union_field(self, field):
self._union_fields.insert(0, field)
# This sets the parent for the schema and also handles nesting.
def _bind_to_schema(self, field_name, schema):
super()._bind_to_schema(field_name, schema)
self._union_fields = self._create_bind_fields(self._union_fields, field_name)
def _create_bind_fields(self, _fields, field_name):
new_union_fields = []
for field in _fields:
field = copy.deepcopy(field)
field._bind_to_schema(field_name, self)
new_union_fields.append(field)
return new_union_fields
def _serialize(self, value, attr, obj, **kwargs):
if value is None:
return None
errors = []
for field in self._union_fields:
try:
return field._serialize(value, attr, obj, **kwargs)
except ValidationError as e:
errors.extend(e.messages)
except (TypeError, ValueError, AttributeError) as e:
errors.extend([str(e)])
raise ValidationError(message=errors, field_name=attr)
def _deserialize(self, value, attr, data, **kwargs):
errors = []
for schema in self._union_fields:
try:
return schema.deserialize(value, attr, data, **kwargs)
except ValidationError as e:
errors.append(e.normalized_messages())
except (FileNotFoundError, TypeError) as e:
errors.append([str(e)])
finally:
# Revert base path to original path when job schema fail to deserialize job. For example, when load
# parallel job with component file reference starting with FILE prefix, maybe first CommandSchema will
# load component yaml according to AnonymousCommandComponentSchema, and YamlFileSchema will update base
# path. When CommandSchema fail to load, then Parallelschema will load component yaml according to
# AnonymousParallelComponentSchema, but base path now is incorrect, and will raise path not found error
# when load component yaml file.
if (
hasattr(schema, "name")
and schema.name == "jobs"
and hasattr(schema, "schema")
and isinstance(schema.schema, PathAwareSchema)
):
# use old base path to recover original base path
schema.schema.context[BASE_PATH_CONTEXT_KEY] = schema.schema.old_base_path
# recover base path of parent schema
schema.context[BASE_PATH_CONTEXT_KEY] = schema.schema.context[BASE_PATH_CONTEXT_KEY]
raise ValidationError(errors, field_name=attr)
class NestedField(Nested):
"""anticipates the default coming in next marshmallow version, unknown=True."""
def __init__(self, *args, **kwargs):
if kwargs.get("unknown") is None:
kwargs["unknown"] = RAISE
super().__init__(*args, **kwargs)
class DumpableIntegerField(fields.Integer):
"""An int field that cannot serialize other type of values to int if self.strict."""
def _serialize(self, value, attr, obj, **kwargs) -> typing.Optional[typing.Union[str, _T]]:
if self.strict and not isinstance(value, int):
# this implementation can serialize bool to bool
raise self.make_error("invalid", input=value)
return super()._serialize(value, attr, obj, **kwargs)
class DumpableFloatField(fields.Float):
"""A float field that cannot serialize other type of values to float if self.strict."""
def __init__(
self,
*,
strict: bool = False,
allow_nan: bool = False,
as_string: bool = False,
**kwargs,
):
self.strict = strict
super().__init__(allow_nan=allow_nan, as_string=as_string, **kwargs)
def _validated(self, value):
if self.strict and not isinstance(value, float):
raise self.make_error("invalid", input=value)
return super()._validated(value)
def _serialize(self, value, attr, obj, **kwargs) -> typing.Optional[typing.Union[str, _T]]:
return super()._serialize(self._validated(value), attr, obj, **kwargs)
def PrimitiveValueField(**kwargs):
"""Function to return a union field for primitive value.
:return: The primitive value field
:rtype: Field
"""
return UnionField(
[
# Note: order matters here - to make sure value parsed correctly.
# By default, when strict is false, marshmallow downcasts float to int.
# Setting it to true will throw a validation error when loading a float to int.
# https://github.com/marshmallow-code/marshmallow/pull/755
# Use DumpableIntegerField to make sure there will be validation error when
# loading/dumping a float to int.
# note that this field can serialize bool instance but cannot deserialize bool instance.
DumpableIntegerField(strict=True),
# Use DumpableFloatField with strict of True to avoid '1'(str) serialized to 1.0(float)
DumpableFloatField(strict=True),
# put string schema after Int and Float to make sure they won't dump to string
fields.Str(),
# fields.Bool comes last since it'll parse anything non-falsy to True
fields.Bool(),
],
**kwargs,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_connection.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
from marshmallow import ValidationError, fields, pre_dump, validates
from promptflow._sdk._constants import (
SCHEMA_KEYS_CONTEXT_CONFIG_KEY,
SCHEMA_KEYS_CONTEXT_SECRET_KEY,
ConnectionType,
CustomStrongTypeConnectionConfigs,
)
from promptflow._sdk.schemas._base import YamlFileSchema
from promptflow._sdk.schemas._fields import StringTransformedEnum
from promptflow._utils.utils import camel_to_snake
def _casting_type(typ):
type_dict = {
ConnectionType.AZURE_OPEN_AI: "azure_open_ai",
ConnectionType.OPEN_AI: "open_ai",
}
if typ in type_dict:
return type_dict.get(typ)
return camel_to_snake(typ)
class ConnectionSchema(YamlFileSchema):
name = fields.Str(attribute="name")
module = fields.Str(dump_default="promptflow.connections")
created_date = fields.Str(dump_only=True)
last_modified_date = fields.Str(dump_only=True)
expiry_time = fields.Str(dump_only=True)
@pre_dump
def _pre_dump(self, data, **kwargs):
from promptflow._sdk.entities._connection import _Connection
if not isinstance(data, _Connection):
return data
# Update the type replica of the connection object to match schema
copied = copy.deepcopy(data)
copied.type = camel_to_snake(copied.type)
return copied
class AzureOpenAIConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(allowed_values="azure_open_ai", required=True)
api_key = fields.Str(required=True)
api_base = fields.Str(required=True)
api_type = fields.Str(dump_default="azure")
api_version = fields.Str(dump_default="2023-07-01-preview")
class OpenAIConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(allowed_values="open_ai", required=True)
api_key = fields.Str(required=True)
organization = fields.Str()
base_url = fields.Str()
class EmbeddingStoreConnectionSchema(ConnectionSchema):
module = fields.Str(dump_default="promptflow_vectordb.connections")
api_key = fields.Str(required=True)
api_base = fields.Str(required=True)
class QdrantConnectionSchema(EmbeddingStoreConnectionSchema):
type = StringTransformedEnum(allowed_values=camel_to_snake(ConnectionType.QDRANT), required=True)
class WeaviateConnectionSchema(EmbeddingStoreConnectionSchema):
type = StringTransformedEnum(allowed_values=camel_to_snake(ConnectionType.WEAVIATE), required=True)
class CognitiveSearchConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(
allowed_values=camel_to_snake(ConnectionType.COGNITIVE_SEARCH),
required=True,
)
api_key = fields.Str(required=True)
api_base = fields.Str(required=True)
api_version = fields.Str(dump_default="2023-07-01-Preview")
class SerpConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(allowed_values=camel_to_snake(ConnectionType.SERP), required=True)
api_key = fields.Str(required=True)
class AzureContentSafetyConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(
allowed_values=camel_to_snake(ConnectionType.AZURE_CONTENT_SAFETY),
required=True,
)
api_key = fields.Str(required=True)
endpoint = fields.Str(required=True)
api_version = fields.Str(dump_default="2023-10-01")
api_type = fields.Str(dump_default="Content Safety")
class FormRecognizerConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(
allowed_values=camel_to_snake(ConnectionType.FORM_RECOGNIZER),
required=True,
)
api_key = fields.Str(required=True)
endpoint = fields.Str(required=True)
api_version = fields.Str(dump_default="2023-07-31")
api_type = fields.Str(dump_default="Form Recognizer")
class CustomConnectionSchema(ConnectionSchema):
type = StringTransformedEnum(allowed_values=camel_to_snake(ConnectionType.CUSTOM), required=True)
configs = fields.Dict(keys=fields.Str(), values=fields.Str())
# Secrets is a must-have field for CustomConnection
secrets = fields.Dict(keys=fields.Str(), values=fields.Str(), required=True)
class CustomStrongTypeConnectionSchema(CustomConnectionSchema):
name = fields.Str(attribute="name")
module = fields.Str(required=True)
custom_type = fields.Str(required=True)
package = fields.Str(required=True)
package_version = fields.Str(required=True)
# TODO: validate configs and secrets
@validates("configs")
def validate_configs(self, value):
schema_config_keys = self.context.get(SCHEMA_KEYS_CONTEXT_CONFIG_KEY, None)
if schema_config_keys:
for key in value:
if CustomStrongTypeConnectionConfigs.is_custom_key(key) and key not in schema_config_keys:
raise ValidationError(f"Invalid config key {key}, please check the schema.")
@validates("secrets")
def validate_secrets(self, value):
schema_secret_keys = self.context.get(SCHEMA_KEYS_CONTEXT_SECRET_KEY, None)
if schema_secret_keys:
for key in value:
if key not in schema_secret_keys:
raise ValidationError(f"Invalid secret key {key}, please check the schema.")
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/_flow.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from marshmallow import fields, validate
from promptflow._sdk._constants import FlowType
from promptflow._sdk.schemas._base import PatchedSchemaMeta, YamlFileSchema
from promptflow._sdk.schemas._fields import LocalPathField, NestedField
class FlowInputSchema(metaclass=PatchedSchemaMeta):
"""Schema for flow input."""
type = fields.Str(required=True)
description = fields.Str()
# Note: default attribute default can be various types, so we use Raw type here,
# but when transforming to json schema, there is no equivalent type, it will become string type
# may need to delete the default type in the generated json schema to avoid false alarm
default = fields.Raw()
is_chat_input = fields.Bool()
is_chat_history = fields.Bool()
class FlowOutputSchema(metaclass=PatchedSchemaMeta):
"""Schema for flow output."""
type = fields.Str(required=True)
reference = fields.Str()
description = fields.Str()
is_chat_output = fields.Bool()
class BaseFlowSchema(YamlFileSchema):
"""Base schema for flow."""
additional_includes = fields.List(fields.Str())
environment = fields.Dict()
# metadata
type = fields.Str(validate=validate.OneOf(FlowType.get_all_values()))
language = fields.Str()
description = fields.Str()
display_name = fields.Str()
tags = fields.Dict(keys=fields.Str(), values=fields.Str())
class FlowSchema(BaseFlowSchema):
"""Schema for flow dag."""
inputs = fields.Dict(keys=fields.Str(), values=NestedField(FlowInputSchema))
outputs = fields.Dict(keys=fields.Str(), values=NestedField(FlowOutputSchema))
nodes = fields.List(fields.Dict())
node_variants = fields.Dict(keys=fields.Str(), values=fields.Dict())
class EagerFlowSchema(BaseFlowSchema):
"""Schema for eager flow."""
# path to flow entry file.
path = LocalPathField(required=True)
# entry function
entry = fields.Str(required=True)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/schemas/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/_errors.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow.exceptions import ErrorTarget, UserErrorException
class BadRequest(UserErrorException):
pass
class JsonPayloadRequiredForMultipleInputFields(BadRequest):
pass
class MissingRequiredFlowInput(BadRequest):
pass
class FlowConnectionError(UserErrorException):
pass
class UnsupportedConnectionProvider(FlowConnectionError):
def __init__(self, provider):
super().__init__(
message_format="Unsupported connection provider {provider}, " "supported are 'local' and typing.Callable.",
provider=provider,
target=ErrorTarget.FLOW_INVOKER,
)
class MissingConnectionProvider(FlowConnectionError):
pass
class InvalidConnectionData(FlowConnectionError):
def __init__(self, connection_name):
super().__init__(
message_format="Invalid connection data detected while overriding connection {connection_name}.",
connection_name=connection_name,
target=ErrorTarget.FLOW_INVOKER)
class UnexpectedConnectionProviderReturn(FlowConnectionError):
pass
class MultipleStreamOutputFieldsNotSupported(UserErrorException):
def __init__(self):
super().__init__(
"Multiple stream output fields not supported.",
target=ErrorTarget.SERVING_APP,
)
class NotAcceptable(UserErrorException):
def __init__(self, media_type, supported_media_types):
super().__init__(
message_format="Media type {media_type} in Accept header is not acceptable. "
"Supported media type(s) - {supported_media_types}",
media_type=media_type,
supported_media_types=supported_media_types,
target=ErrorTarget.SERVING_APP,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/app.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import logging
import mimetypes
import os
from pathlib import Path
from typing import Dict
from flask import Flask, g, jsonify, request
from promptflow._sdk._load_functions import load_flow
from promptflow._sdk._serving.extension.extension_factory import ExtensionFactory
from promptflow._sdk._serving.flow_invoker import FlowInvoker
from promptflow._sdk._serving.response_creator import ResponseCreator
from promptflow._sdk._serving.utils import (
enable_monitoring,
get_output_fields_to_remove,
get_sample_json,
handle_error_to_response,
load_request_data,
streaming_response_required,
)
from promptflow._sdk._utils import setup_user_agent_to_operation_context
from promptflow._utils.exception_utils import ErrorResponse
from promptflow._utils.logger_utils import LoggerFactory
from promptflow._version import VERSION
from promptflow.contracts.run_info import Status
from promptflow.exceptions import SystemErrorException
from promptflow.storage._run_storage import DummyRunStorage
from .swagger import generate_swagger
logger = LoggerFactory.get_logger("pfserving-app", target_stdout=True)
DEFAULT_STATIC_PATH = Path(__file__).parent / "static"
USER_AGENT = f"promptflow-local-serving/{VERSION}"
class PromptflowServingApp(Flask):
def init(self, **kwargs):
with self.app_context():
# default to local, can be override when creating the app
self.extension = ExtensionFactory.create_extension(logger, **kwargs)
self.flow_invoker: FlowInvoker = None
# parse promptflow project path
self.project_path = self.extension.get_flow_project_path()
logger.info(f"Project path: {self.project_path}")
self.flow_entity = load_flow(self.project_path)
self.flow = self.flow_entity._init_executable()
# enable environment_variables
environment_variables = kwargs.get("environment_variables", {})
os.environ.update(environment_variables)
default_environment_variables = self.flow.get_environment_variables_with_overrides()
self.set_default_environment_variables(default_environment_variables)
self.flow_name = self.extension.get_flow_name()
self.flow.name = self.flow_name
conn_data_override, conn_name_override = self.extension.get_override_connections(self.flow)
self.connections_override = conn_data_override
self.connections_name_override = conn_name_override
self.flow_monitor = self.extension.get_flow_monitor()
self.connection_provider = self.extension.get_connection_provider()
self.credential = self.extension.get_credential()
self.sample = get_sample_json(self.project_path, logger)
self.init_swagger()
# try to initialize the flow invoker
try:
self.init_invoker_if_not_exist()
except Exception as e:
if self.extension.raise_ex_on_invoker_initialization_failure(e):
raise e
# ensure response has the correct content type
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
setup_user_agent_to_operation_context(self.extension.get_user_agent())
add_default_routes(self)
# register blueprints
blue_prints = self.extension.get_blueprints()
for blue_print in blue_prints:
self.register_blueprint(blue_print)
def init_invoker_if_not_exist(self):
if self.flow_invoker:
return
logger.info("Promptflow executor starts initializing...")
self.flow_invoker = FlowInvoker(
self.project_path,
connection_provider=self.connection_provider,
streaming=streaming_response_required,
raise_ex=False,
connections=self.connections_override,
connections_name_overrides=self.connections_name_override,
# for serving, we don't need to persist intermediate result, this is to avoid memory leak.
storage=DummyRunStorage(),
credential=self.credential,
)
self.flow = self.flow_invoker.flow
# Set the flow name as folder name
self.flow.name = self.flow_name
self.response_fields_to_remove = get_output_fields_to_remove(self.flow, logger)
logger.info("Promptflow executor initializing succeed!")
def init_swagger(self):
self.response_fields_to_remove = get_output_fields_to_remove(self.flow, logger)
self.swagger = generate_swagger(self.flow, self.sample, self.response_fields_to_remove)
def set_default_environment_variables(self, default_environment_variables: Dict[str, str] = None):
if default_environment_variables is None:
return
for key, value in default_environment_variables.items():
if key not in os.environ:
os.environ[key] = value
def add_default_routes(app: PromptflowServingApp):
@app.errorhandler(Exception)
def handle_error(e):
err_resp, resp_code = handle_error_to_response(e, logger)
app.flow_monitor.handle_error(e, resp_code)
return err_resp, resp_code
@app.route("/score", methods=["POST"])
@enable_monitoring
def score():
"""process a flow request in the runtime."""
raw_data = request.get_data()
logger.debug(f"PromptFlow executor received data: {raw_data}")
app.init_invoker_if_not_exist()
if app.flow.inputs.keys().__len__() == 0:
data = {}
logger.info("Flow has no input, request data will be ignored.")
else:
logger.info("Start loading request data...")
data = load_request_data(app.flow, raw_data, logger)
# set context data
g.data = data
g.flow_id = app.flow.id or app.flow.name
run_id = g.get("req_id", None)
# TODO: refine this once we can directly set the input/output log level to DEBUG in flow_invoker.
disable_data_logging = logger.level >= logging.INFO
flow_result = app.flow_invoker.invoke(data, run_id=run_id, disable_input_output_logging=disable_data_logging)
g.flow_result = flow_result
# check flow result, if failed, return error response
if flow_result.run_info.status != Status.Completed:
if flow_result.run_info.error:
err = ErrorResponse(flow_result.run_info.error)
g.err_code = err.innermost_error_code
return jsonify(err.to_simplified_dict()), err.response_code
else:
# in case of run failed but can't find any error, return 500
exception = SystemErrorException("Flow execution failed without error message.")
return jsonify(ErrorResponse.from_exception(exception).to_simplified_dict()), 500
intermediate_output = flow_result.output or {}
# remove evaluation only fields
result_output = {k: v for k, v in intermediate_output.items() if k not in app.response_fields_to_remove}
response_creator = ResponseCreator(
flow_run_result=result_output,
accept_mimetypes=request.accept_mimetypes,
)
app.flow_monitor.setup_streaming_monitor_if_needed(response_creator, data, intermediate_output)
return response_creator.create_response()
@app.route("/swagger.json", methods=["GET"])
def swagger():
"""Get the swagger object."""
return jsonify(app.swagger)
@app.route("/health", methods=["GET"])
def health():
"""Check if the runtime is alive."""
return {"status": "Healthy", "version": VERSION}
@app.route("/version", methods=["GET"])
def version():
"""Check the runtime's version."""
build_info = os.environ.get("BUILD_INFO", "")
try:
build_info_dict = json.loads(build_info)
version = build_info_dict["build_number"]
except Exception:
version = VERSION
return {"status": "Healthy", "build_info": build_info, "version": version}
def create_app(**kwargs):
app = PromptflowServingApp(__name__)
if __name__ != "__main__":
app.logger.handlers = logger.handlers
app.logger.setLevel(logger.level)
app.init(**kwargs)
return app
if __name__ == "__main__":
create_app().run()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import time
import base64
import zlib
from flask import jsonify, request
from promptflow._sdk._serving._errors import (
JsonPayloadRequiredForMultipleInputFields,
MissingRequiredFlowInput,
NotAcceptable,
)
from promptflow._utils.exception_utils import ErrorResponse, ExceptionPresenter
from promptflow.contracts.flow import Flow as FlowContract
from promptflow.exceptions import ErrorTarget
def load_request_data(flow, raw_data, logger):
try:
data = json.loads(raw_data)
except Exception:
input = None
if flow.inputs.keys().__len__() > 1:
# this should only work if there's only 1 input field, otherwise it will fail
# TODO: add a check to make sure there's only 1 input field
message = (
"Promptflow executor received non json data, but there's more than 1 input fields, "
"please use json request data instead."
)
raise JsonPayloadRequiredForMultipleInputFields(message, target=ErrorTarget.SERVING_APP)
if isinstance(raw_data, bytes) or isinstance(raw_data, bytearray):
input = str(raw_data, "UTF-8")
elif isinstance(raw_data, str):
input = raw_data
default_key = list(flow.inputs.keys())[0]
logger.debug(f"Promptflow executor received non json data: {input}, default key: {default_key}")
data = {default_key: input}
return data
def validate_request_data(flow, data):
"""Validate required request data is provided."""
# TODO: Note that we don't have default flow input presently, all of the default is None.
required_inputs = [k for k, v in flow.inputs.items() if v.default is None]
missing_inputs = [k for k in required_inputs if k not in data]
if missing_inputs:
raise MissingRequiredFlowInput(
f"Required input fields {missing_inputs} are missing in request data {data!r}",
target=ErrorTarget.SERVING_APP,
)
def streaming_response_required():
"""Check if streaming response is required."""
return "text/event-stream" in request.accept_mimetypes.values()
def get_sample_json(project_path, logger):
# load swagger sample if exists
sample_file = os.path.join(project_path, "samples.json")
if not os.path.exists(sample_file):
return None
logger.info("Promptflow sample file detected.")
with open(sample_file, "r", encoding="UTF-8") as f:
sample = json.load(f)
return sample
# get evaluation only fields
def get_output_fields_to_remove(flow: FlowContract, logger) -> list:
"""get output fields to remove."""
included_outputs = os.getenv("PROMPTFLOW_RESPONSE_INCLUDED_FIELDS", None)
if included_outputs:
logger.info(f"Response included fields: {included_outputs}")
res = json.loads(included_outputs)
return [k for k, v in flow.outputs.items() if k not in res]
return [k for k, v in flow.outputs.items() if v.evaluation_only]
def handle_error_to_response(e, logger):
presenter = ExceptionPresenter.create(e)
logger.error(f"Promptflow serving app error: {presenter.to_dict()}")
logger.error(f"Promptflow serving error traceback: {presenter.formatted_traceback}")
resp = ErrorResponse(presenter.to_dict())
response_code = resp.response_code
# The http response code for NotAcceptable is 406.
# Currently the error framework does not allow response code overriding,
# we add a check here to override the response code.
# TODO: Consider how to embed this logic into the error framework.
if isinstance(e, NotAcceptable):
response_code = 406
return jsonify(resp.to_simplified_dict()), response_code
def get_pf_serving_env(env_key: str):
if len(env_key) == 0:
return None
value = os.getenv(env_key, None)
if value is None and env_key.startswith("PROMPTFLOW_"):
value = os.getenv(env_key.replace("PROMPTFLOW_", "PF_"), None)
return value
def get_cost_up_to_now(start_time: float):
return (time.time() - start_time) * 1000
def enable_monitoring(func):
func._enable_monitoring = True
return func
def normalize_connection_name(connection_name: str):
return connection_name.replace(" ", "_")
def decode_dict(data: str) -> dict:
# str -> bytes
data = data.encode()
zipped_conns = base64.b64decode(data)
# gzip decode
conns_data = zlib.decompress(zipped_conns, 16 + zlib.MAX_WBITS)
return json.loads(conns_data.decode())
def encode_dict(data: dict) -> str:
# json encode
data = json.dumps(data)
# gzip compress
gzip_compress = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS | 16)
zipped_data = gzip_compress.compress(data.encode()) + gzip_compress.flush()
# base64 encode
b64_data = base64.b64encode(zipped_data)
# bytes -> str
return b64_data.decode()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/swagger.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
from promptflow.contracts.flow import Flow, FlowInputDefinition, FlowOutputDefinition
from promptflow.contracts.tool import ValueType
type_mapping = {
ValueType.INT: "integer",
ValueType.DOUBLE: "number",
ValueType.BOOL: "boolean",
ValueType.STRING: "string",
ValueType.LIST: "array",
ValueType.OBJECT: "object",
ValueType.IMAGE: "object", # Dump as object as portal test page can't handle image now
}
def generate_input_field_schema(input: FlowInputDefinition) -> dict:
field_schema = {"type": type_mapping[input.type]}
if input.description:
field_schema["description"] = input.description
if input.default:
field_schema["default"] = input.default
if input.type == ValueType.OBJECT:
field_schema["additionalProperties"] = {}
if input.type == ValueType.LIST:
field_schema["items"] = {"type": "object", "additionalProperties": {}}
return field_schema
def generate_output_field_schema(output: FlowOutputDefinition) -> dict:
field_schema = {"type": type_mapping[output.type]}
if output.description:
field_schema["description"] = output.description
if output.type == ValueType.OBJECT:
field_schema["additionalProperties"] = {}
if output.type == ValueType.LIST:
field_schema["items"] = {"type": "object", "additionalProperties": {}}
return field_schema
def generate_swagger(flow: Flow, samples, outputs_to_remove: list) -> dict:
"""convert a flow to swagger object."""
swagger = {"openapi": "3.0.0"}
swagger["info"] = {
"title": f"Promptflow[{flow.name}] API",
"version": "1.0.0",
"x-flow-name": str(flow.name),
}
swagger["components"] = {
"securitySchemes": {
"bearerAuth": {
"type": "http",
"scheme": "bearer",
}
}
}
swagger["security"] = [{"bearerAuth": []}]
input_schema = {"type": "object"}
request_body_required = False
if len(flow.inputs) > 0:
input_schema["properties"] = {}
input_schema["required"] = []
request_body_required = True
for name, input in flow.inputs.items():
if input.is_chat_input:
swagger["info"]["x-chat-input"] = name
swagger["info"]["x-flow-type"] = "chat"
if input.is_chat_history:
swagger["info"]["x-chat-history"] = name
input_schema["properties"][name] = generate_input_field_schema(input)
input_schema["required"].append(name)
output_schema = {"type": "object"}
if len(flow.outputs) > 0:
output_schema["properties"] = {}
for name, output in flow.outputs.items():
# skip evaluation only outputs in swagger
# TODO remove this if sdk removed this evaluation_only field
if output.evaluation_only:
continue
if output.is_chat_output:
swagger["info"]["x-chat-output"] = name
if outputs_to_remove and name in outputs_to_remove:
continue
output_schema["properties"][name] = generate_output_field_schema(output)
example = {}
if samples:
if isinstance(samples, list):
example = samples[0]
else:
logging.warning("samples should be a list of dict, but got %s, skipped.", type(samples))
swagger["paths"] = {
"/score": {
"post": {
"summary": f"run promptflow: {flow.name} with an given input",
"requestBody": {
"description": "promptflow input data",
"required": request_body_required,
"content": {
"application/json": {
"schema": input_schema,
"example": example, # need to check this based on the sample data
}
},
},
"responses": {
"200": {
"description": "successful operation",
"content": {
"application/json": {
"schema": output_schema,
}
},
},
"400": {
"description": "Invalid input",
},
"default": {
"description": "unexpected error",
},
},
}
}
}
return swagger
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/flow_invoker.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
from typing import Callable, Union
from promptflow import PFClient
from promptflow._constants import LINE_NUMBER_KEY
from promptflow._sdk._load_functions import load_flow
from promptflow._sdk._serving._errors import UnexpectedConnectionProviderReturn, UnsupportedConnectionProvider
from promptflow._sdk._serving.flow_result import FlowResult
from promptflow._sdk._serving.utils import validate_request_data
from promptflow._sdk._utils import (
dump_flow_result,
get_local_connections_from_executable,
override_connection_config_with_environment_variable,
resolve_connections_environment_variable_reference,
update_environment_variables_with_connections,
)
from promptflow._sdk.entities._connection import _Connection
from promptflow._sdk.entities._flow import Flow
from promptflow._sdk.operations._flow_operations import FlowOperations
from promptflow._utils.logger_utils import LoggerFactory
from promptflow._utils.multimedia_utils import convert_multimedia_data_to_base64, persist_multimedia_data
from promptflow.contracts.flow import Flow as ExecutableFlow
from promptflow.executor import FlowExecutor
from promptflow.storage._run_storage import DefaultRunStorage
class FlowInvoker:
"""
The invoker of a flow.
:param flow: The path of the flow, or the flow loaded by load_flow().
:type flow: [str, ~promptflow._sdk.entities._flow.Flow]
:param connection_provider: The connection provider, defaults to None
:type connection_provider: [str, Callable], optional
:param streaming: The function or bool to determine enable streaming or not, defaults to lambda: False
:type streaming: Union[Callable[[], bool], bool], optional
:param connections: Pre-resolved connections used when executing, defaults to None
:type connections: dict, optional
:param connections_name_overrides: The connection name overrides, defaults to None
Example: ``{"aoai_connection": "azure_open_ai_connection"}``
The node with reference to connection 'aoai_connection' will be resolved to the actual connection 'azure_open_ai_connection'. # noqa: E501
:type connections_name_overrides: dict, optional
:param raise_ex: Whether to raise exception when executing flow, defaults to True
:type raise_ex: bool, optional
"""
def __init__(
self,
flow: [str, Flow],
connection_provider: [str, Callable] = None,
streaming: Union[Callable[[], bool], bool] = False,
connections: dict = None,
connections_name_overrides: dict = None,
raise_ex: bool = True,
**kwargs,
):
self.logger = kwargs.get("logger", LoggerFactory.get_logger("flowinvoker"))
self.flow_entity = flow if isinstance(flow, Flow) else load_flow(source=flow)
self._executable_flow = ExecutableFlow._from_dict(
flow_dag=self.flow_entity.dag, working_dir=self.flow_entity.code
)
self.connections = connections or {}
self.connections_name_overrides = connections_name_overrides or {}
self.raise_ex = raise_ex
self.storage = kwargs.get("storage", None)
self.streaming = streaming if isinstance(streaming, Callable) else lambda: streaming
# Pass dump_to path to dump flow result for extension.
self._dump_to = kwargs.get("dump_to", None)
# The credential is used as an option to override
# DefaultAzureCredential when using workspace connection provider
self._credential = kwargs.get("credential", None)
self._init_connections(connection_provider)
self._init_executor()
self.flow = self.executor._flow
self._dump_file_prefix = "chat" if self._is_chat_flow else "flow"
def _init_connections(self, connection_provider):
self._is_chat_flow, _, _ = FlowOperations._is_chat_flow(self._executable_flow)
connection_provider = "local" if connection_provider is None else connection_provider
if isinstance(connection_provider, str):
self.logger.info(f"Getting connections from pf client with provider {connection_provider}...")
connections_to_ignore = list(self.connections.keys())
connections_to_ignore.extend(self.connections_name_overrides.keys())
# Note: The connection here could be local or workspace, depends on the connection.provider in pf.yaml.
connections = get_local_connections_from_executable(
executable=self._executable_flow,
client=PFClient(config={"connection.provider": connection_provider}, credential=self._credential),
connections_to_ignore=connections_to_ignore,
# fetch connections with name override
connections_to_add=list(self.connections_name_overrides.values()),
)
# use original name for connection with name override
override_name_to_original_name_mapping = {v: k for k, v in self.connections_name_overrides.items()}
for name, conn in connections.items():
if name in override_name_to_original_name_mapping:
self.connections[override_name_to_original_name_mapping[name]] = conn
else:
self.connections[name] = conn
elif isinstance(connection_provider, Callable):
self.logger.info("Getting connections from custom connection provider...")
connection_list = connection_provider()
if not isinstance(connection_list, list):
raise UnexpectedConnectionProviderReturn(
f"Connection provider {connection_provider} should return a list of connections."
)
if any(not isinstance(item, _Connection) for item in connection_list):
raise UnexpectedConnectionProviderReturn(
f"All items returned by {connection_provider} should be connection type, got {connection_list}."
)
# TODO(2824058): support connection provider when executing function
connections = {item.name: item.to_execution_connection_dict() for item in connection_list}
self.connections.update(connections)
else:
raise UnsupportedConnectionProvider(connection_provider)
override_connection_config_with_environment_variable(self.connections)
resolve_connections_environment_variable_reference(self.connections)
update_environment_variables_with_connections(self.connections)
self.logger.info(f"Promptflow get connections successfully. keys: {self.connections.keys()}")
def _init_executor(self):
self.logger.info("Promptflow executor starts initializing...")
storage = None
if self._dump_to:
storage = DefaultRunStorage(base_dir=self._dump_to, sub_dir=Path(".promptflow/intermediate"))
else:
storage = self.storage
self.executor = FlowExecutor._create_from_flow(
flow=self._executable_flow,
working_dir=self.flow_entity.code,
connections=self.connections,
raise_ex=self.raise_ex,
storage=storage,
)
self.executor.enable_streaming_for_llm_flow(self.streaming)
self.logger.info("Promptflow executor initiated successfully.")
def _invoke(self, data: dict, run_id=None, disable_input_output_logging=False):
"""
Process a flow request in the runtime.
:param data: The request data dict with flow input as keys, for example: {"question": "What is ChatGPT?"}.
:type data: dict
:param run_id: The run id of the flow request, defaults to None
:type run_id: str, optional
:return: The result of executor.
:rtype: ~promptflow.executor._result.LineResult
"""
log_data = "<REDACTED>" if disable_input_output_logging else data
self.logger.info(f"Validating flow input with data {log_data!r}")
validate_request_data(self.flow, data)
self.logger.info(f"Execute flow with data {log_data!r}")
# Pass index 0 as extension require for dumped result.
# TODO: Remove this index after extension remove this requirement.
result = self.executor.exec_line(data, index=0, run_id=run_id, allow_generator_output=self.streaming())
if LINE_NUMBER_KEY in result.output:
# Remove line number from output
del result.output[LINE_NUMBER_KEY]
return result
def invoke(self, data: dict, run_id=None, disable_input_output_logging=False):
"""
Process a flow request in the runtime and return the output of the executor.
:param data: The request data dict with flow input as keys, for example: {"question": "What is ChatGPT?"}.
:type data: dict
:return: The flow output dict, for example: {"answer": "ChatGPT is a chatbot."}.
:rtype: dict
"""
result = self._invoke(data, run_id=run_id, disable_input_output_logging=disable_input_output_logging)
# Get base64 for multi modal object
resolved_outputs = self._convert_multimedia_data_to_base64(result)
self._dump_invoke_result(result)
log_outputs = "<REDACTED>" if disable_input_output_logging else result.output
self.logger.info(f"Flow run result: {log_outputs}")
if not self.raise_ex:
# If raise_ex is False, we will return the trace flow & node run info.
return FlowResult(
output=resolved_outputs or {},
run_info=result.run_info,
node_run_infos=result.node_run_infos,
)
return resolved_outputs
def _convert_multimedia_data_to_base64(self, invoke_result):
resolved_outputs = {
k: convert_multimedia_data_to_base64(v, with_type=True, dict_type=True)
for k, v in invoke_result.output.items()
}
return resolved_outputs
def _dump_invoke_result(self, invoke_result):
if self._dump_to:
invoke_result.output = persist_multimedia_data(
invoke_result.output, base_dir=self._dump_to, sub_dir=Path(".promptflow/output")
)
dump_flow_result(flow_folder=self._dump_to, flow_result=invoke_result, prefix=self._dump_file_prefix)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/flow_result.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from typing import Mapping, Any
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
@dataclass
class FlowResult:
"""The result of a flow call."""
output: Mapping[str, Any]
# trace info of the flow run.
run_info: FlowRunInfo
node_run_infos: Mapping[str, NodeRunInfo]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/response_creator.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import time
from types import GeneratorType
from flask import Response, jsonify
from werkzeug.datastructures import MIMEAccept
from promptflow._sdk._serving._errors import MultipleStreamOutputFieldsNotSupported, NotAcceptable
class ResponseCreator:
"""Generates http response from flow run result."""
def __init__(
self,
flow_run_result,
accept_mimetypes,
stream_start_callback_func=None,
stream_end_callback_func=None,
stream_event_callback_func=None
):
# Fields that are with GeneratorType are streaming outputs.
stream_fields = [k for k, v in flow_run_result.items() if isinstance(v, GeneratorType)]
if len(stream_fields) > 1:
raise MultipleStreamOutputFieldsNotSupported()
self.stream_field_name = stream_fields[0] if stream_fields else None
self.stream_iterator = flow_run_result.pop(self.stream_field_name, None)
self.non_stream_fields = flow_run_result
# According to RFC2616, if "Accept" header is not specified,
# then it is assumed that the client accepts all media types.
# Set */* as the default value here.
# https://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html
if not accept_mimetypes:
accept_mimetypes = MIMEAccept([("*/*", 1)])
self.accept_mimetypes = accept_mimetypes
self._on_stream_start = stream_start_callback_func
self._on_stream_end = stream_end_callback_func
self._on_stream_event = stream_event_callback_func
@property
def has_stream_field(self):
return self.stream_field_name is not None
@property
def text_stream_specified_explicitly(self):
"""Returns True only when text/event-stream is specified explicitly.
For other cases like */* or text/*, it will return False.
"""
return "text/event-stream" in self.accept_mimetypes.values()
@property
def accept_json(self):
"""Returns True if the Accept header includes application/json.
It also returns True when specified with */* or application/*.
"""
return self.accept_mimetypes.accept_json
def create_text_stream_response(self):
def format_event(data):
return f"data: {json.dumps(data)}\n\n"
def generate():
start_time = time.time()
if self._on_stream_start:
self._on_stream_start()
# If there are non streaming fields, yield them firstly.
if self.non_stream_fields:
yield format_event(self.non_stream_fields)
# If there is stream field, read and yield data until the end.
if self.stream_iterator is not None:
for chunk in self.stream_iterator:
if self._on_stream_event:
self._on_stream_event(chunk)
yield format_event({self.stream_field_name: chunk})
if self._on_stream_end:
duration = (time.time() - start_time) * 1000
self._on_stream_end(duration)
return Response(generate(), mimetype="text/event-stream")
def create_json_response(self):
# If there is stream field, iterate over it and get the merged result.
if self.stream_iterator is not None:
merged_text = "".join(self.stream_iterator)
self.non_stream_fields[self.stream_field_name] = merged_text
return jsonify(self.non_stream_fields)
def create_response(self):
if self.has_stream_field:
if self.text_stream_specified_explicitly:
return self.create_text_stream_response()
elif self.accept_json:
return self.create_json_response()
else:
raise NotAcceptable(
media_type=self.accept_mimetypes, supported_media_types="text/event-stream, application/json"
)
else:
if self.accept_json:
return self.create_json_response()
else:
raise NotAcceptable(media_type=self.accept_mimetypes, supported_media_types="application/json")
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/static_web_blueprint.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import flask
from jinja2 import Template
from pathlib import Path
from flask import Blueprint, request, url_for, current_app as app
def construct_staticweb_blueprint(static_folder):
"""Construct static web blueprint."""
staticweb_blueprint = Blueprint('staticweb_blueprint', __name__, static_folder=static_folder)
@staticweb_blueprint.route("/", methods=["GET", "POST"])
def home():
"""Show the home page."""
index_path = Path(static_folder) / "index.html" if static_folder else None
if index_path and index_path.exists():
template = Template(open(index_path, "r", encoding="UTF-8").read())
return flask.render_template(template, url_for=url_for)
else:
return "<h1>Welcome to promptflow app.</h1>"
@staticweb_blueprint.route("/<path:path>", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
def notfound(path):
rules = {rule.rule: rule.methods for rule in app.url_map.iter_rules()}
if path not in rules or request.method not in rules[path]:
unsupported_message = (
f"The requested api {path!r} with {request.method} is not supported by current app, "
f"if you entered the URL manually please check your spelling and try again."
)
return unsupported_message, 404
return staticweb_blueprint
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/monitor_blueprint.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from flask import Blueprint, current_app as app, request
from promptflow._sdk._serving.monitor.flow_monitor import FlowMonitor
def is_monitoring_enabled() -> bool:
enabled = False
if request.endpoint in app.view_functions:
view_func = app.view_functions[request.endpoint]
enabled = hasattr(view_func, "_enable_monitoring")
return enabled
def construct_monitor_blueprint(flow_monitor: FlowMonitor):
"""Construct monitor blueprint."""
monitor_blueprint = Blueprint("monitor_blueprint", __name__)
@monitor_blueprint.before_app_request
def start_monitoring():
if not is_monitoring_enabled():
return
flow_monitor.start_monitoring()
@monitor_blueprint.after_app_request
def finish_monitoring(response):
if not is_monitoring_enabled():
return response
flow_monitor.finish_monitoring(response.status_code)
return response
return monitor_blueprint
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/static/index.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Local Server Test App</title>
<style>
html,
body {
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 0;
margin: 0;
}
#root {
height: 100%;
width: 100%;
display: flex;
}
</style>
<script type="module" crossorigin src="/static/index.js"></script>
</head>
<body>
<div id="root"></div>
<script>
const time = new Date().toISOString();
const now = performance.now();
console.log("[perf " + time + " " + now + "]" + " load script start");
</script>
</body>
</html>
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/static/index.js | var eG=Object.defineProperty;var tG=(e,t,n)=>t in e?eG(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var nG=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports);var gf=(e,t,n)=>(tG(e,typeof t!="symbol"?t+"":t,n),n);var pxe=nG((Axe,rv)=>{function rG(e,t){for(var n=0;n<t.length;n++){const r=t[n];if(typeof r!="string"&&!Array.isArray(r)){for(const o in r)if(o!=="default"&&!(o in e)){const i=Object.getOwnPropertyDescriptor(r,o);i&&Object.defineProperty(e,o,i.get?i:{enumerable:!0,get:()=>r[o]})}}}return Object.freeze(Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}))}(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const o of document.querySelectorAll('link[rel="modulepreload"]'))r(o);new MutationObserver(o=>{for(const i of o)if(i.type==="childList")for(const a of i.addedNodes)a.tagName==="LINK"&&a.rel==="modulepreload"&&r(a)}).observe(document,{childList:!0,subtree:!0});function n(o){const i={};return o.integrity&&(i.integrity=o.integrity),o.referrerPolicy&&(i.referrerPolicy=o.referrerPolicy),o.crossOrigin==="use-credentials"?i.credentials="include":o.crossOrigin==="anonymous"?i.credentials="omit":i.credentials="same-origin",i}function r(o){if(o.ep)return;o.ep=!0;const i=n(o);fetch(o.href,i)}})();var Mf=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function xr(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var XN={exports:{}},H0={};/*
object-assign
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tC={themeDarker:"#004578",themeDark:"#005a9e",themeDarkAlt:"#106ebe",themePrimary:"#0078d4",themeSecondary:"#2b88d8",themeTertiary:"#71afe5",themeLight:"#c7e0f4",themeLighter:"#deecf9",themeLighterAlt:"#eff6fc",black:"#000000",blackTranslucent40:"rgba(0,0,0,.4)",neutralDark:"#201f1e",neutralPrimary:"#323130",neutralPrimaryAlt:"#3b3a39",neutralSecondary:"#605e5c",neutralSecondaryAlt:"#8a8886",neutralTertiary:"#a19f9d",neutralTertiaryAlt:"#c8c6c4",neutralQuaternary:"#d2d0ce",neutralQuaternaryAlt:"#e1dfdd",neutralLight:"#edebe9",neutralLighter:"#f3f2f1",neutralLighterAlt:"#faf9f8",accent:"#0078d4",white:"#ffffff",whiteTranslucent40:"rgba(255,255,255,.4)",yellowDark:"#d29200",yellow:"#ffb900",yellowLight:"#fff100",orange:"#d83b01",orangeLight:"#ea4300",orangeLighter:"#ff8c00",redDark:"#a4262c",red:"#e81123",magentaDark:"#5c005c",magenta:"#b4009e",magentaLight:"#e3008c",purpleDark:"#32145a",purple:"#5c2d91",purpleLight:"#b4a0ff",blueDark:"#002050",blueMid:"#00188f",blue:"#0078d4",blueLight:"#00bcf2",tealDark:"#004b50",teal:"#008272",tealLight:"#00b294",greenDark:"#004b1c",green:"#107c10",greenLight:"#bad80a"},od;(function(e){e.depth0="0 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xo;(function(e){e.size10="10px",e.size12="12px",e.size14="14px",e.size16="16px",e.size18="18px",e.size20="20px",e.size24="24px",e.size28="28px",e.size32="32px",e.size42="42px",e.size68="68px",e.mini="10px",e.xSmall="10px",e.small="12px",e.smallPlus="12px",e.medium="14px",e.mediumPlus="16px",e.icon="16px",e.large="18px",e.xLarge="20px",e.xLargePlus="24px",e.xxLarge="28px",e.xxLargePlus="32px",e.superLarge="42px",e.mega="68px"})(xo||(xo={}));var Gn;(function(e){e.light=100,e.semilight=300,e.regular=400,e.semibold=600,e.bold=700})(Gn||(Gn={}));var rC;(function(e){e.xSmall="10px",e.small="12px",e.medium="16px",e.large="20px"})(rC||(rC={}));var SJ="'Segoe UI', -apple-system, BlinkMacSystemFont, 'Roboto', 'Helvetica Neue', sans-serif",xJ="'Segoe UI', 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n=this._root.current,r=this._getDocument();if(!this.props.preventFocusRestoration&&r&&this._lastIndexPath&&(r.activeElement===r.body||r.activeElement===null||r.activeElement===n)){var o=eJ(n,this._lastIndexPath);o?(this._setActiveElement(o,!0),o.focus(),this._setParkedFocus(!1)):this._setParkedFocus(!0)}},t.prototype.componentWillUnmount=function(){delete nh[this._id],this._isInnerZone||(Tf.delete(this),this._windowElement&&Tf.size===0&&this._windowElement.removeEventListener("keydown",t._onKeyDownCapture,!0)),this._root.current&&this._root.current.removeEventListener("blur",this._onBlur,!0),this._activeElement=null,this._defaultFocusElement=null},t.prototype.render=function(){var n=this,r=this.props,o=r.as,i=r.elementType,a=r.rootProps,s=r.ariaDescribedBy,l=r.ariaLabelledBy,u=r.className,d=dJ(this.props,go),h=o||i||"div";this._evaluateFocusBeforeRender();var p=DJ();return T.createElement(h,oe({"aria-labelledby":l,"aria-describedby":s},d,a,{className:nJ(HJ(),u),ref:this._mergedRef(this.props.elementRef,this._root),"data-focuszone-id":this._id,onKeyDown:function(m){return n._onKeyDown(m,p)},onFocus:this._onFocus,onMouseDownCapture:this._onMouseDown}),this.props.children)},t.prototype.focus=function(n){if(n===void 0&&(n=!1),this._root.current)if(!n&&this._root.current.getAttribute(Mm)==="true"&&this._isInnerZone){var r=this._getOwnerZone(this._root.current);if(r!==this._root.current){var o=nh[r.getAttribute(D2)];return!!o&&o.focusElement(this._root.current)}return!1}else{if(!n&&this._activeElement&&R2(this._root.current,this._activeElement)&&$a(this._activeElement))return this._activeElement.focus(),!0;var i=this._root.current.firstChild;return this.focusElement(va(this._root.current,i,!0))}return!1},t.prototype.focusLast=function(){if(this._root.current){var n=this._root.current&&this._root.current.lastChild;return this.focusElement(Li(this._root.current,n,!0,!0,!0))}return!1},t.prototype.focusElement=function(n,r){var o=this.props,i=o.onBeforeFocus,a=o.shouldReceiveFocus;return a&&!a(n)||i&&!i(n)?!1:n?(this._setActiveElement(n,r),this._activeElement&&this._activeElement.focus(),!0):!1},t.prototype.setFocusAlignment=function(n){this._focusAlignment=n},t.prototype._evaluateFocusBeforeRender=function(){var n=this._root.current,r=this._getDocument();if(r){var o=r.activeElement;if(o!==n){var i=R2(n,o,!1);this._lastIndexPath=i?tJ(n,o):void 0}}},t.prototype._setParkedFocus=function(n){var r=this._root.current;r&&this._isParked!==n&&(this._isParked=n,n?(this.props.allowFocusRoot||(this._parkedTabIndex=r.getAttribute("tabindex"),r.setAttribute("tabindex","-1")),r.focus()):this.props.allowFocusRoot||(this._parkedTabIndex?(r.setAttribute("tabindex",this._parkedTabIndex),this._parkedTabIndex=void 0):r.removeAttribute("tabindex")))},t.prototype._setActiveElement=function(n,r){var o=this._activeElement;this._activeElement=n,o&&(qs(o)&&this._updateTabIndexes(o),o.tabIndex=-1),this._activeElement&&((!this._focusAlignment||r)&&this._setFocusAlignment(n,!0,!0),this._activeElement.tabIndex=0)},t.prototype._preventDefaultWhenHandled=function(n){this.props.preventDefaultWhenHandled&&n.preventDefault()},t.prototype._tryInvokeClickForFocusable=function(n,r){var o=n;if(o===this._root.current)return!1;do{if(o.tagName==="BUTTON"||o.tagName==="A"||o.tagName==="INPUT"||o.tagName==="TEXTAREA")return!1;if(this._isImmediateDescendantOfZone(o)&&o.getAttribute(Mm)==="true"&&o.getAttribute(LJ)!=="true")return zJ(o,r),!0;o=Ua(o,oc)}while(o!==this._root.current);return!1},t.prototype._getFirstInnerZone=function(n){if(n=n||this._activeElement||this._root.current,!n)return null;if(qs(n))return nh[n.getAttribute(D2)];for(var r=n.firstElementChild;r;){if(qs(r))return nh[r.getAttribute(D2)];var o=this._getFirstInnerZone(r);if(o)return o;r=r.nextElementSibling}return null},t.prototype._moveFocus=function(n,r,o,i){i===void 0&&(i=!0);var a=this._activeElement,s=-1,l=void 0,u=!1,d=this.props.direction===ao.bidirectional;if(!a||!this._root.current||this._isElementInput(a)&&!this._shouldInputLoseFocus(a,n))return!1;var h=d?a.getBoundingClientRect():null;do if(a=n?va(this._root.current,a):Li(this._root.current,a),d){if(a){var p=a.getBoundingClientRect(),m=r(h,p);if(m===-1&&s===-1){l=a;break}if(m>-1&&(s===-1||m<s)&&(s=m,l=a),s>=0&&m<0)break}}else{l=a;break}while(a);if(l&&l!==this._activeElement)u=!0,this.focusElement(l);else if(this.props.isCircularNavigation&&i)return n?this.focusElement(va(this._root.current,this._root.current.firstElementChild,!0)):this.focusElement(Li(this._root.current,this._root.current.lastElementChild,!0,!0,!0));return u},t.prototype._moveFocusDown=function(){var n=this,r=-1,o=this._focusAlignment.left||this._focusAlignment.x||0;return this._moveFocus(!0,function(i,a){var s=-1,l=Math.floor(a.top),u=Math.floor(i.bottom);return 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eh(n)?l=parseFloat(a.top.toFixed(3))<parseFloat(i.bottom.toFixed(3)):l=parseFloat(a.bottom.toFixed(3))>parseFloat(i.top.toFixed(3)),l&&a.right<=i.right&&r.props.direction!==ao.vertical?s=i.right-a.right:o||(s=th),s},void 0,o)?(this._setFocusAlignment(this._activeElement,!0,!1),!0):!1},t.prototype._moveFocusRight=function(n){var r=this,o=this._shouldWrapFocus(this._activeElement,M2);return this._moveFocus(!eh(n),function(i,a){var s=-1,l;return eh(n)?l=parseFloat(a.bottom.toFixed(3))>parseFloat(i.top.toFixed(3)):l=parseFloat(a.top.toFixed(3))<parseFloat(i.bottom.toFixed(3)),l&&a.left>=i.left&&r.props.direction!==ao.vertical?s=a.left-i.left:o||(s=th),s},void 0,o)?(this._setFocusAlignment(this._activeElement,!0,!1),!0):!1},t.prototype._moveFocusPaging=function(n,r){r===void 0&&(r=!0);var o=this._activeElement;if(!o||!this._root.current||this._isElementInput(o)&&!this._shouldInputLoseFocus(o,n))return!1;var i=LZ(o);if(!i)return!1;var a=-1,s=void 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i=n.getBoundingClientRect(),a=i.left+i.width/2,s=i.top+i.height/2;this._focusAlignment||(this._focusAlignment={left:a,top:s}),r&&(this._focusAlignment.left=a),o&&(this._focusAlignment.top=s)}},t.prototype._isImmediateDescendantOfZone=function(n){return this._getOwnerZone(n)===this._root.current},t.prototype._getOwnerZone=function(n){for(var r=Ua(n,oc);r&&r!==this._root.current&&r!==this._getDocument().body;){if(qs(r))return r;r=Ua(r,oc)}return r},t.prototype._updateTabIndexes=function(n){!this._activeElement&&this.props.defaultTabbableElement&&typeof this.props.defaultTabbableElement=="function"&&(this._activeElement=this.props.defaultTabbableElement(this._root.current)),!n&&this._root.current&&(this._defaultFocusElement=null,n=this._root.current,this._activeElement&&!R2(n,this._activeElement)&&(this._activeElement=null)),this._activeElement&&!$a(this._activeElement)&&(this._activeElement=null);for(var r=n&&n.children,o=0;r&&o<r.length;o++){var 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n&&n.getAttribute("contenteditable")==="true"},t.prototype._isElementInput=function(n){return!!(n&&n.tagName&&(n.tagName.toLowerCase()==="input"||n.tagName.toLowerCase()==="textarea"))},t.prototype._shouldInputLoseFocus=function(n,r){if(!this._processingTabKey&&n&&n.type&&UJ.indexOf(n.type.toLowerCase())>-1){var o=n.selectionStart,i=n.selectionEnd,a=o!==i,s=n.value,l=n.readOnly;if(a||o>0&&!r&&!l||o!==s.length&&r&&!l||this.props.handleTabKey&&!(this.props.shouldInputLoseFocusOnArrowKey&&this.props.shouldInputLoseFocusOnArrowKey(n)))return!1}return!0},t.prototype._shouldWrapFocus=function(n,r){return this.props.checkForNoWrap?JZ(n,r):!0},t.prototype._portalContainsElement=function(n){return n&&!!this._root.current&&VZ(n,this._root.current)},t.prototype._getDocument=function(){return 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eee(t,n,r,o,i,a,s,l,u,d,h,p)},S0=gl(VJ,iR,void 0,{scope:"ContextualMenuItem"}),AT=function(e){yi(t,e);function t(n){var r=e.call(this,n)||this;return r._onItemMouseEnter=function(o){var i=r.props,a=i.item,s=i.onItemMouseEnter;s&&s(a,o,o.currentTarget)},r._onItemClick=function(o){var i=r.props,a=i.item,s=i.onItemClickBase;s&&s(a,o,o.currentTarget)},r._onItemMouseLeave=function(o){var i=r.props,a=i.item,s=i.onItemMouseLeave;s&&s(a,o)},r._onItemKeyDown=function(o){var i=r.props,a=i.item,s=i.onItemKeyDown;s&&s(a,o)},r._onItemMouseMove=function(o){var i=r.props,a=i.item,s=i.onItemMouseMove;s&&s(a,o,o.currentTarget)},r._getSubmenuTarget=function(){},Tb(r),r}return 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o=this._getUniqueKtp(r);if(n?this.persistedKeytips[o.uniqueID]=o:this.keytips[o.uniqueID]=o,this.inKeytipMode||!this.delayUpdatingKeytipChange){var i=n?ja.PERSISTED_KEYTIP_ADDED:ja.KEYTIP_ADDED;Ws.raise(this,i,{keytip:r,uniqueID:o.uniqueID})}return o.uniqueID},e.prototype.update=function(t,n){var r=this.addParentOverflow(t),o=this._getUniqueKtp(r,n),i=this.keytips[n];i&&(o.keytip.visible=i.keytip.visible,this.keytips[n]=o,delete this.sequenceMapping[i.keytip.keySequences.toString()],this.sequenceMapping[o.keytip.keySequences.toString()]=o.keytip,(this.inKeytipMode||!this.delayUpdatingKeytipChange)&&Ws.raise(this,ja.KEYTIP_UPDATED,{keytip:o.keytip,uniqueID:o.uniqueID}))},e.prototype.unregister=function(t,n,r){r===void 0&&(r=!1),r?delete this.persistedKeytips[n]:delete this.keytips[n],!r&&delete this.sequenceMapping[t.keySequences.toString()];var o=r?ja.PERSISTED_KEYTIP_REMOVED:ja.KEYTIP_REMOVED;(this.inKeytipMode||!this.delayUpdatingKeytipChange)&&Ws.raise(this,o,{keytip:t,uniqueID:n})},e.prototype.enterKeytipMode=function(){Ws.raise(this,ja.ENTER_KEYTIP_MODE)},e.prototype.exitKeytipMode=function(){Ws.raise(this,ja.EXIT_KEYTIP_MODE)},e.prototype.getKeytips=function(){var t=this;return Object.keys(this.keytips).map(function(n){return t.keytips[n].keytip})},e.prototype.addParentOverflow=function(t){var n=Yi([],t.keySequences);if(n.pop(),n.length!==0){var r=this.sequenceMapping[n.toString()];if(r&&r.overflowSetSequence)return oe(oe({},t),{overflowSetSequence:r.overflowSetSequence})}return t},e.prototype.menuExecute=function(t,n){Ws.raise(this,ja.PERSISTED_KEYTIP_EXECUTE,{overflowButtonSequences:t,keytipSequences:n})},e.prototype._getUniqueKtp=function(t,n){return n===void 0&&(n=cu()),{keytip:oe({},t),uniqueID:n}},e._instance=new e,e}();function aR(e){return e.reduce(function(t,n){return t+sC+n.split("").join(sC)},tee)}function 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n=e!==null&&e.apply(this,arguments)||this;return n._anchor=T.createRef(),n._getMemoizedMenuButtonKeytipProps=Cr(function(r){return oe(oe({},r),{hasMenu:!0})}),n._getSubmenuTarget=function(){return n._anchor.current?n._anchor.current:void 0},n._onItemClick=function(r){var o=n.props,i=o.item,a=o.onItemClick;a&&a(i,r)},n._renderAriaDescription=function(r,o){return r?T.createElement("span",{id:n._ariaDescriptionId,className:o},r):null},n}return t.prototype.render=function(){var n=this,r=this.props,o=r.item,i=r.classNames,a=r.index,s=r.focusableElementIndex,l=r.totalItemCount,u=r.hasCheckmarks,d=r.hasIcons,h=r.contextualMenuItemAs,p=h===void 0?S0:h,m=r.expandedMenuItemKey,v=r.onItemClick,_=r.openSubMenu,b=r.dismissSubMenu,E=r.dismissMenu,w=o.rel;o.target&&o.target.toLowerCase()==="_blank"&&(w=w||"nofollow noopener noreferrer");var k=ll(o),y=Zr(o,LF),F=Ja(o),C=o.itemProps,A=o.ariaDescription,P=o.keytipProps;P&&k&&(P=this._getMemoizedMenuButtonKeytipProps(P)),A&&(this._ariaDescriptionId=cu());var I=$0(o.ariaDescribedBy,A?this._ariaDescriptionId:void 0,y["aria-describedby"]),j={"aria-describedby":I};return T.createElement("div",null,T.createElement(x0,{keytipProps:o.keytipProps,ariaDescribedBy:I,disabled:F},function(H){return T.createElement("a",oe({},j,y,H,{ref:n._anchor,href:o.href,target:o.target,rel:w,className:i.root,role:"menuitem","aria-haspopup":k||void 0,"aria-expanded":k?o.key===m:void 0,"aria-posinset":s+1,"aria-setsize":l,"aria-disabled":Ja(o),style:o.style,onClick:n._onItemClick,onMouseEnter:n._onItemMouseEnter,onMouseLeave:n._onItemMouseLeave,onMouseMove:n._onItemMouseMove,onKeyDown:k?n._onItemKeyDown:void 0}),T.createElement(p,oe({componentRef:o.componentRef,item:o,classNames:i,index:a,onCheckmarkClick:u&&v?v:void 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H=o.role||F;I&&(this._ariaDescriptionId=cu());var K=$0(o.ariaDescribedBy,I?this._ariaDescriptionId:void 0,j["aria-describedby"]),U={className:i.root,onClick:this._onItemClick,onKeyDown:C?this._onItemKeyDown:void 0,onMouseEnter:this._onItemMouseEnter,onMouseLeave:this._onItemMouseLeave,onMouseDown:function(se){return v?v(o,se):void 0},onMouseMove:this._onItemMouseMove,href:o.href,title:o.title,"aria-label":P,"aria-describedby":K,"aria-haspopup":C||void 0,"aria-expanded":C?o.key===m:void 0,"aria-posinset":s+1,"aria-setsize":l,"aria-disabled":Ja(o),"aria-checked":(H==="menuitemcheckbox"||H==="menuitemradio")&&y?!!k:void 0,"aria-selected":H==="menuitem"&&y?!!k:void 0,role:H,style:o.style},pe=o.keytipProps;return pe&&C&&(pe=this._getMemoizedMenuButtonKeytipProps(pe)),T.createElement(x0,{keytipProps:pe,ariaDescribedBy:K,disabled:Ja(o)},function(se){return T.createElement("button",oe({ref:n._btn},j,U,se),T.createElement(p,oe({componentRef:o.componentRef,item:o,classNames:i,index:a,onCheckmarkClick:u&&_?_:void 0,hasIcons:d,openSubMenu:b,dismissSubMenu:E,dismissMenu:w,getSubmenuTarget:n._getSubmenuTarget},A)),n._renderAriaDescription(I,i.screenReaderText))})},t}(AT),dee=function(e){var t=e.theme,n=e.getClassNames,r=e.className;if(!t)throw new Error("Theme is undefined or null.");if(n){var o=n(t);return{wrapper:[o.wrapper],divider:[o.divider]}}return{wrapper:[{display:"inline-flex",height:"100%",alignItems:"center"},r],divider:[{width:1,height:"100%",backgroundColor:t.palette.neutralTertiaryAlt}]}},hee=ml(),sR=T.forwardRef(function(e,t){var n=e.styles,r=e.theme,o=e.getClassNames,i=e.className,a=hee(n,{theme:r,getClassNames:o,className:i});return T.createElement("span",{className:a.wrapper,ref:t},T.createElement("span",{className:a.divider}))});sR.displayName="VerticalDividerBase";var pee=gl(sR,dee,void 0,{scope:"VerticalDivider"}),mee=500,gee=function(e){yi(t,e);function t(n){var r=e.call(this,n)||this;return r._getMemoizedMenuButtonKeytipProps=Cr(function(o){return oe(oe({},o),{hasMenu:!0})}),r._onItemKeyDown=function(o){var i=r.props,a=i.item,s=i.onItemKeyDown;o.which===qt.enter?(r._executeItemClick(o),o.preventDefault(),o.stopPropagation()):s&&s(a,o)},r._getSubmenuTarget=function(){return r._splitButton},r._renderAriaDescription=function(o,i){return o?T.createElement("span",{id:r._ariaDescriptionId,className:i},o):null},r._onItemMouseEnterPrimary=function(o){var i=r.props,a=i.item,s=i.onItemMouseEnter;s&&s(oe(oe({},a),{subMenuProps:void 0,items:void 0}),o,r._splitButton)},r._onItemMouseEnterIcon=function(o){var i=r.props,a=i.item,s=i.onItemMouseEnter;s&&s(a,o,r._splitButton)},r._onItemMouseMovePrimary=function(o){var i=r.props,a=i.item,s=i.onItemMouseMove;s&&s(oe(oe({},a),{subMenuProps:void 0,items:void 0}),o,r._splitButton)},r._onItemMouseMoveIcon=function(o){var i=r.props,a=i.item,s=i.onItemMouseMove;s&&s(a,o,r._splitButton)},r._onIconItemClick=function(o){var i=r.props,a=i.item,s=i.onItemClickBase;s&&s(a,o,r._splitButton?r._splitButton:o.currentTarget)},r._executeItemClick=function(o){var i=r.props,a=i.item,s=i.executeItemClick,l=i.onItemClick;if(!(a.disabled||a.isDisabled)){if(r._processingTouch&&l)return l(a,o);s&&s(a,o)}},r._onTouchStart=function(o){r._splitButton&&!("onpointerdown"in r._splitButton)&&r._handleTouchAndPointerEvent(o)},r._onPointerDown=function(o){o.pointerType==="touch"&&(r._handleTouchAndPointerEvent(o),o.preventDefault(),o.stopImmediatePropagation())},r._async=new _b(r),r._events=new Ws(r),r}return t.prototype.componentDidMount=function(){this._splitButton&&"onpointerdown"in this._splitButton&&this._events.on(this._splitButton,"pointerdown",this._onPointerDown,!0)},t.prototype.componentWillUnmount=function(){this._async.dispose(),this._events.dispose()},t.prototype.render=function(){var n=this,r=this.props,o=r.item,i=r.classNames,a=r.index,s=r.focusableElementIndex,l=r.totalItemCount,u=r.hasCheckmarks,d=r.hasIcons,h=r.onItemMouseLeave,p=r.expandedMenuItemKey,m=ll(o),v=o.keytipProps;v&&(v=this._getMemoizedMenuButtonKeytipProps(v));var _=o.ariaDescription;return _&&(this._ariaDescriptionId=cu()),T.createElement(x0,{keytipProps:v,disabled:Ja(o)},function(b){return T.createElement("div",{"data-ktp-target":b["data-ktp-target"],ref:function(E){return n._splitButton=E},role:oR(o),"aria-label":o.ariaLabel,className:i.splitContainer,"aria-disabled":Ja(o),"aria-expanded":m?o.key===p:void 0,"aria-haspopup":!0,"aria-describedby":$0(o.ariaDescribedBy,_?n._ariaDescriptionId:void 0,b["aria-describedby"]),"aria-checked":o.isChecked||o.checked,"aria-posinset":s+1,"aria-setsize":l,onMouseEnter:n._onItemMouseEnterPrimary,onMouseLeave:h?h.bind(n,oe(oe({},o),{subMenuProps:null,items:null})):void 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this._splitButtonContainer.current&&this.props.onPointerUp&&this._events.on(this._splitButtonContainer.current,"pointerup",this.props.onPointerUp,!0))},t.prototype.componentDidUpdate=function(n,r){this.props.onAfterMenuDismiss&&!r.menuHidden&&this.state.menuHidden&&this.props.onAfterMenuDismiss()},t.prototype.componentWillUnmount=function(){this._async.dispose(),this._events.dispose()},t.prototype.focus=function(){this._isSplitButton&&this._splitButtonContainer.current?(dd(!0),this._splitButtonContainer.current.focus()):this._buttonElement.current&&(dd(!0),this._buttonElement.current.focus())},t.prototype.dismissMenu=function(){this._dismissMenu()},t.prototype.openMenu=function(n,r){this._openMenu(n,r)},t.prototype._onRenderContent=function(n,r){var o=this,i=this.props,a=n,s=i.menuIconProps,l=i.menuProps,u=i.onRenderIcon,d=u===void 0?this._onRenderIcon:u,h=i.onRenderAriaDescription,p=h===void 0?this._onRenderAriaDescription:h,m=i.onRenderChildren,v=m===void 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* Licensed under the MIT License.
*/let w_;const OC=typeof DOMRect<"u"?DOMRect:class{constructor(e,t,n,r){this.left=e||0,this.top=t||0,this.right=(e||0)+(n||0),this.bottom=(t||0)+(r||0)}};let koe=0;try{document.createTreeWalker(document,NodeFilter.SHOW_ELEMENT),w_=!1}catch{w_=!0}function bl(e){const t=e();let n=t.__tabsterInstanceContext;return n||(n={elementByUId:{},basics:{Promise:t.Promise||void 0,WeakRef:t.WeakRef||void 0},containerBoundingRectCache:{},lastContainerBoundingRectCacheId:0,fakeWeakRefs:[],fakeWeakRefsStarted:!1},t.__tabsterInstanceContext=n),n}function Soe(e){const t=e.__tabsterInstanceContext;t&&(t.elementByUId={},delete t.WeakRef,t.containerBoundingRectCache={},t.containerBoundingRectCacheTimer&&e.clearTimeout(t.containerBoundingRectCacheTimer),t.fakeWeakRefsTimer&&e.clearTimeout(t.fakeWeakRefsTimer),t.fakeWeakRefs=[],delete e.__tabsterInstanceContext)}function xoe(e){const t=e.__tabsterInstanceContext;return new((t==null?void 0:t.basics.WeakMap)||WeakMap)}class gO{constructor(t){this._target=t}deref(){return this._target}static cleanup(t,n){return t._target?n||!Zb(t._target.ownerDocument,t._target)?(delete t._target,!0):!1:!0}}class os{constructor(t,n,r){const o=bl(t);let i;o.WeakRef?i=new o.WeakRef(n):(i=new gO(n),o.fakeWeakRefs.push(i)),this._ref=i,this._data=r}get(){const t=this._ref;let n;return t&&(n=t.deref(),n||delete this._ref),n}getData(){return this._data}}function vO(e,t){const n=bl(e);n.fakeWeakRefs=n.fakeWeakRefs.filter(r=>!gO.cleanup(r,t))}function bO(e){const t=bl(e);t.fakeWeakRefsStarted||(t.fakeWeakRefsStarted=!0,t.WeakRef=Foe(t)),t.fakeWeakRefsTimer||(t.fakeWeakRefsTimer=e().setTimeout(()=>{t.fakeWeakRefsTimer=void 0,vO(e),bO(e)},2*60*1e3))}function Coe(e){const t=bl(e);t.fakeWeakRefsStarted=!1,t.fakeWeakRefsTimer&&(e().clearTimeout(t.fakeWeakRefsTimer),t.fakeWeakRefsTimer=void 0,t.fakeWeakRefs=[])}function Xb(e,t,n){if(t.nodeType!==Node.ELEMENT_NODE)return;const r=w_?n:{acceptNode:n};return e.createTreeWalker(t,NodeFilter.SHOW_ELEMENT,r,!1)}function yO(e,t){let n=t.__tabsterCacheId;const r=bl(e),o=n?r.containerBoundingRectCache[n]:void 0;if(o)return o.rect;const i=t.ownerDocument&&t.ownerDocument.documentElement;if(!i)return new OC;let a=0,s=0,l=i.clientWidth,u=i.clientHeight;if(t!==i){const h=t.getBoundingClientRect();a=Math.max(a,h.left),s=Math.max(s,h.top),l=Math.min(l,h.right),u=Math.min(u,h.bottom)}const d=new OC(a<l?a:-1,s<u?s:-1,a<l?l-a:0,s<u?u-s:0);return n||(n="r-"+ ++r.lastContainerBoundingRectCacheId,t.__tabsterCacheId=n),r.containerBoundingRectCache[n]={rect:d,element:t},r.containerBoundingRectCacheTimer||(r.containerBoundingRectCacheTimer=window.setTimeout(()=>{r.containerBoundingRectCacheTimer=void 0;for(const h of Object.keys(r.containerBoundingRectCache))delete r.containerBoundingRectCache[h].element.__tabsterCacheId;r.containerBoundingRectCache={}},50)),d}function DC(e,t){const n=EO(t);if(n){const r=yO(e,n),o=t.getBoundingClientRect();return o.top>=r.top&&o.bottom<=r.bottom}return!1}function PC(e,t,n){const r=EO(t);if(r){const o=yO(e,r),i=t.getBoundingClientRect();n?r.scrollTop+=i.top-o.top:r.scrollTop+=i.bottom-o.bottom}}function EO(e){const t=e.ownerDocument;if(t){for(let n=e.parentElement;n;n=n.parentElement)if(n.scrollWidth>n.clientWidth||n.scrollHeight>n.clientHeight)return n;return t.documentElement}return null}function Aoe(e){e.__shouldIgnoreFocus=!0}function _O(e){return!!e.__shouldIgnoreFocus}function Noe(e){const t=new Uint32Array(4);if(e.crypto&&e.crypto.getRandomValues)e.crypto.getRandomValues(t);else if(e.msCrypto&&e.msCrypto.getRandomValues)e.msCrypto.getRandomValues(t);else for(let r=0;r<t.length;r++)t[r]=4294967295*Math.random();const n=[];for(let r=0;r<t.length;r++)n.push(t[r].toString(36));return n.push("|"),n.push((++koe).toString(36)),n.push("|"),n.push(Date.now().toString(36)),n.join("")}function Hh(e,t){const n=bl(e);let r=t.__tabsterElementUID;return r||(r=t.__tabsterElementUID=Noe(e())),!n.elementByUId[r]&&Zb(t.ownerDocument,t)&&(n.elementByUId[r]=new os(e,t)),r}function MC(e,t){const n=bl(e);for(const r of Object.keys(n.elementByUId)){const o=n.elementByUId[r],i=o&&o.get();i&&t&&!t.contains(i)||delete n.elementByUId[r]}}function Zb(e,t){var n;return!!(!((n=e==null?void 0:e.body)===null||n===void 0)&&n.contains(t))}function TO(e,t){const n=e.matches||e.matchesSelector||e.msMatchesSelector||e.webkitMatchesSelector;return n&&n.call(e,t)}function wO(e){const t=bl(e);if(t.basics.Promise)return t.basics.Promise;throw new Error("No Promise defined.")}function Foe(e){return e.basics.WeakRef}let Ioe=0;class Jb{constructor(t,n,r){const o=t.getWindow;this._tabster=t,this._element=new os(o,n),this._props={...r},this.id="i"+ ++Ioe}getElement(){return this._element.get()}getProps(){return this._props}setProps(t){this._props={...t}}}class k_{constructor(t,n,r,o){var i;this._focusIn=u=>{const d=this.input;if(this.onFocusIn&&d){const h=es.getLastPhantomFrom()||u.relatedTarget;this.onFocusIn(this,this._isBackward(!0,d,h),h)}},this._focusOut=u=>{this.shouldMoveOut=!1;const d=this.input;if(this.onFocusOut&&d){const h=u.relatedTarget;this.onFocusOut(this,this._isBackward(!1,d,h),h)}};const a=t(),s=a.document.createElement("i");s.tabIndex=0,s.setAttribute("role","none"),s.setAttribute(hO,""),s.setAttribute("aria-hidden","true");const l=s.style;l.position="fixed",l.width=l.height="1px",l.opacity="0.001",l.zIndex="-1",l.setProperty("content-visibility","hidden"),Aoe(s),this.input=s,this.isFirst=r.isFirst,this.isOutside=n,this._isPhantom=(i=r.isPhantom)!==null&&i!==void 0?i:!1,s.addEventListener("focusin",this._focusIn),s.addEventListener("focusout",this._focusOut),s.__tabsterDummyContainer=o,this._isPhantom&&(this._disposeTimer=a.setTimeout(()=>{delete this._disposeTimer,this.dispose()},0),this._clearDisposeTimeout=()=>{this._disposeTimer&&(a.clearTimeout(this._disposeTimer),delete this._disposeTimer),delete this._clearDisposeTimeout})}dispose(){var t;this._clearDisposeTimeout&&this._clearDisposeTimeout();const n=this.input;n&&(delete this.onFocusIn,delete this.onFocusOut,delete this.input,n.removeEventListener("focusin",this._focusIn),n.removeEventListener("focusout",this._focusOut),delete n.__tabsterDummyContainer,(t=n.parentElement)===null||t===void 0||t.removeChild(n))}setTopLeft(t,n){var r;const o=(r=this.input)===null||r===void 0?void 0:r.style;o&&(o.top=`${t}px`,o.left=`${n}px`)}_isBackward(t,n,r){return t&&!r?!this.isFirst:!!(r&&n.compareDocumentPosition(r)&Node.DOCUMENT_POSITION_FOLLOWING)}}const PT={Root:1,Modalizer:2,Mover:3,Groupper:4};class es{constructor(t,n,r,o){this._element=n,this._instance=new Boe(t,n,this,r,o),this.moveOutWithDefaultAction=i=>{var a;(a=this._instance)===null||a===void 0||a.moveOutWithDefaultAction(i)}}_setHandlers(t,n){this._onFocusIn=t,this._onFocusOut=n}getHandler(t){return t?this._onFocusIn:this._onFocusOut}setTabbable(t){var n;(n=this._instance)===null||n===void 0||n.setTabbable(this,t)}dispose(){this._instance&&(this._instance.dispose(this),delete this._instance),delete this._onFocusIn,delete this._onFocusOut}static getLastPhantomFrom(){const t=es._lastPhantomFrom;return delete es._lastPhantomFrom,t}static moveWithPhantomDummy(t,n,r,o){const a=new k_(t.getWindow,!0,{isPhantom:!0,isFirst:!0}).input;if(a){const s=n.parentElement;if(s){let l=r&&!o||!r&&o?n.nextElementSibling:n;if(l)if(o){const u=l.previousElementSibling;u&&u.__tabsterDummyContainer&&(l=u)}else l.__tabsterDummyContainer&&(l=l.nextElementSibling);s.insertBefore(a,l),es._lastPhantomFrom=n,t.getWindow().setTimeout(()=>{delete es._lastPhantomFrom},0),np(a)}}}}class Boe{constructor(t,n,r,o,i){var a;this._wrappers=[],this._isOutside=!1,this._transformElements=[],this._onFocusIn=(d,h,p)=>{this._onFocus(!0,d,h,p)},this._onFocusOut=(d,h,p)=>{this._onFocus(!1,d,h,p)},this.moveOutWithDefaultAction=d=>{const h=this._firstDummy,p=this._lastDummy;h!=null&&h.input&&(p!=null&&p.input)&&(d?(h.shouldMoveOut=!0,h.input.tabIndex=0,h.input.focus()):(p.shouldMoveOut=!0,p.input.tabIndex=0,p.input.focus()))},this.setTabbable=(d,h)=>{var p,m;for(const _ of this._wrappers)if(_.manager===d){_.tabbable=h;break}const v=this._getCurrent();if(v){const _=v.tabbable?0:-1;let b=(p=this._firstDummy)===null||p===void 0?void 0:p.input;b&&(b.tabIndex=_),b=(m=this._lastDummy)===null||m===void 0?void 0:m.input,b&&(b.tabIndex=_)}},this._addTransformOffsets=()=>{const d=this._getWindow();this._scrollTimer&&d.clearTimeout(this._scrollTimer),this._scrollTimer=d.setTimeout(()=>{delete this._scrollTimer,this._reallyAddTransformOffsets()},100)};const s=n.get();if(!s)throw new Error("No element");this._getWindow=t.getWindow;const l=s.__tabsterDummy;if((l||this)._wrappers.push({manager:r,priority:o,tabbable:!0}),l)return l;s.__tabsterDummy=this,this._firstDummy=new k_(this._getWindow,this._isOutside,{isFirst:!0},n),this._lastDummy=new k_(this._getWindow,this._isOutside,{isFirst:!1},n),this._firstDummy.onFocusIn=this._onFocusIn,this._firstDummy.onFocusOut=this._onFocusOut,this._lastDummy.onFocusIn=this._onFocusIn,this._lastDummy.onFocusOut=this._onFocusOut,this._element=n,this._addDummyInputs();const u=(a=n.get())===null||a===void 0?void 0:a.tagName;this._isOutside=(i||u==="UL"||u==="OL"||u==="TABLE")&&!(u==="LI"||u==="TD"||u==="TH"),(typeof process>"u"||{}.NODE_ENV!=="test")&&this._observeMutations()}dispose(t,n){var r,o,i;if((this._wrappers=this._wrappers.filter(s=>s.manager!==t&&!n)).length===0){delete((r=this._element)===null||r===void 0?void 0:r.get()).__tabsterDummy,this._unobserve&&(this._unobserve(),delete this._unobserve);for(const l of this._transformElements)l.removeEventListener("scroll",this._addTransformOffsets);this._transformElements=[];const s=this._getWindow();this._scrollTimer&&(s.clearTimeout(this._scrollTimer),delete this._scrollTimer),this._addTimer&&(s.clearTimeout(this._addTimer),delete this._addTimer),(o=this._firstDummy)===null||o===void 0||o.dispose(),(i=this._lastDummy)===null||i===void 0||i.dispose()}}_onFocus(t,n,r,o){var i;const a=this._getCurrent();a&&((i=a.manager.getHandler(t))===null||i===void 0||i(n,r,o))}_getCurrent(){return this._wrappers.sort((t,n)=>t.tabbable!==n.tabbable?t.tabbable?-1:1:t.priority-n.priority),this._wrappers[0]}_addDummyInputs(){this._addTimer||(this._addTimer=this._getWindow().setTimeout(()=>{var t,n,r;delete this._addTimer;const o=(t=this._element)===null||t===void 0?void 0:t.get(),i=(n=this._firstDummy)===null||n===void 0?void 0:n.input,a=(r=this._lastDummy)===null||r===void 0?void 0:r.input;if(!(!o||!i||!a)){if(this._isOutside){const s=o.parentElement;if(s){const l=o.nextElementSibling;l!==a&&s.insertBefore(a,l),o.previousElementSibling!==i&&s.insertBefore(i,o)}}else{o.lastElementChild!==a&&o.appendChild(a);const s=o.firstElementChild;s&&s!==i&&o.insertBefore(i,s)}this._addTransformOffsets()}},0))}_observeMutations(){var t;if(this._unobserve)return;const n=new MutationObserver(()=>{this._unobserve&&this._addDummyInputs()}),r=(t=this._element)===null||t===void 0?void 0:t.get(),o=this._isOutside?r==null?void 0:r.parentElement:r;o&&(n.observe(o,{childList:!0}),this._unobserve=()=>{n.disconnect()})}_reallyAddTransformOffsets(){var t,n,r,o;const i=((t=this._firstDummy)===null||t===void 0?void 0:t.input)||((n=this._lastDummy)===null||n===void 0?void 0:n.input),a=this._transformElements,s=[],l=new WeakMap,u=new WeakMap;let d=0,h=0;for(const m of a)l.set(m,m);const p=this._getWindow();for(let m=i;m;m=m.parentElement){const v=p.getComputedStyle(m).transform;if(v&&v!=="none"){let _=l.get(m);_||(_=m,_.addEventListener("scroll",this._addTransformOffsets)),s.push(_),u.set(_,_),d+=_.scrollTop,h+=_.scrollLeft}}for(const m of a)u.get(m)||m.removeEventListener("scroll",this._addTransformOffsets);this._transformElements=s,(r=this._firstDummy)===null||r===void 0||r.setTopLeft(d,h),(o=this._lastDummy)===null||o===void 0||o.setTopLeft(d,h)}}function kO(e){let t=null;for(let n=e.lastElementChild;n;n=n.lastElementChild)t=n;return t||void 0}function LC(e,t){let n=e,r=null;for(;n&&!r;)r=t?n.previousElementSibling:n.nextElementSibling,n=n.parentElement;return r||void 0}function Wd(e,t,n){const r=document.createEvent("HTMLEvents");return r.initEvent(t,!0,!0),r.details=n,e.dispatchEvent(r),!r.defaultPrevented}class jC extends es{constructor(t,n,r){super(t,n,PT.Root),this._onDummyInputFocus=o=>{var i;if(o.shouldMoveOut)this._setFocused(!1,!0);else{this._tabster.keyboardNavigation.setNavigatingWithKeyboard(!0);const a=this._element.get();if(a&&(this._setFocused(!0,!0),o.isFirst?this._tabster.focusedElement.focusFirst({container:a}):this._tabster.focusedElement.focusLast({container:a})))return;(i=o.input)===null||i===void 0||i.blur()}},this._setHandlers(this._onDummyInputFocus),this._tabster=t,this._setFocused=r}}class zC extends Jb{constructor(t,n,r,o){super(t,n,o),this._isFocused=!1,this._setFocused=(a,s)=>{if(this._setFocusedTimer&&(this._tabster.getWindow().clearTimeout(this._setFocusedTimer),delete this._setFocusedTimer),this._isFocused===a)return;const l=this._element.get();l&&(a?(this._isFocused=!0,Wd(this._tabster.root.eventTarget,"focus",{element:l,fromAdjacent:s})):this._setFocusedTimer=this._tabster.getWindow().setTimeout(()=>{delete this._setFocusedTimer,this._isFocused=!1,Wd(this._tabster.root.eventTarget,"blur",{element:l,fromAdjacent:s})},0))},this._onFocus=a=>{var s;const l=this._tabster.getWindow();if(this._setTabbableTimer&&(l.clearTimeout(this._setTabbableTimer),delete this._setTabbableTimer),a){const u=Io.getTabsterContext(this._tabster,a);if(u&&this._setFocused(u.root.getElement()===this._element.get()),!u||u.uncontrolled||this._tabster.rootDummyInputs){(s=this._dummyManager)===null||s===void 0||s.setTabbable(!1);return}}else this._setFocused(!1);this._setTabbableTimer=l.setTimeout(()=>{var u;delete this._setTabbableTimer,(u=this._dummyManager)===null||u===void 0||u.setTabbable(!0)},0)},this._onDispose=r;const i=t.getWindow;this.uid=Hh(i,n),(t.controlTab||t.rootDummyInputs)&&(this._dummyManager=new jC(t,this._element,this._setFocused)),t.focusedElement.subscribe(this._onFocus),this._add()}dispose(){var t;this._onDispose(this);const n=this._tabster.getWindow();this._setFocusedTimer&&(n.clearTimeout(this._setFocusedTimer),delete this._setFocusedTimer),this._setTabbableTimer&&(n.clearTimeout(this._setTabbableTimer),delete this._setTabbableTimer),(t=this._dummyManager)===null||t===void 0||t.dispose(),this._remove()}moveOutWithDefaultAction(t){const n=this._dummyManager;if(n)n.moveOutWithDefaultAction(t);else{const r=this.getElement();r&&jC.moveWithPhantomDummy(this._tabster,r,!0,t)}}_add(){}_remove(){}}class Io{constructor(t,n){this._roots={},this.rootById={},this._init=()=>{this._initTimer=void 0},this._onRootDispose=r=>{delete this._roots[r.id]},this._tabster=t,this._win=t.getWindow,this._initTimer=this._win().setTimeout(this._init,0),this._autoRoot=n,this.eventTarget=woe(this._win)}dispose(){const t=this._win();this._autoRootInstance&&(this._autoRootInstance.dispose(),delete this._autoRootInstance,delete this._autoRoot),this._initTimer&&(t.clearTimeout(this._initTimer),this._initTimer=void 0),Object.keys(this._roots).forEach(n=>{this._roots[n]&&(this._roots[n].dispose(),delete this._roots[n])}),this.rootById={}}createRoot(t,n){const r=new zC(this._tabster,t,this._onRootDispose,n);return this._roots[r.id]=r,r}static getRootByUId(t,n){const r=t().__tabsterInstance;return r&&r.root.rootById[n]}static getTabsterContext(t,n,r){r===void 0&&(r={});var o,i,a;if(!n.ownerDocument)return;const s=r.checkRtl;let l,u,d,h,p=!1,m,v,_,b=n;const E={};for(;b&&(!l||s);){const w=Au(t,b);if(s&&v===void 0){const F=b.dir;F&&(v=F.toLowerCase()==="rtl")}if(!w){b=b.parentElement;continue}w.uncontrolled&&(_=b),!h&&(!((o=w.focusable)===null||o===void 0)&&o.excludeFromMover)&&!d&&(p=!0);const k=w.groupper,y=w.mover;!d&&k&&(d=k),!h&&y&&(h=y,m=!!d),!u&&w.modalizer&&(u=w.modalizer),w.root&&(l=w.root),!((i=w.focusable)===null||i===void 0)&&i.ignoreKeydown&&Object.assign(E,w.focusable.ignoreKeydown),b=b.parentElement}if(!l){const w=t.root,k=w._autoRoot;if(k&&!w._autoRootInstance){const y=(a=n.ownerDocument)===null||a===void 0?void 0:a.body;y&&(w._autoRootInstance=new zC(w._tabster,y,w._onRootDispose,k))}l=w._autoRootInstance}return d&&!h&&(m=!0),l?{root:l,modalizer:u,groupper:d,mover:h,isGroupperFirst:m,isRtl:s?!!v:void 0,uncontrolled:_,isExcludedFromMover:p,ignoreKeydown:E}:void 0}onRoot(t,n){n?delete this.rootById[t.uid]:this.rootById[t.uid]=t}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/const A0=10;class Roe{}class Ooe extends Roe{constructor(t,n){super(),this.uid=n.uid,this._tabster=t,this._deloser=n}belongsTo(t){return t===this._deloser}unshift(t){this._deloser.unshift(t)}async focusAvailable(){const t=this._deloser.findAvailable();return t?this._tabster.focusedElement.focus(t):!1}async resetFocus(){const t=this._tabster.getWindow;return wO(t).resolve(this._deloser.resetFocus())}}class Doe{constructor(t,n){this._history=[],this._tabster=t,this.rootUId=n}getLength(){return this._history.length}removeDeloser(t){this._history=this._history.filter(n=>!n.belongsTo(t))}hasDeloser(t){return this._history.some(n=>n.belongsTo(t))}}class Poe extends Doe{unshiftToDeloser(t,n){let r;for(let o=0;o<this._history.length;o++)if(this._history[o].belongsTo(t)){r=this._history[o],this._history.splice(o,1);break}r||(r=new Ooe(this._tabster,t)),r.unshift(n),this._history.unshift(r),this._history.splice(A0,this._history.length-A0)}async focusAvailable(t){let n=!!t;for(const r of this._history)if(t&&r.belongsTo(t)&&(n=!1),!n&&await r.focusAvailable())return!0;return!1}async resetFocus(t){let n=!!t;const r={};for(const o of this._history)t&&o.belongsTo(t)&&(n=!1),!n&&!r[o.uid]&&(r[o.uid]=o);for(const o of Object.keys(r))if(await r[o].resetFocus())return!0;return!1}}class Moe{constructor(t){this._history=[],this._tabster=t}dispose(){this._history=[]}process(t){var n;const r=Io.getTabsterContext(this._tabster,t),o=r&&r.root.uid,i=SO.getDeloser(this._tabster,t);if(!o||!i)return;const a=this.make(o,()=>new Poe(this._tabster,o));return(!r||!r.modalizer||!((n=r.modalizer)===null||n===void 0)&&n.isActive())&&a.unshiftToDeloser(i,t),i}make(t,n){let r;for(let o=0;o<this._history.length;o++){const i=this._history[o];if(i.rootUId===t){r=i,this._history.splice(o,1);break}}return r||(r=n()),this._history.unshift(r),this._history.splice(A0,this._history.length-A0),r}removeDeloser(t){this._history.forEach(n=>{n.removeDeloser(t)}),this._history=this._history.filter(n=>n.getLength()>0)}async focusAvailable(t){let n=!!t;for(const r of this._history)if(t&&r.hasDeloser(t)&&(n=!1),!n&&await r.focusAvailable(t))return!0;return!1}async resetFocus(t){let n=!!t;for(const r of this._history)if(t&&r.hasDeloser(t)&&(n=!1),!n&&await r.resetFocus(t))return!0;return!1}}function HC(e,t,n){const r=[],o=/(:|\.|\[|\]|,|=|@)/g,i="\\$1";e.id&&r.push("#"+e.id.replace(o,i)),t!==!1&&e.className&&e.className.split(" ").forEach(l=>{l=l.trim(),l&&r.push("."+l.replace(o,i))});let a=0,s;if(n!==!1&&r.length===0){for(s=e;s;)a++,s=s.previousElementSibling;r.unshift(":nth-child("+a+")")}return r.unshift(e.tagName.toLowerCase()),r.join("")}function Loe(e){if(!Zb(e.ownerDocument,e))return;const t=[HC(e)];let n=e.parentElement;for(;n;){const r=n.tagName==="BODY";if(t.unshift(HC(n,!1,!r)),r)break;n=n.parentElement}return t.join(" ")}class UC extends Jb{constructor(t,n,r,o){super(t,n,o),this._isActive=!1,this._history=[[]],this._snapshotIndex=0,this.isActive=()=>this._isActive,this.setSnapshot=i=>{this._snapshotIndex=i,this._history.length>i+1&&this._history.splice(i+1,this._history.length-i-1),this._history[i]||(this._history[i]=[])},this.focusFirst=()=>{const i=this._element.get();return!!i&&this._tabster.focusedElement.focusFirst({container:i})},this.focusDefault=()=>{const i=this._element.get();return!!i&&this._tabster.focusedElement.focusDefault(i)},this.resetFocus=()=>{const i=this._element.get();return!!i&&this._tabster.focusedElement.resetFocus(i)},this.clearHistory=i=>{const a=this._element.get();if(!a){this._history[this._snapshotIndex]=[];return}this._history[this._snapshotIndex]=this._history[this._snapshotIndex].filter(s=>{const l=s.get();return l&&i?a.contains(l):!1})},this.uid=Hh(t.getWindow,n),this._onDispose=r}dispose(){this._remove(),this._onDispose(this),this._isActive=!1,this._snapshotIndex=0,this._props={},this._history=[]}setActive(t){this._isActive=t}getActions(){return{focusDefault:this.focusDefault,focusFirst:this.focusFirst,resetFocus:this.resetFocus,clearHistory:this.clearHistory,setSnapshot:this.setSnapshot,isActive:this.isActive}}unshift(t){let n=this._history[this._snapshotIndex];for(n=this._history[this._snapshotIndex]=n.filter(r=>{const o=r.get();return o&&o!==t}),n.unshift(new os(this._tabster.getWindow,t,Loe(t)));n.length>A0;)n.pop()}findAvailable(){const t=this._element.get();if(!t||!this._tabster.focusable.isVisible(t))return null;let n=this._props.restoreFocusOrder,r=null;const o=Io.getTabsterContext(this._tabster,t);if(!o)return null;const i=o.root,a=i.getElement();if(!a)return null;if(n===void 0&&(n=i.getProps().restoreFocusOrder),n===Wf.RootDefault&&(r=this._tabster.focusable.findDefault({container:a})),!r&&n===Wf.RootFirst&&(r=this._findFirst(a)),r)return r;const s=this._findInHistory(),l=this._tabster.focusable.findDefault({container:t}),u=this._findFirst(t);return s&&n===Wf.History?s:l&&n===Wf.DeloserDefault?l:u&&n===Wf.DeloserFirst?u:l||s||u||null}customFocusLostHandler(t){return Wd(t,pO,this.getActions())}_findInHistory(){const t=this._history[this._snapshotIndex].slice(0);this.clearHistory(!0);for(let n=0;n<t.length;n++){const r=t[n],o=r.get(),i=this._element.get();if(o&&i&&i.contains(o)){if(this._tabster.focusable.isFocusable(o))return o}else if(!this._props.noSelectorCheck){const a=r.getData();if(a&&i){let s;try{s=i.ownerDocument.querySelectorAll(a)}catch{continue}for(let l=0;l<s.length;l++){const u=s[l];if(u&&this._tabster.focusable.isFocusable(u))return u}}}}return null}_findFirst(t){if(this._tabster.keyboardNavigation.isNavigatingWithKeyboard()){const n=this._tabster.focusable.findFirst({container:t});if(n)return n}return null}_remove(){}}class SO{constructor(t,n){this._inDeloser=!1,this._isRestoringFocus=!1,this._isPaused=!1,this._init=()=>{this._initTimer=void 0,this._tabster.focusedElement.subscribe(this._onFocus)},this._onFocus=o=>{if(this._restoreFocusTimer&&(this._win().clearTimeout(this._restoreFocusTimer),this._restoreFocusTimer=void 0),!o){this._scheduleRestoreFocus();return}const i=this._history.process(o);i?this._activate(i):this._deactivate()},this._onDeloserDispose=o=>{this._history.removeDeloser(o),o.isActive()&&this._scheduleRestoreFocus()},this._tabster=t,this._win=t.getWindow,this._history=new Moe(t),this._initTimer=this._win().setTimeout(this._init,0);const r=n==null?void 0:n.autoDeloser;r&&(this._autoDeloser=r)}dispose(){const t=this._win();this._initTimer&&(t.clearTimeout(this._initTimer),this._initTimer=void 0),this._restoreFocusTimer&&(t.clearTimeout(this._restoreFocusTimer),this._restoreFocusTimer=void 0),this._autoDeloserInstance&&(this._autoDeloserInstance.dispose(),delete this._autoDeloserInstance,delete this._autoDeloser),this._tabster.focusedElement.unsubscribe(this._onFocus),this._history.dispose(),delete this._curDeloser}createDeloser(t,n){var r;const o=new UC(this._tabster,t,this._onDeloserDispose,n);return t.contains((r=this._tabster.focusedElement.getFocusedElement())!==null&&r!==void 0?r:null)&&this._activate(o),o}getActions(t){for(let n=t;n;n=n.parentElement){const r=Au(this._tabster,n);if(r&&r.deloser)return r.deloser.getActions()}}pause(){this._isPaused=!0,this._restoreFocusTimer&&(this._win().clearTimeout(this._restoreFocusTimer),this._restoreFocusTimer=void 0)}resume(t){this._isPaused=!1,t&&this._scheduleRestoreFocus()}_activate(t){const n=this._curDeloser;n!==t&&(this._inDeloser=!0,n==null||n.setActive(!1),t.setActive(!0),this._curDeloser=t)}_deactivate(){var t;this._inDeloser=!1,(t=this._curDeloser)===null||t===void 0||t.setActive(!1),this._curDeloser=void 0}_scheduleRestoreFocus(t){if(this._isPaused||this._isRestoringFocus)return;const n=async()=>{this._restoreFocusTimer=void 0;const r=this._tabster.focusedElement.getLastFocusedElement();if(!t&&(this._isRestoringFocus||!this._inDeloser||r!=null&&r.offsetParent))return;const o=this._curDeloser;if(o){if(r&&o.customFocusLostHandler(r))return;const i=o.findAvailable();if(i&&this._tabster.focusedElement.focus(i))return}this._deactivate(),this._isRestoringFocus=!0,await this._history.focusAvailable(null)||await this._history.resetFocus(null),this._isRestoringFocus=!1};t?n():this._restoreFocusTimer=this._win().setTimeout(n,100)}static getDeloser(t,n){var r;for(let i=n;i;i=i.parentElement){const a=Au(t,i);if(a&&a.deloser)return a.deloser}const o=t.deloser&&t.deloser;if(o){if(o._autoDeloserInstance)return o._autoDeloserInstance;const i=o._autoDeloser;if(!o._autoDeloserInstance&&i){const a=(r=n.ownerDocument)===null||r===void 0?void 0:r.body;a&&(o._autoDeloserInstance=new UC(t,a,t.deloser._onDeloserDispose,i))}return o._autoDeloserInstance}}static getHistory(t){return t._history}static forceRestoreFocus(t){t._scheduleRestoreFocus(!0)}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/let xO=class{constructor(){this._callbacks=[]}dispose(){this._callbacks=[],delete this._val}subscribe(t){this._callbacks.indexOf(t)<0&&this._callbacks.push(t)}unsubscribe(t){const n=this._callbacks.indexOf(t);n>=0&&this._callbacks.splice(n,1)}setVal(t,n){this._val!==t&&(this._val=t,this._callCallbacks(t,n))}getVal(){return this._val}trigger(t,n){this._callCallbacks(t,n)}_callCallbacks(t,n){this._callbacks.forEach(r=>r(t,n))}};/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/const joe=["a[href]","button:not([disabled])","input:not([disabled])","select:not([disabled])","textarea:not([disabled])","*[tabindex]","*[contenteditable]"].join(", ");class zoe{constructor(t,n){this._tabster=t,this._win=n}dispose(){}_getBody(){const t=this._tabster.focusedElement.getLastFocusedElement();return t&&t.ownerDocument?t.ownerDocument.body:this._win().document.body}getProps(t){const n=Au(this._tabster,t);return n&&n.focusable||{}}isFocusable(t,n,r,o){return TO(t,joe)&&(n||t.tabIndex!==-1)?(r||this.isVisible(t))&&(o||this.isAccessible(t)):!1}isVisible(t){if(!t.ownerDocument||t.offsetParent===null&&t.ownerDocument.body!==t)return!1;const n=t.ownerDocument.defaultView;if(!n)return!1;const r=t.ownerDocument.body.getBoundingClientRect();return!(r.width===0&&r.height===0||n.getComputedStyle(t).visibility==="hidden")}isAccessible(t){var n;for(let r=t;r;r=r.parentElement){const o=Au(this._tabster,r);if(this._isHidden(r)||!((n=o==null?void 0:o.focusable)===null||n===void 0?void 0:n.ignoreAriaDisabled)&&this._isDisabled(r))return!1}return!0}_isDisabled(t){return t.hasAttribute("disabled")}_isHidden(t){const n=t.getAttribute("aria-hidden");return!!(n&&n.toLowerCase()==="true")}findFirst(t){return this.findElement({container:this._getBody(),...t})}findLast(t){return this.findElement({container:this._getBody(),isBackward:!0,...t})}findNext(t){return this.findElement({container:this._getBody(),...t})}findPrev(t){return this.findElement({container:this._getBody(),isBackward:!0,...t})}findDefault(t){return this.findElement({...t,acceptCondition:n=>this._tabster.focusable.isFocusable(n,t.includeProgrammaticallyFocusable)&&!!this.getProps(n).isDefault})||null}findAll(t){return this._findElements(!0,t)||[]}findElement(t){const n=this._findElements(!1,t);return n&&n[0]}_findElements(t,n){const{container:r,currentElement:o=null,includeProgrammaticallyFocusable:i,ignoreUncontrolled:a,ignoreAccessibiliy:s,isBackward:l,onUncontrolled:u,onElement:d}=n,h=[];let{acceptCondition:p}=n;if(!r)return null;p||(p=E=>this._tabster.focusable.isFocusable(E,i,s,s));const m={container:r,from:o||r,isBackward:l,acceptCondition:p,includeProgrammaticallyFocusable:i,ignoreUncontrolled:a,ignoreAccessibiliy:s,cachedGrouppers:{}},v=Xb(r.ownerDocument,r,E=>this._acceptElement(E,m));if(!v)return null;const _=E=>{const w=m.foundElement;return w&&h.push(w),t?w&&(m.found=!1,delete m.foundElement,delete m.fromCtx,m.from=w,d&&!d(w))?!1:!!(w||E):!!(E&&!w)};if(o)v.currentNode=o;else if(l){const E=kO(r);if(!E)return null;if(this._acceptElement(E,m)===NodeFilter.FILTER_ACCEPT&&!_(!0))return h;v.currentNode=E}let b;do b=(l?v.previousNode():v.nextNode())||void 0;while(_());if(!t){const E=m.nextUncontrolled;if(E)return u&&u(E),b?void 0:null}return h.length?h:null}_acceptElement(t,n){if(n.found)return NodeFilter.FILTER_ACCEPT;const r=n.container;if(t===r)return NodeFilter.FILTER_SKIP;if(!r.contains(t)||t.__tabsterDummyContainer)return NodeFilter.FILTER_REJECT;let o=n.lastToIgnore;if(o){if(o.contains(t))return NodeFilter.FILTER_REJECT;o=n.lastToIgnore=void 0}const i=n.currentCtx=Io.getTabsterContext(this._tabster,t);if(!i)return NodeFilter.FILTER_SKIP;if(n.ignoreUncontrolled){if(_O(t))return NodeFilter.FILTER_SKIP}else if(i.uncontrolled&&!n.nextUncontrolled&&this._tabster.focusable.isFocusable(t,void 0,!0,!0)&&!i.groupper&&!i.mover)return n.nextUncontrolled=i.uncontrolled,NodeFilter.FILTER_REJECT;if(t.tagName==="IFRAME"||t.tagName==="WEBVIEW")return n.found=!0,n.lastToIgnore=n.foundElement=t,NodeFilter.FILTER_ACCEPT;if(!n.ignoreAccessibiliy&&!this.isAccessible(t))return NodeFilter.FILTER_REJECT;let a,s=n.fromCtx;s||(s=n.fromCtx=Io.getTabsterContext(this._tabster,n.from));const l=s==null?void 0:s.mover;let u=i.groupper,d=i.mover;if(u||d||l){const h=u==null?void 0:u.getElement(),p=l==null?void 0:l.getElement();let m=d==null?void 0:d.getElement();m&&p&&r.contains(p)&&(!h||!d||p.contains(h))&&(d=l,m=p),h&&(h===r||!r.contains(h))&&(u=void 0),m&&!r.contains(m)&&(d=void 0),u&&d&&(m&&h&&!h.contains(m)?d=void 0:u=void 0),u&&(a=u.acceptElement(t,n)),d&&(a=d.acceptElement(t,n))}return a===void 0&&(a=n.acceptCondition(t)?NodeFilter.FILTER_ACCEPT:NodeFilter.FILTER_SKIP),a===NodeFilter.FILTER_ACCEPT&&!n.found&&(n.found=!0,n.foundElement=t),a}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/const Ht={Tab:9,Enter:13,Esc:27,Space:32,PageUp:33,PageDown:34,End:35,Home:36,Left:37,Up:38,Right:39,Down:40};/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/class ar extends xO{constructor(t,n){super(),this._init=()=>{this._initTimer=void 0;const r=this._win();r.document.addEventListener(wa,this._onFocusIn,!0),r.document.addEventListener("focusout",this._onFocusOut,!0),r.addEventListener("keydown",this._onKeyDown,!0)},this._onFocusIn=r=>{this._setFocusedElement(r.target,r.details.relatedTarget,r.details.isFocusedProgrammatically)},this._onFocusOut=r=>{this._setFocusedElement(void 0,r.relatedTarget)},this._validateFocusedElement=r=>{},this._onKeyDown=r=>{var o,i,a,s;if(r.keyCode!==Ht.Tab||r.ctrlKey)return;const l=this.getVal();if(!l||!l.ownerDocument||l.contentEditable==="true")return;const u=this._tabster,d=u.controlTab,h=Io.getTabsterContext(u,l);if(!h||!d&&!h.groupper&&!h.mover||h.ignoreKeydown[r.key])return;const p=r.shiftKey,m=ar.findNextTabbable(u,h,void 0,l,p);if((!m||!d&&!m.element)&&!d){const _=m==null?void 0:m.lastMoverOrGroupper;if(_){(o=_.dummyManager)===null||o===void 0||o.moveOutWithDefaultAction(p);return}}let v;if(m){let _=m.uncontrolled;if(_){const b=h.isGroupperFirst;let E=!1;if(b!==void 0){const w=(i=h.groupper)===null||i===void 0?void 0:i.getElement(),k=(a=h.mover)===null||a===void 0?void 0:a.getElement();let y;b&&w&&_.contains(w)?y=w:!b&&k&&_.contains(k)&&(y=k),y&&(_=y,E=!0)}_&&h.uncontrolled!==_&&es.moveWithPhantomDummy(this._tabster,_,E,p);return}if(v=m.element,h.modalizer){const b=v&&Io.getTabsterContext(u,v);if((!b||h.root.uid!==b.root.uid||!(!((s=b.modalizer)===null||s===void 0)&&s.isActive()))&&h.modalizer.onBeforeFocusOut()){r.preventDefault();return}if(!v&&h.modalizer.isActive()&&h.modalizer.getProps().isTrapped){const E=p?"findLast":"findFirst";v=u.focusable[E]({container:h.modalizer.getElement()})}}}v?v.tagName!=="IFRAME"&&(r.preventDefault(),r.stopImmediatePropagation(),np(v)):h.root.moveOutWithDefaultAction(p)},this._tabster=t,this._win=n,this._initTimer=n().setTimeout(this._init,0)}dispose(){super.dispose();const t=this._win();this._initTimer&&(t.clearTimeout(this._initTimer),this._initTimer=void 0),t.document.removeEventListener(wa,this._onFocusIn,!0),t.document.removeEventListener("focusout",this._onFocusOut,!0),t.removeEventListener("keydown",this._onKeyDown,!0),delete ar._lastResetElement,delete this._nextVal,delete this._lastVal}static forgetMemorized(t,n){var r,o;let i=ar._lastResetElement,a=i&&i.get();a&&n.contains(a)&&delete ar._lastResetElement,a=(o=(r=t._nextVal)===null||r===void 0?void 0:r.element)===null||o===void 0?void 0:o.get(),a&&n.contains(a)&&delete t._nextVal,i=t._lastVal,a=i&&i.get(),a&&n.contains(a)&&delete t._lastVal}getFocusedElement(){return this.getVal()}getLastFocusedElement(){var t;let n=(t=this._lastVal)===null||t===void 0?void 0:t.get();return(!n||n&&!Zb(n.ownerDocument,n))&&(this._lastVal=n=void 0),n}focus(t,n,r){return this._tabster.focusable.isFocusable(t,n,!1,r)?(t.focus(),!0):!1}focusDefault(t){const n=this._tabster.focusable.findDefault({container:t});return n?(this._tabster.focusedElement.focus(n),!0):!1}_focusFirstOrLast(t,n){const r=this._tabster.focusable,o=n.container;let i,a;if(o){const s=Io.getTabsterContext(this._tabster,o);if(s){let l=ar.findNextTabbable(this._tabster,s,o,void 0,!t);if(l)for(a=l.element,i=l.uncontrolled;!a&&i;)r.isFocusable(i,!1,!0,!0)?a=i:a=r[t?"findFirst":"findLast"]({container:i,ignoreUncontrolled:!0,ignoreAccessibiliy:!0}),a||(l=ar.findNextTabbable(this._tabster,s,i,void 0,!t),l&&(a=l.element,i=l.uncontrolled))}}return a&&!(o!=null&&o.contains(a))&&(a=void 0),a?(this.focus(a,!1,!0),!0):!1}focusFirst(t){return this._focusFirstOrLast(!0,t)}focusLast(t){return this._focusFirstOrLast(!1,t)}resetFocus(t){if(!this._tabster.focusable.isVisible(t))return!1;if(this._tabster.focusable.isFocusable(t,!0,!0,!0))this.focus(t);else{const n=t.getAttribute("tabindex"),r=t.getAttribute("aria-hidden");t.tabIndex=-1,t.setAttribute("aria-hidden","true"),ar._lastResetElement=new os(this._win,t),this.focus(t,!0,!0),this._setOrRemoveAttribute(t,"tabindex",n),this._setOrRemoveAttribute(t,"aria-hidden",r)}return!0}_setOrRemoveAttribute(t,n,r){r===null?t.removeAttribute(n):t.setAttribute(n,r)}_setFocusedElement(t,n,r){var o;if(this._tabster._noop)return;const i={relatedTarget:n};if(t){const s=(o=ar._lastResetElement)===null||o===void 0?void 0:o.get();if(ar._lastResetElement=void 0,s===t||_O(t))return;i.isFocusedProgrammatically=r}const a=this._nextVal={element:t?new os(this._win,t):void 0,details:i};t&&t!==this._val&&this._validateFocusedElement(t),this._nextVal===a&&this.setVal(t,i),this._nextVal=void 0}setVal(t,n){super.setVal(t,n),t&&(this._lastVal=new os(this._win,t))}static findNextTabbable(t,n,r,o,i){var a;const s=r||n.root.getElement();if(!s)return null;let l=null;const u=ar._isTabbingTimer,d=t.getWindow();u&&d.clearTimeout(u),ar.isTabbing=!0,ar._isTabbingTimer=d.setTimeout(()=>{delete ar._isTabbingTimer,ar.isTabbing=!1},0);const h=m=>{l=m.findNextTabbable(o,i)};if(n.groupper&&n.mover){let m=n.isGroupperFirst;if(m&&o){const v=Io.getTabsterContext(t,o);(v==null?void 0:v.groupper)!==n.groupper&&(m=!1)}h(m?n.groupper:n.mover)}else if(n.groupper)h(n.groupper);else if(n.mover)h(n.mover);else{let m;const v=b=>{m=b},_=i?t.focusable.findPrev({container:s,currentElement:o,onUncontrolled:v}):t.focusable.findNext({container:s,currentElement:o,onUncontrolled:v});l={element:m?void 0:_,uncontrolled:m}}const p=(a=l==null?void 0:l.lastMoverOrGroupper)===null||a===void 0?void 0:a.getElement();if(p){l=null;const m=LC(p,i);if(m){const v=Io.getTabsterContext(t,m,{checkRtl:!0});if(v){let _=LC(m,!i);_&&(i||(_=kO(_)),l=ar.findNextTabbable(t,v,s,_,i))}}}return l}}ar.isTabbing=!1;/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/class Hoe extends xO{constructor(t){super(),this._onChange=n=>{this.setVal(n,void 0)},this._keyborg=Yb(t()),this._keyborg.subscribe(this._onChange)}dispose(){super.dispose(),this._keyborg&&(this._keyborg.unsubscribe(this._onChange),Qb(this._keyborg),delete this._keyborg)}setNavigatingWithKeyboard(t){var n;(n=this._keyborg)===null||n===void 0||n.setVal(t)}isNavigatingWithKeyboard(){var t;return!!(!((t=this._keyborg)===null||t===void 0)&&t.isNavigatingWithKeyboard())}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/let Uoe=0;class qoe extends Jb{constructor(t,n,r,o,i,a){super(t,n,a),this._isFocused=!1,this._onKeyDown=l=>{const u=l.keyCode,d=l.shiftKey;if(u!==Ht.Tab)return;const h=this._tabster.focusedElement.getFocusedElement(),p=this.getElement();let m=d?"findPrev":"findNext",v;h&&(p!=null&&p.contains(h))&&(v=this._tabster.focusable[m]({container:this.getElement(),currentElement:h})),!v&&this._props.isTrapped&&(m=d?"findLast":"findFirst",v=this._tabster.focusable[m]({container:this.getElement()})),v?(l.preventDefault(),l.stopImmediatePropagation(),np(v)):this._props.isOthersAccessible||this._moveOutWithDefault(d)},this.internalId="ml"+ ++Uoe,this.userId=a.id,this._onDispose=r,this._moveOutWithDefault=o,this._onActiveChange=i,t.controlTab||n.addEventListener("keydown",this._onKeyDown);const s=n.parentElement;s?this._modalizerParent=new os(t.getWindow,s):this._modalizerParent=null,this._setAccessibilityProps()}setProps(t){t.id&&(this.userId=t.id),this._props={...t},this._setAccessibilityProps()}dispose(){this._onDispose(this),this._remove()}setActive(t){var n,r,o;if(t===this._isActive)return;this._isActive=t,this._onActiveChange(t);let i=((n=this._element.get())===null||n===void 0?void 0:n.ownerDocument)||((o=(r=this._modalizerParent)===null||r===void 0?void 0:r.get())===null||o===void 0?void 0:o.ownerDocument);i||(i=this._tabster.getWindow().document);const a=i.body,s=Xb(i,a,l=>{var u;if(this._props.isOthersAccessible)return NodeFilter.FILTER_REJECT;const d=this._element.get(),h=(u=this._modalizerParent)===null||u===void 0?void 0:u.get(),p=d===l,m=!!l.contains(d||null),v=!!l.contains(h||null);if(p)return NodeFilter.FILTER_REJECT;if(m||v)return NodeFilter.FILTER_SKIP;Toe(this._tabster,l,"aria-hidden",t?"true":void 0);const _=d===(d==null?void 0:d.ownerDocument.body)?!1:d==null?void 0:d.ownerDocument.body.contains(d);return!(h===(h==null?void 0:h.ownerDocument.body)?!1:h==null?void 0:h.ownerDocument.body.contains(h))&&!_?NodeFilter.FILTER_SKIP:NodeFilter.FILTER_REJECT});if(s)for(;s.nextNode(););}isActive(){return!!this._isActive}contains(t){var n;return!!(!((n=this.getElement())===null||n===void 0)&&n.contains(t))}onBeforeFocusOut(){const t=this.getElement();return t?!Wd(t,mO,{eventName:"beforefocusout"}):!1}_remove(){}_setAccessibilityProps(){}}class $oe extends es{constructor(t,n,r){super(n,r,PT.Modalizer),this._onFocusDummyInput=o=>{const i=this._modalizerAPI.activeModalizer;if(!i||o.shouldMoveOut)return;const a=o.isFirst?"findFirst":"findLast",s=this._tabster.focusable[a]({container:i.getElement()});s&&this._tabster.focusedElement.focus(s)},this._modalizerAPI=t,this._tabster=n,this.setTabbable(!1),this._setHandlers(this._onFocusDummyInput)}}class Woe{constructor(t){if(this._init=()=>{this._initTimer=void 0,this._tabster.focusedElement.subscribe(this._onFocus)},this._onModalizerDispose=n=>{n.setActive(!1),this.activeModalizer===n&&(this.activeModalizer=void 0),delete this._modalizers[n.userId]},this._onFocus=(n,r)=>{var o,i,a;const s=n&&Io.getTabsterContext(this._tabster,n);if(!s||!n)return;this._focusOutTimer&&(this._win().clearTimeout(this._focusOutTimer),this._focusOutTimer=void 0);const l=s==null?void 0:s.modalizer;if(l!==this.activeModalizer){if(r.isFocusedProgrammatically&&!(!((o=this.activeModalizer)===null||o===void 0)&&o.contains(n)))(i=this.activeModalizer)===null||i===void 0||i.setActive(!1),this.activeModalizer=void 0,l&&(this.activeModalizer=l,this.activeModalizer.setActive(!0));else if(!(!((a=this.activeModalizer)===null||a===void 0)&&a.getProps().isOthersAccessible)){const u=this._win();u.clearTimeout(this._restoreModalizerFocusTimer),this._restoreModalizerFocusTimer=u.setTimeout(()=>this._restoreModalizerFocus(n),100)}}},this._tabster=t,this._win=t.getWindow,this._initTimer=this._win().setTimeout(this._init,0),this._modalizers={},!t.controlTab){const n=this._win().document.body;this._dummyManager=new $oe(this,t,new os(this._win,n))}}dispose(){var t;const n=this._win();(t=this._dummyManager)===null||t===void 0||t.dispose(),this._initTimer&&(n.clearTimeout(this._initTimer),this._initTimer=void 0),n.clearTimeout(this._restoreModalizerFocusTimer),n.clearTimeout(this._focusOutTimer),Object.keys(this._modalizers).forEach(r=>{this._modalizers[r]&&(this._modalizers[r].dispose(),delete this._modalizers[r])}),this._tabster.focusedElement.unsubscribe(this._onFocus),delete this.activeModalizer}createModalizer(t,n){var r,o,i,a,s;const l=new qoe(this._tabster,t,this._onModalizerDispose,(o=(r=this._dummyManager)===null||r===void 0?void 0:r.moveOutWithDefaultAction)!==null&&o!==void 0?o:()=>null,(a=(i=this._dummyManager)===null||i===void 0?void 0:i.setTabbable)!==null&&a!==void 0?a:()=>null,n);if(this._modalizers[n.id]=l,t.contains((s=this._tabster.focusedElement.getFocusedElement())!==null&&s!==void 0?s:null)){const u=this.activeModalizer;u&&u.setActive(!1),this.activeModalizer=l,this.activeModalizer.setActive(!0)}return l}focus(t,n,r){const o=Io.getTabsterContext(this._tabster,t);if(o&&o.modalizer){this.activeModalizer=o.modalizer,this.activeModalizer.setActive(!0);const i=this.activeModalizer.getProps(),a=this.activeModalizer.getElement();if(a){if(n===void 0&&(n=i.isNoFocusFirst),!n&&this._tabster.keyboardNavigation.isNavigatingWithKeyboard()&&this._tabster.focusedElement.focusFirst({container:a})||(r===void 0&&(r=i.isNoFocusDefault),!r&&this._tabster.focusedElement.focusDefault(a)))return!0;this._tabster.focusedElement.resetFocus(a)}}return!1}updateModalizer(t,n){n&&(t.isActive()&&t.setActive(!1),delete this._modalizers[t.userId],this.activeModalizer===t&&(this.activeModalizer=void 0))}_restoreModalizerFocus(t){if(!(t!=null&&t.ownerDocument)||!this.activeModalizer)return;let n=this._tabster.focusable.findFirst({container:this.activeModalizer.getElement()});if(n){if(t.compareDocumentPosition(n)&document.DOCUMENT_POSITION_PRECEDING&&(n=this._tabster.focusable.findLast({container:t.ownerDocument.body}),!n))throw new Error("Something went wrong.");this._tabster.focusedElement.focus(n)}else t.blur()}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/const Goe=["input","textarea","*[contenteditable]"].join(", ");class Koe extends es{constructor(t,n,r){super(n,t,PT.Mover),this._onFocusDummyInput=o=>{var i,a;const s=this._element.get(),l=o.input;if(s&&!o.shouldMoveOut&&l){const u=Io.getTabsterContext(this._tabster,s);let d;u&&(d=(i=ar.findNextTabbable(this._tabster,u,void 0,l,!o.isFirst))===null||i===void 0?void 0:i.element);const h=(a=this._getMemorized())===null||a===void 0?void 0:a.get();h&&(d=h),d&&np(d)}},this._tabster=n,this._getMemorized=r,this._setHandlers(this._onFocusDummyInput)}}const Y2=1,qC=2,$C=3;class Voe extends Jb{constructor(t,n,r,o){super(t,n,o),this._visible={},this._onIntersection=a=>{for(const s of a){const l=s.target,u=Hh(this._win,l);let d,h=this._fullyVisible;if(s.intersectionRatio>=.25&&(d=s.intersectionRatio>=.75?Ul.Visible:Ul.PartiallyVisible,d===Ul.Visible&&(h=u)),this._visible[u]!==d){d===void 0?(delete this._visible[u],h===u&&delete this._fullyVisible):(this._visible[u]=d,this._fullyVisible=h);const p=this.getState(l);p&&Wd(l,T_,p)}}},this._win=t.getWindow,(this._props.trackState||this._props.visibilityAware)&&(this._intersectionObserver=new IntersectionObserver(this._onIntersection,{threshold:[0,.25,.5,.75,1]}),this._observeState()),this._onDispose=r;const i=()=>o.memorizeCurrent?this._current:void 0;t.controlTab||(this.dummyManager=new Koe(this._element,t,i))}dispose(){var t;this._onDispose(this),this._intersectionObserver&&(this._intersectionObserver.disconnect(),delete this._intersectionObserver),delete this._current,delete this._fullyVisible,delete this._allElements,delete this._updateQueue,this._unobserve&&(this._unobserve(),delete this._unobserve);const n=this._win();this._setCurrentTimer&&(n.clearTimeout(this._setCurrentTimer),delete this._setCurrentTimer),this._updateTimer&&(n.clearTimeout(this._updateTimer),delete this._updateTimer),(t=this.dummyManager)===null||t===void 0||t.dispose()}setCurrent(t){t?this._current=new os(this._win,t):this._current=void 0,(this._props.trackState||this._props.visibilityAware)&&!this._setCurrentTimer&&(this._setCurrentTimer=this._win().setTimeout(()=>{var n;delete this._setCurrentTimer;const r=[];this._current!==this._prevCurrent&&(r.push(this._current),r.push(this._prevCurrent),this._prevCurrent=this._current);for(const o of r){const i=o==null?void 0:o.get();if(i&&((n=this._allElements)===null||n===void 0?void 0:n.get(i))===this){const a=this._props;if(i&&(a.visibilityAware!==void 0||a.trackState)){const s=this.getState(i);s&&Wd(i,T_,s)}}}}))}getCurrent(){var t;return((t=this._current)===null||t===void 0?void 0:t.get())||null}findNextTabbable(t,n){var r;const o=this.getElement(),i=o&&((r=t==null?void 0:t.__tabsterDummyContainer)===null||r===void 0?void 0:r.get())===o;if(!o)return null;const s=this._tabster.focusable;let l=null,u;const d=h=>{u=h};return(this._props.tabbable||i||t&&!o.contains(t))&&(l=n?s.findPrev({currentElement:t,container:o,onUncontrolled:d}):s.findNext({currentElement:t,container:o,onUncontrolled:d})),{element:l,uncontrolled:u,lastMoverOrGroupper:l||u?void 0:this}}acceptElement(t,n){var r,o,i;if(!ar.isTabbing)return!((r=n.currentCtx)===null||r===void 0)&&r.isExcludedFromMover?NodeFilter.FILTER_REJECT:void 0;const{memorizeCurrent:a,visibilityAware:s}=this._props,l=this.getElement();if(l&&(a||s)&&(!l.contains(n.from)||((o=n.from.__tabsterDummyContainer)===null||o===void 0?void 0:o.get())===l)){if(a){const u=(i=this._current)===null||i===void 0?void 0:i.get();if(u&&n.acceptCondition(u))return n.found=!0,n.foundElement=u,n.lastToIgnore=l,NodeFilter.FILTER_ACCEPT}if(s){const u=this._tabster.focusable.findElement({container:l,ignoreUncontrolled:!0,isBackward:n.isBackward,acceptCondition:d=>{var h;const p=Hh(this._win,d),m=this._visible[p];return l!==d&&!!(!((h=this._allElements)===null||h===void 0)&&h.get(d))&&n.acceptCondition(d)&&(m===Ul.Visible||m===Ul.PartiallyVisible&&(s===Ul.PartiallyVisible||!this._fullyVisible))}});if(u)return n.found=!0,n.foundElement=u,n.lastToIgnore=l,NodeFilter.FILTER_ACCEPT}}}_observeState(){const t=this.getElement();if(this._unobserve||!t||typeof MutationObserver>"u")return;const n=this._win(),r=this._allElements=new WeakMap,o=this._tabster.focusable;let i=this._updateQueue=[];const a=new MutationObserver(m=>{for(const v of m){const _=v.target,b=v.removedNodes,E=v.addedNodes;if(v.type==="attributes")v.attributeName==="tabindex"&&i.push({element:_,type:qC});else{for(let w=0;w<b.length;w++)i.push({element:b[w],type:$C});for(let w=0;w<E.length;w++)i.push({element:E[w],type:Y2})}}h()}),s=(m,v)=>{var _,b;const E=r.get(m);E&&v&&((_=this._intersectionObserver)===null||_===void 0||_.unobserve(m),r.delete(m)),!E&&!v&&(r.set(m,this),(b=this._intersectionObserver)===null||b===void 0||b.observe(m))},l=m=>{const v=o.isFocusable(m);r.get(m)?v||s(m,!0):v&&s(m)},u=m=>{const{mover:v}=p(m);if(v&&v!==this)if(v.getElement()===m&&o.isFocusable(m))s(m);else return;const _=Xb(n.document,m,b=>{const{mover:E,groupper:w}=p(b);if(E&&E!==this)return NodeFilter.FILTER_REJECT;const k=w==null?void 0:w.getFirst(!0);return w&&w.getElement()!==b&&k&&k!==b?NodeFilter.FILTER_REJECT:(o.isFocusable(b)&&s(b),NodeFilter.FILTER_SKIP)});if(_)for(_.currentNode=m;_.nextNode(););},d=m=>{r.get(m)&&s(m,!0);for(let _=m.firstElementChild;_;_=_.nextElementSibling)d(_)},h=()=>{!this._updateTimer&&i.length&&(this._updateTimer=n.setTimeout(()=>{delete this._updateTimer;for(const{element:m,type:v}of i)switch(v){case qC:l(m);break;case Y2:u(m);break;case $C:d(m);break}i=this._updateQueue=[]},0))},p=m=>{const v={};for(let _=m;_;_=_.parentElement){const b=Au(this._tabster,_);if(b&&(b.groupper&&!v.groupper&&(v.groupper=b.groupper),b.mover)){v.mover=b.mover;break}}return v};i.push({element:t,type:Y2}),h(),a.observe(t,{childList:!0,subtree:!0,attributes:!0,attributeFilter:["tabindex"]}),this._unobserve=()=>{a.disconnect()}}getState(t){const n=Hh(this._win,t);if(n in this._visible){const r=this._visible[n]||Ul.Invisible;return{isCurrent:this._current?this._current.get()===t:void 0,visibility:r}}}}function Yoe(e,t,n,r,o,i,a,s){const l=n<o?o-n:a<e?e-a:0,u=r<i?i-r:s<t?t-s:0;return l===0?u:u===0?l:Math.sqrt(l*l+u*u)}class Qoe{constructor(t,n){this._init=()=>{this._initTimer=void 0,this._win().addEventListener("keydown",this._onKeyDown,!0)},this._onMoverDispose=r=>{delete this._movers[r.id]},this._onFocus=r=>{var o;for(let i=r;i;i=i.parentElement){const a=(o=Au(this._tabster,i))===null||o===void 0?void 0:o.mover;if(a){a.setCurrent(r);break}}},this._onKeyDown=async r=>{var o;this._ignoredInputTimer&&(this._win().clearTimeout(this._ignoredInputTimer),delete this._ignoredInputTimer),(o=this._ignoredInputResolve)===null||o===void 0||o.call(this,!1);let i=r.keyCode;switch(i){case Ht.Down:case Ht.Right:case Ht.Up:case Ht.Left:case Ht.PageDown:case Ht.PageUp:case Ht.Home:case Ht.End:break;default:return}const a=this._tabster,s=a.focusedElement.getFocusedElement();if(!s||await this._isIgnoredInput(s,i))return;const l=Io.getTabsterContext(a,s,{checkRtl:!0});if(!l||!l.mover||l.isExcludedFromMover||l.isGroupperFirst&&l.groupper&&l.groupper.isActive(!0))return;const u=l.mover,d=u.getElement();if(!d)return;const h=a.focusable,p=u.getProps(),m=p.direction||Gf.Both,v=m===Gf.Both,_=v||m===Gf.Vertical,b=v||m===Gf.Horizontal,E=m===Gf.Grid,w=p.cyclic;let k,y,F=0,C=0;if(E&&(y=s.getBoundingClientRect(),F=Math.ceil(y.left),C=Math.floor(y.right)),!(p.disableHomeEndKeys&&(i===Ht.Home||i===Ht.End))){if(l.isRtl&&(i===Ht.Right?i=Ht.Left:i===Ht.Left&&(i=Ht.Right)),i===Ht.Down&&_||i===Ht.Right&&(b||E))if(k=h.findNext({currentElement:s,container:d}),k&&E){const A=Math.ceil(k.getBoundingClientRect().left);C>A&&(k=void 0)}else!k&&w&&(k=h.findFirst({container:d}));else if(i===Ht.Up&&_||i===Ht.Left&&(b||E))k=h.findPrev({currentElement:s,container:d}),k&&E?Math.floor(k.getBoundingClientRect().right)>F&&(k=void 0):!k&&w&&(k=h.findLast({container:d}));else if(i===Ht.Home)k=h.findFirst({container:d});else if(i===Ht.End)k=h.findLast({container:d});else if(i===Ht.PageUp){let A=h.findPrev({currentElement:s,container:d}),P=null;for(;A;)P=A,A=DC(this._win,A)?h.findPrev({currentElement:A,container:d}):null;k=P,k&&PC(this._win,k,!1)}else if(i===Ht.PageDown){let A=h.findNext({currentElement:s,container:d}),P=null;for(;A;)P=A,A=DC(this._win,A)?h.findNext({currentElement:A,container:d}):null;k=P,k&&PC(this._win,k,!0)}else if(E){const A=i===Ht.Up,P=F,I=Math.ceil(y.top),j=C,H=Math.floor(y.bottom);let K,U,pe=0;h.findAll({container:d,currentElement:s,isBackward:A,onElement:se=>{const J=se.getBoundingClientRect(),$=Math.ceil(J.left),_e=Math.ceil(J.top),ve=Math.floor(J.right),fe=Math.floor(J.bottom);if(A&&I<fe||!A&&H>_e)return!0;const R=Math.ceil(Math.min(j,ve))-Math.floor(Math.max(P,$)),L=Math.ceil(Math.min(j-P,ve-$));if(R>0&&L>=R){const Ae=R/L;Ae>pe&&(K=se,pe=Ae)}else if(pe===0){const Ae=Yoe(P,I,j,H,$,_e,ve,fe);(U===void 0||Ae<U)&&(U=Ae,K=se)}else if(pe>0)return!1;return!0}}),k=K}k&&(r.preventDefault(),r.stopImmediatePropagation(),np(k))}},this._tabster=t,this._win=n,this._initTimer=n().setTimeout(this._init,0),this._movers={},t.focusedElement.subscribe(this._onFocus)}dispose(){var t;const n=this._win();this._tabster.focusedElement.unsubscribe(this._onFocus),this._initTimer&&(n.clearTimeout(this._initTimer),delete this._initTimer),(t=this._ignoredInputResolve)===null||t===void 0||t.call(this,!1),this._ignoredInputTimer&&(n.clearTimeout(this._ignoredInputTimer),delete this._ignoredInputTimer),n.removeEventListener("keydown",this._onKeyDown,!0),Object.keys(this._movers).forEach(r=>{this._movers[r]&&(this._movers[r].dispose(),delete this._movers[r])})}createMover(t,n){const r=new Voe(this._tabster,t,this._onMoverDispose,n);return this._movers[r.id]=r,r}async _isIgnoredInput(t,n){var r;if(t.getAttribute("aria-expanded")==="true")return!0;if(TO(t,Goe)){let o=0,i=0,a=0,s;if(t.tagName==="INPUT"||t.tagName==="TEXTAREA"){const l=t.type;if(a=(t.value||"").length,l==="email"||l==="number"){if(a){const d=(r=t.ownerDocument.defaultView)===null||r===void 0?void 0:r.getSelection();if(d){const h=d.toString().length,p=n===Ht.Left||n===Ht.Up;if(d.modify("extend",p?"backward":"forward","character"),h!==d.toString().length)return d.modify("extend",p?"forward":"backward","character"),!0;a=0}}}else{const d=t.selectionStart;if(d===null)return l==="hidden";o=d||0,i=t.selectionEnd||0}}else t.contentEditable==="true"&&(s=new(wO(this._win))(l=>{this._ignoredInputResolve=v=>{delete this._ignoredInputResolve,l(v)};const u=this._win();this._ignoredInputTimer&&u.clearTimeout(this._ignoredInputTimer);const{anchorNode:d,focusNode:h,anchorOffset:p,focusOffset:m}=u.getSelection()||{};this._ignoredInputTimer=u.setTimeout(()=>{var v,_,b;delete this._ignoredInputTimer;const{anchorNode:E,focusNode:w,anchorOffset:k,focusOffset:y}=u.getSelection()||{};if(E!==d||w!==h||k!==p||y!==m){(v=this._ignoredInputResolve)===null||v===void 0||v.call(this,!1);return}if(o=k||0,i=y||0,a=((_=t.textContent)===null||_===void 0?void 0:_.length)||0,E&&w&&t.contains(E)&&t.contains(w)&&E!==t){let F=!1;const C=A=>{if(A===E)F=!0;else if(A===w)return!0;const P=A.textContent;if(P&&!A.firstChild){const j=P.length;F?w!==E&&(i+=j):(o+=j,i+=j)}let I=!1;for(let j=A.firstChild;j&&!I;j=j.nextSibling)I=C(j);return I};C(t)}(b=this._ignoredInputResolve)===null||b===void 0||b.call(this,!0)},0)}));if(s&&!await s||o!==i||o>0&&(n===Ht.Left||n===Ht.Up||n===Ht.Home)||o<a&&(n===Ht.Right||n===Ht.Down||n===Ht.End))return!0}return!1}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/function Xoe(e,t,n,r){if(typeof MutationObserver>"u")return()=>{};const o=t.getWindow;let i;const a=d=>{for(const h of d){const p=h.target,m=h.removedNodes,v=h.addedNodes;if(h.type==="attributes")h.attributeName===xd&&n(t,p);else{for(let _=0;_<m.length;_++)s(m[_],!0);for(let _=0;_<v.length;_++)s(v[_])}}};function s(d,h){i||(i=bl(o).elementByUId),l(d,h);const p=Xb(e,d,m=>l(m,h));if(p)for(;p.nextNode(););}function l(d,h){var p;if(!d.getAttribute)return NodeFilter.FILTER_SKIP;const m=d.__tabsterElementUID;return m&&i&&(h?delete i[m]:(p=i[m])!==null&&p!==void 0||(i[m]=new os(o,d))),(Au(t,d)||d.hasAttribute(xd))&&n(t,d,h),NodeFilter.FILTER_SKIP}const u=new MutationObserver(a);return r&&s(o().document.body),u.observe(e,{childList:!0,subtree:!0,attributes:!0,attributeFilter:[xd]}),()=>{u.disconnect()}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/class Zoe{constructor(){}}/*!
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/class Joe{constructor(t){this.keyboardNavigation=t.keyboardNavigation,this.focusedElement=t.focusedElement,this.focusable=t.focusable,this.root=t.root,this.uncontrolled=t.uncontrolled,this.core=t}}class eie{constructor(t,n){var r;this._forgetMemorizedElements=[],this._wrappers=new Set,this._version="3.0.8",this._noop=!1,this.getWindow=()=>{if(!this._win)throw new Error("Using disposed Tabster.");return this._win},this._storage=xoe(t),this._win=t;const o=this.getWindow;this.keyboardNavigation=new Hoe(o),this.focusedElement=new ar(this,o),this.focusable=new zoe(this,o),this.root=new Io(this,n==null?void 0:n.autoRoot),this.uncontrolled=new Zoe,this.controlTab=(r=n==null?void 0:n.controlTab)!==null&&r!==void 0?r:!0,this.rootDummyInputs=!!(n!=null&&n.rootDummyInputs),this.internal={stopObserver:()=>{this._unobserve&&(this._unobserve(),delete this._unobserve)},resumeObserver:i=>{if(!this._unobserve){const a=o().document;this._unobserve=Xoe(a,this,_oe,i)}}},this.internal.resumeObserver(!1),bO(o)}createTabster(t){const n=new Joe(this);return t||this._wrappers.add(n),n}disposeTabster(t,n){n?this._wrappers.clear():this._wrappers.delete(t),this._wrappers.size===0&&this.dispose()}dispose(){var t,n,r,o,i,a,s;this.internal.stopObserver();const l=this._win;this._forgetMemorizedElements=[],l&&this._forgetMemorizedTimer&&(l.clearTimeout(this._forgetMemorizedTimer),delete this._forgetMemorizedTimer),(t=this.outline)===null||t===void 0||t.dispose(),(n=this.crossOrigin)===null||n===void 0||n.dispose(),(r=this.deloser)===null||r===void 0||r.dispose(),(o=this.groupper)===null||o===void 0||o.dispose(),(i=this.mover)===null||i===void 0||i.dispose(),(a=this.modalizer)===null||a===void 0||a.dispose(),(s=this.observedElement)===null||s===void 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0:r.meta,this.cacheTime=Math.max(this.cacheTime||0,(o=this.options.cacheTime)!=null?o:5*60*1e3)},t.setDefaultOptions=function(r){this.defaultOptions=r},t.scheduleGc=function(){var r=this;this.clearGcTimeout(),M_(this.cacheTime)&&(this.gcTimeout=setTimeout(function(){r.optionalRemove()},this.cacheTime))},t.clearGcTimeout=function(){this.gcTimeout&&(clearTimeout(this.gcTimeout),this.gcTimeout=void 0)},t.optionalRemove=function(){this.observers.length||(this.state.isFetching?this.hadObservers&&this.scheduleGc():this.cache.remove(this))},t.setData=function(r,o){var i,a,s=this.state.data,l=i1e(r,s);return(i=(a=this.options).isDataEqual)!=null&&i.call(a,s,l)?l=s:this.options.structuralSharing!==!1&&(l=tb(s,l)),this.dispatch({data:l,type:"success",dataUpdatedAt:o==null?void 0:o.updatedAt}),l},t.setState=function(r,o){this.dispatch({type:"setState",state:r,setStateOptions:o})},t.cancel=function(r){var o,i=this.promise;return(o=this.retryer)==null||o.cancel(r),i?i.then(Kr).catch(Kr):Promise.resolve()},t.destroy=function(){this.clearGcTimeout(),this.cancel({silent:!0})},t.reset=function(){this.destroy(),this.setState(this.initialState)},t.isActive=function(){return this.observers.some(function(r){return r.options.enabled!==!1})},t.isFetching=function(){return this.state.isFetching},t.isStale=function(){return this.state.isInvalidated||!this.state.dataUpdatedAt||this.observers.some(function(r){return r.getCurrentResult().isStale})},t.isStaleByTime=function(r){return r===void 0&&(r=0),this.state.isInvalidated||!this.state.dataUpdatedAt||!SD(this.state.dataUpdatedAt,r)},t.onFocus=function(){var r,o=this.observers.find(function(i){return i.shouldFetchOnWindowFocus()});o&&o.refetch(),(r=this.retryer)==null||r.continue()},t.onOnline=function(){var r,o=this.observers.find(function(i){return i.shouldFetchOnReconnect()});o&&o.refetch(),(r=this.retryer)==null||r.continue()},t.addObserver=function(r){this.observers.indexOf(r)===-1&&(this.observers.push(r),this.hadObservers=!0,this.clearGcTimeout(),this.cache.notify({type:"observerAdded",query:this,observer:r}))},t.removeObserver=function(r){this.observers.indexOf(r)!==-1&&(this.observers=this.observers.filter(function(o){return o!==r}),this.observers.length||(this.retryer&&(this.retryer.isTransportCancelable||this.abortSignalConsumed?this.retryer.cancel({revert:!0}):this.retryer.cancelRetry()),this.cacheTime?this.scheduleGc():this.cache.remove(this)),this.cache.notify({type:"observerRemoved",query:this,observer:r}))},t.getObserversCount=function(){return this.observers.length},t.invalidate=function(){this.state.isInvalidated||this.dispatch({type:"invalidate"})},t.fetch=function(r,o){var i=this,a,s,l;if(this.state.isFetching){if(this.state.dataUpdatedAt&&(o!=null&&o.cancelRefetch))this.cancel({silent:!0});else if(this.promise){var u;return(u=this.retryer)==null||u.continueRetry(),this.promise}}if(r&&this.setOptions(r),!this.options.queryFn){var d=this.observers.find(function(w){return w.options.queryFn});d&&this.setOptions(d.options)}var h=Jv(this.queryKey),p=CD(),m={queryKey:h,pageParam:void 0,meta:this.meta};Object.defineProperty(m,"signal",{enumerable:!0,get:function(){if(p)return i.abortSignalConsumed=!0,p.signal}});var v=function(){return i.options.queryFn?(i.abortSignalConsumed=!1,i.options.queryFn(m)):Promise.reject("Missing queryFn")},_={fetchOptions:o,options:this.options,queryKey:h,state:this.state,fetchFn:v,meta:this.meta};if((a=this.options.behavior)!=null&&a.onFetch){var b;(b=this.options.behavior)==null||b.onFetch(_)}if(this.revertState=this.state,!this.state.isFetching||this.state.fetchMeta!==((s=_.fetchOptions)==null?void 0:s.meta)){var E;this.dispatch({type:"fetch",meta:(E=_.fetchOptions)==null?void 0:E.meta})}return this.retryer=new ND({fn:_.fetchFn,abort:p==null||(l=p.abort)==null?void 0:l.bind(p),onSuccess:function(k){i.setData(k),i.cache.config.onSuccess==null||i.cache.config.onSuccess(k,i),i.cacheTime===0&&i.optionalRemove()},onError:function(k){Ug(k)&&k.silent||i.dispatch({type:"error",error:k}),Ug(k)||(i.cache.config.onError==null||i.cache.config.onError(k,i),rb().error(k)),i.cacheTime===0&&i.optionalRemove()},onFail:function(){i.dispatch({type:"failed"})},onPause:function(){i.dispatch({type:"pause"})},onContinue:function(){i.dispatch({type:"continue"})},retry:_.options.retry,retryDelay:_.options.retryDelay}),this.promise=this.retryer.promise,this.promise},t.dispatch=function(r){var o=this;this.state=this.reducer(this.state,r),Dn.batch(function(){o.observers.forEach(function(i){i.onQueryUpdate(r)}),o.cache.notify({query:o,type:"queryUpdated",action:r})})},t.getDefaultState=function(r){var o=typeof r.initialData=="function"?r.initialData():r.initialData,i=typeof r.initialData<"u",a=i?typeof r.initialDataUpdatedAt=="function"?r.initialDataUpdatedAt():r.initialDataUpdatedAt:0,s=typeof o<"u";return{data:o,dataUpdateCount:0,dataUpdatedAt:s?a??Date.now():0,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchMeta:null,isFetching:!1,isInvalidated:!1,isPaused:!1,status:s?"success":"idle"}},t.reducer=function(r,o){var i,a;switch(o.type){case"failed":return At({},r,{fetchFailureCount:r.fetchFailureCount+1});case"pause":return At({},r,{isPaused:!0});case"continue":return At({},r,{isPaused:!1});case"fetch":return At({},r,{fetchFailureCount:0,fetchMeta:(i=o.meta)!=null?i:null,isFetching:!0,isPaused:!1},!r.dataUpdatedAt&&{error:null,status:"loading"});case"success":return At({},r,{data:o.data,dataUpdateCount:r.dataUpdateCount+1,dataUpdatedAt:(a=o.dataUpdatedAt)!=null?a:Date.now(),error:null,fetchFailureCount:0,isFetching:!1,isInvalidated:!1,isPaused:!1,status:"success"});case"error":var s=o.error;return Ug(s)&&s.revert&&this.revertState?At({},this.revertState):At({},r,{error:s,errorUpdateCount:r.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:r.fetchFailureCount+1,isFetching:!1,isPaused:!1,status:"error"});case"invalidate":return At({},r,{isInvalidated:!0});case"setState":return At({},r,o.state);default:return r}},e}(),ID=function(e){l1(t,e);function t(r){var o;return o=e.call(this)||this,o.config=r||{},o.queries=[],o.queriesMap={},o}var n=t.prototype;return n.build=function(o,i,a){var s,l=i.queryKey,u=(s=i.queryHash)!=null?s:ew(l,i),d=this.get(u);return d||(d=new g1e({cache:this,queryKey:l,queryHash:u,options:o.defaultQueryOptions(i),state:a,defaultOptions:o.getQueryDefaults(l),meta:i.meta}),this.add(d)),d},n.add=function(o){this.queriesMap[o.queryHash]||(this.queriesMap[o.queryHash]=o,this.queries.push(o),this.notify({type:"queryAdded",query:o}))},n.remove=function(o){var i=this.queriesMap[o.queryHash];i&&(o.destroy(),this.queries=this.queries.filter(function(a){return a!==o}),i===o&&delete this.queriesMap[o.queryHash],this.notify({type:"queryRemoved",query:o}))},n.clear=function(){var o=this;Dn.batch(function(){o.queries.forEach(function(i){o.remove(i)})})},n.get=function(o){return this.queriesMap[o]},n.getAll=function(){return this.queries},n.find=function(o,i){var a=Jl(o,i),s=a[0];return typeof s.exact>"u"&&(s.exact=!0),this.queries.find(function(l){return O6(s,l)})},n.findAll=function(o,i){var a=Jl(o,i),s=a[0];return Object.keys(s).length>0?this.queries.filter(function(l){return O6(s,l)}):this.queries},n.notify=function(o){var i=this;Dn.batch(function(){i.listeners.forEach(function(a){a(o)})})},n.onFocus=function(){var o=this;Dn.batch(function(){o.queries.forEach(function(i){i.onFocus()})})},n.onOnline=function(){var o=this;Dn.batch(function(){o.queries.forEach(function(i){i.onOnline()})})},t}(u1),v1e=function(){function e(n){this.options=At({},n.defaultOptions,n.options),this.mutationId=n.mutationId,this.mutationCache=n.mutationCache,this.observers=[],this.state=n.state||BD(),this.meta=n.meta}var t=e.prototype;return t.setState=function(r){this.dispatch({type:"setState",state:r})},t.addObserver=function(r){this.observers.indexOf(r)===-1&&this.observers.push(r)},t.removeObserver=function(r){this.observers=this.observers.filter(function(o){return o!==r})},t.cancel=function(){return this.retryer?(this.retryer.cancel(),this.retryer.promise.then(Kr).catch(Kr)):Promise.resolve()},t.continue=function(){return this.retryer?(this.retryer.continue(),this.retryer.promise):this.execute()},t.execute=function(){var r=this,o,i=this.state.status==="loading",a=Promise.resolve();return i||(this.dispatch({type:"loading",variables:this.options.variables}),a=a.then(function(){r.mutationCache.config.onMutate==null||r.mutationCache.config.onMutate(r.state.variables,r)}).then(function(){return r.options.onMutate==null?void 0:r.options.onMutate(r.state.variables)}).then(function(s){s!==r.state.context&&r.dispatch({type:"loading",context:s,variables:r.state.variables})})),a.then(function(){return r.executeMutation()}).then(function(s){o=s,r.mutationCache.config.onSuccess==null||r.mutationCache.config.onSuccess(o,r.state.variables,r.state.context,r)}).then(function(){return r.options.onSuccess==null?void 0:r.options.onSuccess(o,r.state.variables,r.state.context)}).then(function(){return r.options.onSettled==null?void 0:r.options.onSettled(o,null,r.state.variables,r.state.context)}).then(function(){return r.dispatch({type:"success",data:o}),o}).catch(function(s){return r.mutationCache.config.onError==null||r.mutationCache.config.onError(s,r.state.variables,r.state.context,r),rb().error(s),Promise.resolve().then(function(){return r.options.onError==null?void 0:r.options.onError(s,r.state.variables,r.state.context)}).then(function(){return r.options.onSettled==null?void 0:r.options.onSettled(void 0,s,r.state.variables,r.state.context)}).then(function(){throw r.dispatch({type:"error",error:s}),s})})},t.executeMutation=function(){var r=this,o;return this.retryer=new ND({fn:function(){return r.options.mutationFn?r.options.mutationFn(r.state.variables):Promise.reject("No mutationFn found")},onFail:function(){r.dispatch({type:"failed"})},onPause:function(){r.dispatch({type:"pause"})},onContinue:function(){r.dispatch({type:"continue"})},retry:(o=this.options.retry)!=null?o:0,retryDelay:this.options.retryDelay}),this.retryer.promise},t.dispatch=function(r){var o=this;this.state=b1e(this.state,r),Dn.batch(function(){o.observers.forEach(function(i){i.onMutationUpdate(r)}),o.mutationCache.notify(o)})},e}();function BD(){return{context:void 0,data:void 0,error:null,failureCount:0,isPaused:!1,status:"idle",variables:void 0}}function b1e(e,t){switch(t.type){case"failed":return At({},e,{failureCount:e.failureCount+1});case"pause":return At({},e,{isPaused:!0});case"continue":return At({},e,{isPaused:!1});case"loading":return At({},e,{context:t.context,data:void 0,error:null,isPaused:!1,status:"loading",variables:t.variables});case"success":return At({},e,{data:t.data,error:null,status:"success",isPaused:!1});case"error":return At({},e,{data:void 0,error:t.error,failureCount:e.failureCount+1,isPaused:!1,status:"error"});case"setState":return At({},e,t.state);default:return e}}var y1e=function(e){l1(t,e);function t(r){var o;return o=e.call(this)||this,o.config=r||{},o.mutations=[],o.mutationId=0,o}var n=t.prototype;return n.build=function(o,i,a){var s=new v1e({mutationCache:this,mutationId:++this.mutationId,options:o.defaultMutationOptions(i),state:a,defaultOptions:i.mutationKey?o.getMutationDefaults(i.mutationKey):void 0,meta:i.meta});return this.add(s),s},n.add=function(o){this.mutations.push(o),this.notify(o)},n.remove=function(o){this.mutations=this.mutations.filter(function(i){return i!==o}),o.cancel(),this.notify(o)},n.clear=function(){var o=this;Dn.batch(function(){o.mutations.forEach(function(i){o.remove(i)})})},n.getAll=function(){return this.mutations},n.find=function(o){return typeof o.exact>"u"&&(o.exact=!0),this.mutations.find(function(i){return D6(o,i)})},n.findAll=function(o){return this.mutations.filter(function(i){return D6(o,i)})},n.notify=function(o){var i=this;Dn.batch(function(){i.listeners.forEach(function(a){a(o)})})},n.onFocus=function(){this.resumePausedMutations()},n.onOnline=function(){this.resumePausedMutations()},n.resumePausedMutations=function(){var o=this.mutations.filter(function(i){return i.state.isPaused});return Dn.batch(function(){return o.reduce(function(i,a){return i.then(function(){return a.continue().catch(Kr)})},Promise.resolve())})},t}(u1);function E1e(){return{onFetch:function(t){t.fetchFn=function(){var n,r,o,i,a,s,l=(n=t.fetchOptions)==null||(r=n.meta)==null?void 0:r.refetchPage,u=(o=t.fetchOptions)==null||(i=o.meta)==null?void 0:i.fetchMore,d=u==null?void 0:u.pageParam,h=(u==null?void 0:u.direction)==="forward",p=(u==null?void 0:u.direction)==="backward",m=((a=t.state.data)==null?void 0:a.pages)||[],v=((s=t.state.data)==null?void 0:s.pageParams)||[],_=CD(),b=_==null?void 0:_.signal,E=v,w=!1,k=t.options.queryFn||function(){return Promise.reject("Missing queryFn")},y=function(pe,se,J,$){return E=$?[se].concat(E):[].concat(E,[se]),$?[J].concat(pe):[].concat(pe,[J])},F=function(pe,se,J,$){if(w)return Promise.reject("Cancelled");if(typeof J>"u"&&!se&&pe.length)return Promise.resolve(pe);var _e={queryKey:t.queryKey,signal:b,pageParam:J,meta:t.meta},ve=k(_e),fe=Promise.resolve(ve).then(function(L){return y(pe,J,L,$)});if(nb(ve)){var R=fe;R.cancel=ve.cancel}return fe},C;if(!m.length)C=F([]);else if(h){var A=typeof d<"u",P=A?d:L6(t.options,m);C=F(m,A,P)}else if(p){var I=typeof d<"u",j=I?d:_1e(t.options,m);C=F(m,I,j,!0)}else(function(){E=[];var U=typeof t.options.getNextPageParam>"u",pe=l&&m[0]?l(m[0],0,m):!0;C=pe?F([],U,v[0]):Promise.resolve(y([],v[0],m[0]));for(var se=function(_e){C=C.then(function(ve){var fe=l&&m[_e]?l(m[_e],_e,m):!0;if(fe){var R=U?v[_e]:L6(t.options,ve);return F(ve,U,R)}return Promise.resolve(y(ve,v[_e],m[_e]))})},J=1;J<m.length;J++)se(J)})();var H=C.then(function(U){return{pages:U,pageParams:E}}),K=H;return K.cancel=function(){w=!0,_==null||_.abort(),nb(C)&&C.cancel()},H}}}}function L6(e,t){return e.getNextPageParam==null?void 0:e.getNextPageParam(t[t.length-1],t)}function _1e(e,t){return e.getPreviousPageParam==null?void 0:e.getPreviousPageParam(t[0],t)}var T1e=function(){function e(n){n===void 0&&(n={}),this.queryCache=n.queryCache||new ID,this.mutationCache=n.mutationCache||new y1e,this.defaultOptions=n.defaultOptions||{},this.queryDefaults=[],this.mutationDefaults=[]}var t=e.prototype;return t.mount=function(){var r=this;this.unsubscribeFocus=Wh.subscribe(function(){Wh.isFocused()&&Hg.isOnline()&&(r.mutationCache.onFocus(),r.queryCache.onFocus())}),this.unsubscribeOnline=Hg.subscribe(function(){Wh.isFocused()&&Hg.isOnline()&&(r.mutationCache.onOnline(),r.queryCache.onOnline())})},t.unmount=function(){var r,o;(r=this.unsubscribeFocus)==null||r.call(this),(o=this.unsubscribeOnline)==null||o.call(this)},t.isFetching=function(r,o){var i=Jl(r,o),a=i[0];return a.fetching=!0,this.queryCache.findAll(a).length},t.isMutating=function(r){return this.mutationCache.findAll(At({},r,{fetching:!0})).length},t.getQueryData=function(r,o){var i;return(i=this.queryCache.find(r,o))==null?void 0:i.state.data},t.getQueriesData=function(r){return this.getQueryCache().findAll(r).map(function(o){var i=o.queryKey,a=o.state,s=a.data;return[i,s]})},t.setQueryData=function(r,o,i){var a=zg(r),s=this.defaultQueryOptions(a);return this.queryCache.build(this,s).setData(o,i)},t.setQueriesData=function(r,o,i){var a=this;return Dn.batch(function(){return a.getQueryCache().findAll(r).map(function(s){var l=s.queryKey;return[l,a.setQueryData(l,o,i)]})})},t.getQueryState=function(r,o){var i;return(i=this.queryCache.find(r,o))==null?void 0:i.state},t.removeQueries=function(r,o){var i=Jl(r,o),a=i[0],s=this.queryCache;Dn.batch(function(){s.findAll(a).forEach(function(l){s.remove(l)})})},t.resetQueries=function(r,o,i){var a=this,s=Jl(r,o,i),l=s[0],u=s[1],d=this.queryCache,h=At({},l,{active:!0});return Dn.batch(function(){return d.findAll(l).forEach(function(p){p.reset()}),a.refetchQueries(h,u)})},t.cancelQueries=function(r,o,i){var a=this,s=Jl(r,o,i),l=s[0],u=s[1],d=u===void 0?{}:u;typeof d.revert>"u"&&(d.revert=!0);var h=Dn.batch(function(){return a.queryCache.findAll(l).map(function(p){return p.cancel(d)})});return Promise.all(h).then(Kr).catch(Kr)},t.invalidateQueries=function(r,o,i){var a,s,l,u=this,d=Jl(r,o,i),h=d[0],p=d[1],m=At({},h,{active:(a=(s=h.refetchActive)!=null?s:h.active)!=null?a:!0,inactive:(l=h.refetchInactive)!=null?l:!1});return Dn.batch(function(){return u.queryCache.findAll(h).forEach(function(v){v.invalidate()}),u.refetchQueries(m,p)})},t.refetchQueries=function(r,o,i){var a=this,s=Jl(r,o,i),l=s[0],u=s[1],d=Dn.batch(function(){return a.queryCache.findAll(l).map(function(p){return p.fetch(void 0,At({},u,{meta:{refetchPage:l==null?void 0:l.refetchPage}}))})}),h=Promise.all(d).then(Kr);return u!=null&&u.throwOnError||(h=h.catch(Kr)),h},t.fetchQuery=function(r,o,i){var a=zg(r,o,i),s=this.defaultQueryOptions(a);typeof s.retry>"u"&&(s.retry=!1);var l=this.queryCache.build(this,s);return l.isStaleByTime(s.staleTime)?l.fetch(s):Promise.resolve(l.state.data)},t.prefetchQuery=function(r,o,i){return this.fetchQuery(r,o,i).then(Kr).catch(Kr)},t.fetchInfiniteQuery=function(r,o,i){var a=zg(r,o,i);return a.behavior=E1e(),this.fetchQuery(a)},t.prefetchInfiniteQuery=function(r,o,i){return this.fetchInfiniteQuery(r,o,i).then(Kr).catch(Kr)},t.cancelMutations=function(){var r=this,o=Dn.batch(function(){return r.mutationCache.getAll().map(function(i){return i.cancel()})});return Promise.all(o).then(Kr).catch(Kr)},t.resumePausedMutations=function(){return this.getMutationCache().resumePausedMutations()},t.executeMutation=function(r){return this.mutationCache.build(this,r).execute()},t.getQueryCache=function(){return this.queryCache},t.getMutationCache=function(){return this.mutationCache},t.getDefaultOptions=function(){return this.defaultOptions},t.setDefaultOptions=function(r){this.defaultOptions=r},t.setQueryDefaults=function(r,o){var i=this.queryDefaults.find(function(a){return Sc(r)===Sc(a.queryKey)});i?i.defaultOptions=o:this.queryDefaults.push({queryKey:r,defaultOptions:o})},t.getQueryDefaults=function(r){var o;return r?(o=this.queryDefaults.find(function(i){return eb(r,i.queryKey)}))==null?void 0:o.defaultOptions:void 0},t.setMutationDefaults=function(r,o){var i=this.mutationDefaults.find(function(a){return Sc(r)===Sc(a.mutationKey)});i?i.defaultOptions=o:this.mutationDefaults.push({mutationKey:r,defaultOptions:o})},t.getMutationDefaults=function(r){var o;return r?(o=this.mutationDefaults.find(function(i){return eb(r,i.mutationKey)}))==null?void 0:o.defaultOptions:void 0},t.defaultQueryOptions=function(r){if(r!=null&&r._defaulted)return r;var o=At({},this.defaultOptions.queries,this.getQueryDefaults(r==null?void 0:r.queryKey),r,{_defaulted:!0});return!o.queryHash&&o.queryKey&&(o.queryHash=ew(o.queryKey,o)),o},t.defaultQueryObserverOptions=function(r){return this.defaultQueryOptions(r)},t.defaultMutationOptions=function(r){return r!=null&&r._defaulted?r:At({},this.defaultOptions.mutations,this.getMutationDefaults(r==null?void 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a=this.currentQuery,s=this.options,l=this.currentResult,u=this.currentResultState,d=this.currentResultOptions,h=o!==a,p=h?o.state:this.currentQueryInitialState,m=h?this.currentResult:this.previousQueryResult,v=o.state,_=v.dataUpdatedAt,b=v.error,E=v.errorUpdatedAt,w=v.isFetching,k=v.status,y=!1,F=!1,C;if(i.optimisticResults){var A=this.hasListeners(),P=!A&&j6(o,i),I=A&&z6(o,a,i,s);(P||I)&&(w=!0,_||(k="loading"))}if(i.keepPreviousData&&!v.dataUpdateCount&&(m!=null&&m.isSuccess)&&k!=="error")C=m.data,_=m.dataUpdatedAt,k=m.status,y=!0;else if(i.select&&typeof v.data<"u")if(l&&v.data===(u==null?void 0:u.data)&&i.select===this.selectFn)C=this.selectResult;else try{this.selectFn=i.select,C=i.select(v.data),i.structuralSharing!==!1&&(C=tb(l==null?void 0:l.data,C)),this.selectResult=C,this.selectError=null}catch(K){rb().error(K),this.selectError=K}else C=v.data;if(typeof i.placeholderData<"u"&&typeof C>"u"&&(k==="loading"||k==="idle")){var 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H={status:k,isLoading:k==="loading",isSuccess:k==="success",isError:k==="error",isIdle:k==="idle",data:C,dataUpdatedAt:_,error:b,errorUpdatedAt:E,failureCount:v.fetchFailureCount,errorUpdateCount:v.errorUpdateCount,isFetched:v.dataUpdateCount>0||v.errorUpdateCount>0,isFetchedAfterMount:v.dataUpdateCount>p.dataUpdateCount||v.errorUpdateCount>p.errorUpdateCount,isFetching:w,isRefetching:w&&k!=="loading",isLoadingError:k==="error"&&v.dataUpdatedAt===0,isPlaceholderData:F,isPreviousData:y,isRefetchError:k==="error"&&v.dataUpdatedAt!==0,isStale:tw(o,i),refetch:this.refetch,remove:this.remove};return H},n.shouldNotifyListeners=function(o,i){if(!i)return!0;var a=this.options,s=a.notifyOnChangeProps,l=a.notifyOnChangePropsExclusions;if(!s&&!l||s==="tracked"&&!this.trackedProps.length)return!0;var u=s==="tracked"?this.trackedProps:s;return Object.keys(o).some(function(d){var h=d,p=o[h]!==i[h],m=u==null?void 0:u.some(function(_){return _===d}),v=l==null?void 0:l.some(function(_){return 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* Lodash <https://lodash.com/>
* Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
* Released under MIT license <https://lodash.com/license>
* Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
* Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
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//! license : MIT
//! momentjs.com
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r=Nt(e);t[Sr]=r===24?0:r});cn(["a","A"],function(e,t,n){n._isPm=n._locale.isPM(e),n._meridiem=e});cn(["h","hh"],function(e,t,n){t[Sr]=Nt(e),yt(n).bigHour=!0});cn("hmm",function(e,t,n){var r=e.length-2;t[Sr]=Nt(e.substr(0,r)),t[_a]=Nt(e.substr(r)),yt(n).bigHour=!0});cn("hmmss",function(e,t,n){var r=e.length-4,o=e.length-2;t[Sr]=Nt(e.substr(0,r)),t[_a]=Nt(e.substr(r,2)),t[tl]=Nt(e.substr(o)),yt(n).bigHour=!0});cn("Hmm",function(e,t,n){var r=e.length-2;t[Sr]=Nt(e.substr(0,r)),t[_a]=Nt(e.substr(r))});cn("Hmmss",function(e,t,n){var r=e.length-4,o=e.length-2;t[Sr]=Nt(e.substr(0,r)),t[_a]=Nt(e.substr(r,2)),t[tl]=Nt(e.substr(o))});function Nme(e){return(e+"").toLowerCase().charAt(0)==="p"}var Fme=/[ap]\.?m?\.?/i,Ime=f1("Hours",!0);function Bme(e,t,n){return e>11?n?"pm":"PM":n?"am":"AM"}var 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See http://momentjs.com/guides/#/warnings/dst-shifted/ for more information",Ege);function Dve(e){return Nn(e*1e3)}function Pve(){return Nn.apply(null,arguments).parseZone()}function JP(e){return e}var Gt=dw.prototype;Gt.calendar=Tpe;Gt.longDateFormat=xpe;Gt.invalidDate=Ape;Gt.ordinal=Ipe;Gt.preparse=JP;Gt.postformat=JP;Gt.relativeTime=Rpe;Gt.pastFuture=Ope;Gt.set=Epe;Gt.eras=lve;Gt.erasParse=uve;Gt.erasConvertYear=cve;Gt.erasAbbrRegex=gve;Gt.erasNameRegex=mve;Gt.erasNarrowRegex=vve;Gt.months=Ype;Gt.monthsShort=Qpe;Gt.monthsParse=Zpe;Gt.monthsRegex=tme;Gt.monthsShortRegex=eme;Gt.week=ome;Gt.firstDayOfYear=sme;Gt.firstDayOfWeek=ame;Gt.weekdays=vme;Gt.weekdaysMin=yme;Gt.weekdaysShort=bme;Gt.weekdaysParse=_me;Gt.weekdaysRegex=Sme;Gt.weekdaysShortRegex=xme;Gt.weekdaysMinRegex=Cme;Gt.isPM=Nme;Gt.meridiem=Bme;function db(e,t,n,r){var o=El(),i=gs().set(r,t);return o[n](i,e)}function eM(e,t,n){if(fl(e)&&(t=e,e=void 0),e=e||"",t!=null)return db(e,t,n,"month");var r,o=[];for(r=0;r<12;r++)o[r]=db(e,r,n,"month");return o}function Aw(e,t,n,r){typeof e=="boolean"?(fl(t)&&(n=t,t=void 0),t=t||""):(t=e,n=t,e=!1,fl(t)&&(n=t,t=void 0),t=t||"");var o=El(),i=e?o._week.dow:0,a,s=[];if(n!=null)return db(t,(n+i)%7,r,"day");for(a=0;a<7;a++)s[a]=db(t,(a+i)%7,r,"day");return s}function Mve(e,t){return eM(e,t,"months")}function Lve(e,t){return eM(e,t,"monthsShort")}function jve(e,t,n){return Aw(e,t,n,"weekdays")}function zve(e,t,n){return Aw(e,t,n,"weekdaysShort")}function Hve(e,t,n){return Aw(e,t,n,"weekdaysMin")}Tu("en",{eras:[{since:"0001-01-01",until:1/0,offset:1,name:"Anno Domini",narrow:"AD",abbr:"AD"},{since:"0000-12-31",until:-1/0,offset:1,name:"Before Christ",narrow:"BC",abbr:"BC"}],dayOfMonthOrdinalParse:/\d{1,2}(th|st|nd|rd)/,ordinal:function(e){var t=e%10,n=Nt(e%100/10)===1?"th":t===1?"st":t===2?"nd":t===3?"rd":"th";return e+n}});De.lang=ta("moment.lang is deprecated. 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e.languages.go["class-name"]}Hw.displayName="ini";Hw.aliases=[];function Hw(e){e.languages.ini={comment:{pattern:/(^[ \f\t\v]*)[#;][^\n\r]*/m,lookbehind:!0},section:{pattern:/(^[ \f\t\v]*)\[[^\n\r\]]*\]?/m,lookbehind:!0,inside:{"section-name":{pattern:/(^\[[ \f\t\v]*)[^ \f\t\v\]]+(?:[ \f\t\v]+[^ \f\t\v\]]+)*/,lookbehind:!0,alias:"selector"},punctuation:/\[|\]/}},key:{pattern:/(^[ \f\t\v]*)[^ \f\n\r\t\v=]+(?:[ \f\t\v]+[^ \f\n\r\t\v=]+)*(?=[ \f\t\v]*=)/m,lookbehind:!0,alias:"attr-name"},value:{pattern:/(=[ \f\t\v]*)[^ \f\n\r\t\v]+(?:[ \f\t\v]+[^ \f\n\r\t\v]+)*/,lookbehind:!0,alias:"attr-value",inside:{"inner-value":{pattern:/^("|').+(?=\1$)/,lookbehind:!0}}},punctuation:/=/}}Uw.displayName="java";Uw.aliases=[];function Uw(e){e.register(bs),function(t){var n=/\b(?:abstract|assert|boolean|break|byte|case|catch|char|class|const|continue|default|do|double|else|enum|exports|extends|final|finally|float|for|goto|if|implements|import|instanceof|int|interface|long|module|native|new|non-sealed|null|open|opens|package|permits|private|protected|provides|public|record(?!\s*[(){}[\]<>=%~.:,;?+\-*/&|^])|requires|return|sealed|short|static|strictfp|super|switch|synchronized|this|throw|throws|to|transient|transitive|try|uses|var|void|volatile|while|with|yield)\b/,r=/(?:[a-z]\w*\s*\.\s*)*(?:[A-Z]\w*\s*\.\s*)*/.source,o={pattern:RegExp(/(^|[^\w.])/.source+r+/[A-Z](?:[\d_A-Z]*[a-z]\w*)?\b/.source),lookbehind:!0,inside:{namespace:{pattern:/^[a-z]\w*(?:\s*\.\s*[a-z]\w*)*(?:\s*\.)?/,inside:{punctuation:/\./}},punctuation:/\./}};t.languages.java=t.languages.extend("clike",{string:{pattern:/(^|[^\\])"(?:\\.|[^"\\\r\n])*"/,lookbehind:!0,greedy:!0},"class-name":[o,{pattern:RegExp(/(^|[^\w.])/.source+r+/[A-Z]\w*(?=\s+\w+\s*[;,=()]|\s*(?:\[[\s,]*\]\s*)?::\s*new\b)/.source),lookbehind:!0,inside:o.inside},{pattern:RegExp(/(\b(?:class|enum|extends|implements|instanceof|interface|new|record|throws)\s+)/.source+r+/[A-Z]\w*\b/.source),lookbehind:!0,inside:o.inside}],keyword:n,function:[t.languages.clike.function,{pattern:/(::\s*)[a-z_]\w*/,lookbehind:!0}],number:/\b0b[01][01_]*L?\b|\b0x(?:\.[\da-f_p+-]+|[\da-f_]+(?:\.[\da-f_p+-]+)?)\b|(?:\b\d[\d_]*(?:\.[\d_]*)?|\B\.\d[\d_]*)(?:e[+-]?\d[\d_]*)?[dfl]?/i,operator:{pattern:/(^|[^.])(?:<<=?|>>>?=?|->|--|\+\+|&&|\|\||::|[?:~]|[-+*/%&|^!=<>]=?)/m,lookbehind:!0},constant:/\b[A-Z][A-Z_\d]+\b/}),t.languages.insertBefore("java","string",{"triple-quoted-string":{pattern:/"""[ 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n.source})),lookbehind:!0,inside:{punctuation:/\./}}})}(e)}qw.displayName="regex";qw.aliases=[];function qw(e){(function(t){var n={pattern:/\\[\\(){}[\]^$+*?|.]/,alias:"escape"},r=/\\(?:x[\da-fA-F]{2}|u[\da-fA-F]{4}|u\{[\da-fA-F]+\}|0[0-7]{0,2}|[123][0-7]{2}|c[a-zA-Z]|.)/,o={pattern:/\.|\\[wsd]|\\p\{[^{}]+\}/i,alias:"class-name"},i={pattern:/\\[wsd]|\\p\{[^{}]+\}/i,alias:"class-name"},a="(?:[^\\\\-]|"+r.source+")",s=RegExp(a+"-"+a),l={pattern:/(<|')[^<>']+(?=[>']$)/,lookbehind:!0,alias:"variable"};t.languages.regex={"char-class":{pattern:/((?:^|[^\\])(?:\\\\)*)\[(?:[^\\\]]|\\[\s\S])*\]/,lookbehind:!0,inside:{"char-class-negation":{pattern:/(^\[)\^/,lookbehind:!0,alias:"operator"},"char-class-punctuation":{pattern:/^\[|\]$/,alias:"punctuation"},range:{pattern:s,inside:{escape:r,"range-punctuation":{pattern:/-/,alias:"operator"}}},"special-escape":n,"char-set":i,escape:r}},"special-escape":n,"char-set":o,backreference:[{pattern:/\\(?![123][0-7]{2})[1-9]/,alias:"keyword"},{pattern:/\\k<[^<>']+>/,alias:"keyword",inside:{"group-name":l}}],anchor:{pattern:/[$^]|\\[ABbGZz]/,alias:"function"},escape:r,group:[{pattern:/\((?:\?(?:<[^<>']+>|'[^<>']+'|[>:]|<?[=!]|[idmnsuxU]+(?:-[idmnsuxU]+)?:?))?/,alias:"punctuation",inside:{"group-name":l}},{pattern:/\)/,alias:"punctuation"}],quantifier:{pattern:/(?:[+*?]|\{\d+(?:,\d*)?\})[?+]?/,alias:"number"},alternation:{pattern:/\|/,alias:"keyword"}}})(e)}Iy.displayName="javascript";Iy.aliases=["js"];function Iy(e){e.register(bs),e.languages.javascript=e.languages.extend("clike",{"class-name":[e.languages.clike["class-name"],{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$A-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\.(?:constructor|prototype))/,lookbehind:!0}],keyword:[{pattern:/((?:^|\})\s*)catch\b/,lookbehind:!0},{pattern:/(^|[^.]|\.\.\.\s*)\b(?:as|assert(?=\s*\{)|async(?=\s*(?:function\b|\(|[$\w\xA0-\uFFFF]|$))|await|break|case|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally(?=\s*(?:\{|$))|for|from(?=\s*(?:['"]|$))|function|(?:get|set)(?=\s*(?:[#\[$\w\xA0-\uFFFF]|$))|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)\b/,lookbehind:!0}],function:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*(?:\.\s*(?:apply|bind|call)\s*)?\()/,number:{pattern:RegExp(/(^|[^\w$])/.source+"(?:"+(/NaN|Infinity/.source+"|"+/0[bB][01]+(?:_[01]+)*n?/.source+"|"+/0[oO][0-7]+(?:_[0-7]+)*n?/.source+"|"+/0[xX][\dA-Fa-f]+(?:_[\dA-Fa-f]+)*n?/.source+"|"+/\d+(?:_\d+)*n/.source+"|"+/(?:\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\.\d+(?:_\d+)*)(?:[Ee][+-]?\d+(?:_\d+)*)?/.source)+")"+/(?![\w$])/.source),lookbehind:!0},operator:/--|\+\+|\*\*=?|=>|&&=?|\|\|=?|[!=]==|<<=?|>>>?=?|[-+*/%&|^!=<>]=?|\.{3}|\?\?=?|\?\.?|[~:]/}),e.languages.javascript["class-name"][0].pattern=/(\b(?:class|extends|implements|instanceof|interface|new)\s+)[\w.\\]+/,e.languages.insertBefore("javascript","keyword",{regex:{pattern:RegExp(/((?:^|[^$\w\xA0-\uFFFF."'\])\s]|\b(?:return|yield))\s*)/.source+/\//.source+"(?:"+/(?:\[(?:[^\]\\\r\n]|\\.)*\]|\\.|[^/\\\[\r\n])+\/[dgimyus]{0,7}/.source+"|"+/(?:\[(?:[^[\]\\\r\n]|\\.|\[(?:[^[\]\\\r\n]|\\.|\[(?:[^[\]\\\r\n]|\\.)*\])*\])*\]|\\.|[^/\\\[\r\n])+\/[dgimyus]{0,7}v[dgimyus]{0,7}/.source+")"+/(?=(?:\s|\/\*(?:[^*]|\*(?!\/))*\*\/)*(?:$|[\r\n,.;:})\]]|\/\/))/.source),lookbehind:!0,greedy:!0,inside:{"regex-source":{pattern:/^(\/)[\s\S]+(?=\/[a-z]*$)/,lookbehind:!0,alias:"language-regex",inside:e.languages.regex},"regex-delimiter":/^\/|\/$/,"regex-flags":/^[a-z]+$/}},"function-variable":{pattern:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*[=:]\s*(?:async\s*)?(?:\bfunction\b|(?:\((?:[^()]|\([^()]*\))*\)|(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)\s*=>))/,alias:"function"},parameter:[{pattern:/(function(?:\s+(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)?\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\))/,lookbehind:!0,inside:e.languages.javascript},{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*=>)/i,lookbehind:!0,inside:e.languages.javascript},{pattern:/(\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*=>)/,lookbehind:!0,inside:e.languages.javascript},{pattern:/((?:\b|\s|^)(?!(?:as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)(?![$\w\xA0-\uFFFF]))(?:(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*\s*)\(\s*|\]\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*\{)/,lookbehind:!0,inside:e.languages.javascript}],constant:/\b[A-Z](?:[A-Z_]|\dx?)*\b/}),e.languages.insertBefore("javascript","string",{hashbang:{pattern:/^#!.*/,greedy:!0,alias:"comment"},"template-string":{pattern:/`(?:\\[\s\S]|\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}|(?!\$\{)[^\\`])*`/,greedy:!0,inside:{"template-punctuation":{pattern:/^`|`$/,alias:"string"},interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}/,lookbehind:!0,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:e.languages.javascript}},string:/[\s\S]+/}},"string-property":{pattern:/((?:^|[,{])[ \t]*)(["'])(?:\\(?:\r\n|[\s\S])|(?!\2)[^\\\r\n])*\2(?=\s*:)/m,lookbehind:!0,greedy:!0,alias:"property"}}),e.languages.insertBefore("javascript","operator",{"literal-property":{pattern:/((?:^|[,{])[ \t]*)(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*:)/m,lookbehind:!0,alias:"property"}}),e.languages.markup&&(e.languages.markup.tag.addInlined("script","javascript"),e.languages.markup.tag.addAttribute(/on(?:abort|blur|change|click|composition(?:end|start|update)|dblclick|error|focus(?:in|out)?|key(?:down|up)|load|mouse(?:down|enter|leave|move|out|over|up)|reset|resize|scroll|select|slotchange|submit|unload|wheel)/.source,"javascript")),e.languages.js=e.languages.javascript}$w.displayName="json";$w.aliases=["webmanifest"];function $w(e){e.languages.json={property:{pattern:/(^|[^\\])"(?:\\.|[^\\"\r\n])*"(?=\s*:)/,lookbehind:!0,greedy:!0},string:{pattern:/(^|[^\\])"(?:\\.|[^\\"\r\n])*"(?!\s*:)/,lookbehind:!0,greedy:!0},comment:{pattern:/\/\/.*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},number:/-?\b\d+(?:\.\d+)?(?:e[+-]?\d+)?\b/i,punctuation:/[{}[\],]/,operator:/:/,boolean:/\b(?:false|true)\b/,null:{pattern:/\bnull\b/,alias:"keyword"}},e.languages.webmanifest=e.languages.json}Ww.displayName="kotlin";Ww.aliases=["kt","kts"];function Ww(e){e.register(bs),function(t){t.languages.kotlin=t.languages.extend("clike",{keyword:{pattern:/(^|[^.])\b(?:abstract|actual|annotation|as|break|by|catch|class|companion|const|constructor|continue|crossinline|data|do|dynamic|else|enum|expect|external|final|finally|for|fun|get|if|import|in|infix|init|inline|inner|interface|internal|is|lateinit|noinline|null|object|open|operator|out|override|package|private|protected|public|reified|return|sealed|set|super|suspend|tailrec|this|throw|to|try|typealias|val|var|vararg|when|where|while)\b/,lookbehind:!0},function:[{pattern:/(?:`[^\r\n`]+`|\b\w+)(?=\s*\()/,greedy:!0},{pattern:/(\.)(?:`[^\r\n`]+`|\w+)(?=\s*\{)/,lookbehind:!0,greedy:!0}],number:/\b(?:0[xX][\da-fA-F]+(?:_[\da-fA-F]+)*|0[bB][01]+(?:_[01]+)*|\d+(?:_\d+)*(?:\.\d+(?:_\d+)*)?(?:[eE][+-]?\d+(?:_\d+)*)?[fFL]?)\b/,operator:/\+[+=]?|-[-=>]?|==?=?|!(?:!|==?)?|[\/*%<>]=?|[?:]:?|\.\.|&&|\|\||\b(?:and|inv|or|shl|shr|ushr|xor)\b/}),delete t.languages.kotlin["class-name"];var n={"interpolation-punctuation":{pattern:/^\$\{?|\}$/,alias:"punctuation"},expression:{pattern:/[\s\S]+/,inside:t.languages.kotlin}};t.languages.insertBefore("kotlin","string",{"string-literal":[{pattern:/"""(?:[^$]|\$(?:(?!\{)|\{[^{}]*\}))*?"""/,alias:"multiline",inside:{interpolation:{pattern:/\$(?:[a-z_]\w*|\{[^{}]*\})/i,inside:n},string:/[\s\S]+/}},{pattern:/"(?:[^"\\\r\n$]|\\.|\$(?:(?!\{)|\{[^{}]*\}))*"/,alias:"singleline",inside:{interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$(?:[a-z_]\w*|\{[^{}]*\})/i,lookbehind:!0,inside:n},string:/[\s\S]+/}}],char:{pattern:/'(?:[^'\\\r\n]|\\(?:.|u[a-fA-F0-9]{0,4}))'/,greedy:!0}}),delete t.languages.kotlin.string,t.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),t.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),t.languages.kt=t.languages.kotlin,t.languages.kts=t.languages.kotlin}(e)}Gw.displayName="less";Gw.aliases=[];function Gw(e){e.register(v1),e.languages.less=e.languages.extend("css",{comment:[/\/\*[\s\S]*?\*\//,{pattern:/(^|[^\\])\/\/.*/,lookbehind:!0}],atrule:{pattern:/@[\w-](?:\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{punctuation:/[:()]/}},selector:{pattern:/(?:@\{[\w-]+\}|[^{};\s@])(?:@\{[\w-]+\}|\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};@\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{variable:/@+[\w-]+/}},property:/(?:@\{[\w-]+\}|[\w-])+(?:\+_?)?(?=\s*:)/,operator:/[+\-*\/]/}),e.languages.insertBefore("less","property",{variable:[{pattern:/@[\w-]+\s*:/,inside:{punctuation:/:/}},/@@?[\w-]+/],"mixin-usage":{pattern:/([{;]\s*)[.#](?!\d)[\w-].*?(?=[(;])/,lookbehind:!0,alias:"function"}})}Kw.displayName="lua";Kw.aliases=[];function Kw(e){e.languages.lua={comment:/^#!.+|--(?:\[(=*)\[[\s\S]*?\]\1\]|.*)/m,string:{pattern:/(["'])(?:(?!\1)[^\\\r\n]|\\z(?:\r\n|\s)|\\(?:\r\n|[^z]))*\1|\[(=*)\[[\s\S]*?\]\2\]/,greedy:!0},number:/\b0x[a-f\d]+(?:\.[a-f\d]*)?(?:p[+-]?\d+)?\b|\b\d+(?:\.\B|(?:\.\d*)?(?:e[+-]?\d+)?\b)|\B\.\d+(?:e[+-]?\d+)?\b/i,keyword:/\b(?:and|break|do|else|elseif|end|false|for|function|goto|if|in|local|nil|not|or|repeat|return|then|true|until|while)\b/,function:/(?!\d)\w+(?=\s*(?:[({]))/,operator:[/[-+*%^&|#]|\/\/?|<[<=]?|>[>=]?|[=~]=?/,{pattern:/(^|[^.])\.\.(?!\.)/,lookbehind:!0}],punctuation:/[\[\](){},;]|\.+|:+/}}Vw.displayName="makefile";Vw.aliases=[];function Vw(e){e.languages.makefile={comment:{pattern:/(^|[^\\])#(?:\\(?:\r\n|[\s\S])|[^\\\r\n])*/,lookbehind:!0},string:{pattern:/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,greedy:!0},"builtin-target":{pattern:/\.[A-Z][^:#=\s]+(?=\s*:(?!=))/,alias:"builtin"},target:{pattern:/^(?:[^:=\s]|[ 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y.a.createElement("div",{className:"react-json-view",style:l(l({},J(ce,"app-container").style),He)},y.a.createElement(zu,{message:ee,active:z,theme:ce,rjvId:this.rjvId}),y.a.createElement(_1,Object.assign({},this.props,{src:te,name:he,theme:ce,type:j(te),rjvId:this.rjvId})),y.a.createElement(Zc,{active:ue,theme:ce,rjvId:this.rjvId,defaultValue:tt}))}}],[{key:"getDerivedStateFromProps",value:function(B,z){if(B.src!==z.prevSrc||B.name!==z.prevName||B.theme!==z.prevTheme){var ee={src:B.src,name:B.name,theme:B.theme,validationMessage:B.validationMessage,prevSrc:B.src,prevName:B.name,prevTheme:B.theme};return O.validateState(ee)}return null}}]),O}(y.a.PureComponent);Es.defaultProps={src:{},name:"root",theme:"rjv-default",collapsed:!1,collapseStringsAfterLength:!1,shouldCollapse:!1,sortKeys:!1,quotesOnKeys:!0,groupArraysAfterLength:100,indentWidth:4,enableClipboard:!0,displayObjectSize:!0,displayDataTypes:!0,onEdit:!1,onDelete:!1,onAdd:!1,onSelect:!1,iconStyle:"triangle",style:{},validationMessage:"Validation Error",defaultValue:null,displayArrayKey:!0},Es.validateState=function(q){var W={};return j(q.theme)!=="object"||function(O){var B=["base00","base01","base02","base03","base04","base05","base06","base07","base08","base09","base0A","base0B","base0C","base0D","base0E","base0F"];if(j(O)==="object"){for(var z=0;z<B.length;z++)if(!(B[z]in O))return!1;return!0}return!1}(q.theme)||(console.error("react-json-view error:","theme prop must be a theme name or valid base-16 theme object.",'defaulting to "rjv-default" theme'),W.theme="rjv-default"),j(q.src)!=="object"&&j(q.src)!=="array"&&(console.error("react-json-view 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this.skipNextNewLine&&t===ac.LINE_FEED?(this.skipNextNewLine=!1,this._addGap(),this.advance()):t===ac.CARRIAGE_RETURN?(this.skipNextNewLine=!0,ac.LINE_FEED):(this.skipNextNewLine=!1,Vf.isSurrogate(t)&&(t=this._processSurrogate(t)),t>31&&t<127||t===ac.LINE_FEED||t===ac.CARRIAGE_RETURN||t>159&&t<64976||this._checkForProblematicCharacters(t),t)}_checkForProblematicCharacters(t){Vf.isControlCodePoint(t)?this._err(G9.controlCharacterInInputStream):Vf.isUndefinedCodePoint(t)&&this._err(G9.noncharacterInInputStream)}retreat(){this.pos===this.lastGapPos&&(this.lastGapPos=this.gapStack.pop(),this.pos--),this.pos--}};var o_e=r_e,i_e=new 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this.state=Z9):t===Y.APOSTROPHE?(this.currentToken.publicId="",this.state=J9):t===Y.GREATER_THAN_SIGN?(this._err(ye.missingDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this.state=It,this._emitCurrentToken()):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.missingQuoteBeforeDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(Ds)))}[Z9](t){t===Y.QUOTATION_MARK?this.state=eE:t===Y.NULL?(this._err(ye.unexpectedNullCharacter),this.currentToken.publicId+=ln.REPLACEMENT_CHARACTER):t===Y.GREATER_THAN_SIGN?(this._err(ye.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=It):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=Wn(t)}[J9](t){t===Y.APOSTROPHE?this.state=eE:t===Y.NULL?(this._err(ye.unexpectedNullCharacter),this.currentToken.publicId+=ln.REPLACEMENT_CHARACTER):t===Y.GREATER_THAN_SIGN?(this._err(ye.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=It):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=Wn(t)}[eE](t){Sn(t)?this.state=tN:t===Y.GREATER_THAN_SIGN?(this.state=It,this._emitCurrentToken()):t===Y.QUOTATION_MARK?(this._err(ye.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=fh):t===Y.APOSTROPHE?(this._err(ye.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=dh):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(Ds))}[tN](t){Sn(t)||(t===Y.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=It):t===Y.QUOTATION_MARK?(this.currentToken.systemId="",this.state=fh):t===Y.APOSTROPHE?(this.currentToken.systemId="",this.state=dh):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(Ds)))}[nN](t){Sn(t)?this.state=rN:t===Y.QUOTATION_MARK?(this._err(ye.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=fh):t===Y.APOSTROPHE?(this._err(ye.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=dh):t===Y.GREATER_THAN_SIGN?(this._err(ye.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=It,this._emitCurrentToken()):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(Ds))}[rN](t){Sn(t)||(t===Y.QUOTATION_MARK?(this.currentToken.systemId="",this.state=fh):t===Y.APOSTROPHE?(this.currentToken.systemId="",this.state=dh):t===Y.GREATER_THAN_SIGN?(this._err(ye.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=It,this._emitCurrentToken()):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(Ds)))}[fh](t){t===Y.QUOTATION_MARK?this.state=tE:t===Y.NULL?(this._err(ye.unexpectedNullCharacter),this.currentToken.systemId+=ln.REPLACEMENT_CHARACTER):t===Y.GREATER_THAN_SIGN?(this._err(ye.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=It):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=Wn(t)}[dh](t){t===Y.APOSTROPHE?this.state=tE:t===Y.NULL?(this._err(ye.unexpectedNullCharacter),this.currentToken.systemId+=ln.REPLACEMENT_CHARACTER):t===Y.GREATER_THAN_SIGN?(this._err(ye.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=It):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=Wn(t)}[tE](t){Sn(t)||(t===Y.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=It):t===Y.EOF?(this._err(ye.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(ye.unexpectedCharacterAfterDoctypeSystemIdentifier),this._reconsumeInState(Ds)))}[Ds](t){t===Y.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=It):t===Y.NULL?this._err(ye.unexpectedNullCharacter):t===Y.EOF&&(this._emitCurrentToken(),this._emitEOFToken())}[ug](t){t===Y.RIGHT_SQUARE_BRACKET?this.state=oN:t===Y.EOF?(this._err(ye.eofInCdata),this._emitEOFToken()):this._emitCodePoint(t)}[oN](t){t===Y.RIGHT_SQUARE_BRACKET?this.state=iN:(this._emitChars("]"),this._reconsumeInState(ug))}[iN](t){t===Y.GREATER_THAN_SIGN?this.state=It:t===Y.RIGHT_SQUARE_BRACKET?this._emitChars("]"):(this._emitChars("]]"),this._reconsumeInState(ug))}[Rf](t){this.tempBuff=[Y.AMPERSAND],t===Y.NUMBER_SIGN?(this.tempBuff.push(t),this.state=lN):nE(t)?this._reconsumeInState(aN):(this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[aN](t){const n=this._matchNamedCharacterReference(t);if(this._ensureHibernation())this.tempBuff=[Y.AMPERSAND];else if(n){const r=this.tempBuff[this.tempBuff.length-1]===Y.SEMICOLON;this._isCharacterReferenceAttributeQuirk(r)||(r||this._errOnNextCodePoint(ye.missingSemicolonAfterCharacterReference),this.tempBuff=n),this._flushCodePointsConsumedAsCharacterReference(),this.state=this.returnState}else this._flushCodePointsConsumedAsCharacterReference(),this.state=sN}[sN](t){nE(t)?this._isCharacterReferenceInAttribute()?this.currentAttr.value+=Wn(t):this._emitCodePoint(t):(t===Y.SEMICOLON&&this._err(ye.unknownNamedCharacterReference),this._reconsumeInState(this.returnState))}[lN](t){this.charRefCode=0,t===Y.LATIN_SMALL_X||t===Y.LATIN_CAPITAL_X?(this.tempBuff.push(t),this.state=uN):this._reconsumeInState(cN)}[uN](t){u_e(t)?this._reconsumeInState(fN):(this._err(ye.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[cN](t){Qh(t)?this._reconsumeInState(dN):(this._err(ye.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[fN](t){gL(t)?this.charRefCode=this.charRefCode*16+t-55:vL(t)?this.charRefCode=this.charRefCode*16+t-87:Qh(t)?this.charRefCode=this.charRefCode*16+t-48:t===Y.SEMICOLON?this.state=hh:(this._err(ye.missingSemicolonAfterCharacterReference),this._reconsumeInState(hh))}[dN](t){Qh(t)?this.charRefCode=this.charRefCode*10+t-48:t===Y.SEMICOLON?this.state=hh:(this._err(ye.missingSemicolonAfterCharacterReference),this._reconsumeInState(hh))}[hh](){if(this.charRefCode===Y.NULL)this._err(ye.nullCharacterReference),this.charRefCode=Y.REPLACEMENT_CHARACTER;else if(this.charRefCode>1114111)this._err(ye.characterReferenceOutsideUnicodeRange),this.charRefCode=Y.REPLACEMENT_CHARACTER;else if(ln.isSurrogate(this.charRefCode))this._err(ye.surrogateCharacterReference),this.charRefCode=Y.REPLACEMENT_CHARACTER;else if(ln.isUndefinedCodePoint(this.charRefCode))this._err(ye.noncharacterCharacterReference);else if(ln.isControlCodePoint(this.charRefCode)||this.charRefCode===Y.CARRIAGE_RETURN){this._err(ye.controlCharacterReference);const t=s_e[this.charRefCode];t&&(this.charRefCode=t)}this.tempBuff=[this.charRefCode],this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState)}};oa.CHARACTER_TOKEN="CHARACTER_TOKEN";oa.NULL_CHARACTER_TOKEN="NULL_CHARACTER_TOKEN";oa.WHITESPACE_CHARACTER_TOKEN="WHITESPACE_CHARACTER_TOKEN";oa.START_TAG_TOKEN="START_TAG_TOKEN";oa.END_TAG_TOKEN="END_TAG_TOKEN";oa.COMMENT_TOKEN="COMMENT_TOKEN";oa.DOCTYPE_TOKEN="DOCTYPE_TOKEN";oa.EOF_TOKEN="EOF_TOKEN";oa.HIBERNATION_TOKEN="HIBERNATION_TOKEN";oa.MODE={DATA:It,RCDATA:Yf,RAWTEXT:Sh,SCRIPT_DATA:zs,PLAINTEXT:mL};oa.getTokenAttr=function(e,t){for(let n=e.attrs.length-1;n>=0;n--)if(e.attrs[n].name===t)return e.attrs[n].value;return null};var Oy=oa,Fa={};const rE=Fa.NAMESPACES={HTML:"http://www.w3.org/1999/xhtml",MATHML:"http://www.w3.org/1998/Math/MathML",SVG:"http://www.w3.org/2000/svg",XLINK:"http://www.w3.org/1999/xlink",XML:"http://www.w3.org/XML/1998/namespace",XMLNS:"http://www.w3.org/2000/xmlns/"};Fa.ATTRS={TYPE:"type",ACTION:"action",ENCODING:"encoding",PROMPT:"prompt",NAME:"name",COLOR:"color",FACE:"face",SIZE:"size"};Fa.DOCUMENT_MODE={NO_QUIRKS:"no-quirks",QUIRKS:"quirks",LIMITED_QUIRKS:"limited-quirks"};const 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n=this.current;if(this.pop(),n===t)break}}popUntilNumberedHeaderPopped(){for(;this.stackTop>-1;){const t=this.currentTagName,n=this.treeAdapter.getNamespaceURI(this.current);if(this.pop(),t===Fe.H1||t===Fe.H2||t===Fe.H3||t===Fe.H4||t===Fe.H5||t===Fe.H6&&n===un.HTML)break}}popUntilTableCellPopped(){for(;this.stackTop>-1;){const 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n=this.stackTop;n>=0;n--)if(this.items[n]===t){this.items.splice(n,1),this.stackTop--,this._updateCurrentElement();break}}tryPeekProperlyNestedBodyElement(){const t=this.items[1];return t&&this.treeAdapter.getTagName(t)===Fe.BODY?t:null}contains(t){return this._indexOf(t)>-1}getCommonAncestor(t){let n=this._indexOf(t);return--n>=0?this.items[n]:null}isRootHtmlElementCurrent(){return this.stackTop===0&&this.currentTagName===Fe.HTML}hasInScope(t){for(let n=this.stackTop;n>=0;n--){const r=this.treeAdapter.getTagName(this.items[n]),o=this.treeAdapter.getNamespaceURI(this.items[n]);if(r===t&&o===un.HTML)return!0;if(cg(r,o))return!1}return!0}hasNumberedHeaderInScope(){for(let t=this.stackTop;t>=0;t--){const n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if((n===Fe.H1||n===Fe.H2||n===Fe.H3||n===Fe.H4||n===Fe.H5||n===Fe.H6)&&r===un.HTML)return!0;if(cg(n,r))return!1}return!0}hasInListItemScope(t){for(let n=this.stackTop;n>=0;n--){const 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t=this.fragmentContext;do{if(this.treeAdapter.getTagName(t)===x.FORM){this.formElement=t;break}t=this.treeAdapter.getParentNode(t)}while(t)}_initTokenizerForFragmentParsing(){if(this.treeAdapter.getNamespaceURI(this.fragmentContext)===ze.HTML){const t=this.treeAdapter.getTagName(this.fragmentContext);t===x.TITLE||t===x.TEXTAREA?this.tokenizer.state=X.MODE.RCDATA:t===x.STYLE||t===x.XMP||t===x.IFRAME||t===x.NOEMBED||t===x.NOFRAMES||t===x.NOSCRIPT?this.tokenizer.state=X.MODE.RAWTEXT:t===x.SCRIPT?this.tokenizer.state=X.MODE.SCRIPT_DATA:t===x.PLAINTEXT&&(this.tokenizer.state=X.MODE.PLAINTEXT)}}_setDocumentType(t){const n=t.name||"",r=t.publicId||"",o=t.systemId||"";this.treeAdapter.setDocumentType(this.document,n,r,o)}_attachElementToTree(t){if(this._shouldFosterParentOnInsertion())this._fosterParentElement(t);else{const n=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.appendChild(n,t)}}_appendElement(t,n){const r=this.treeAdapter.createElement(t.tagName,n,t.attrs);this._attachElementToTree(r)}_insertElement(t,n){const r=this.treeAdapter.createElement(t.tagName,n,t.attrs);this._attachElementToTree(r),this.openElements.push(r)}_insertFakeElement(t){const n=this.treeAdapter.createElement(t,ze.HTML,[]);this._attachElementToTree(n),this.openElements.push(n)}_insertTemplate(t){const n=this.treeAdapter.createElement(t.tagName,ze.HTML,t.attrs),r=this.treeAdapter.createDocumentFragment();this.treeAdapter.setTemplateContent(n,r),this._attachElementToTree(n),this.openElements.push(n)}_insertFakeRootElement(){const t=this.treeAdapter.createElement(x.HTML,ze.HTML,[]);this.treeAdapter.appendChild(this.openElements.current,t),this.openElements.push(t)}_appendCommentNode(t,n){const r=this.treeAdapter.createCommentNode(t.data);this.treeAdapter.appendChild(n,r)}_insertCharacters(t){if(this._shouldFosterParentOnInsertion())this._fosterParentText(t.chars);else{const n=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.insertText(n,t.chars)}}_adoptNodes(t,n){for(let r=this.treeAdapter.getFirstChild(t);r;r=this.treeAdapter.getFirstChild(t))this.treeAdapter.detachNode(r),this.treeAdapter.appendChild(n,r)}_shouldProcessTokenInForeignContent(t){const n=this._getAdjustedCurrentElement();if(!n||n===this.document)return!1;const r=this.treeAdapter.getNamespaceURI(n);if(r===ze.HTML||this.treeAdapter.getTagName(n)===x.ANNOTATION_XML&&r===ze.MATHML&&t.type===X.START_TAG_TOKEN&&t.tagName===x.SVG)return!1;const o=t.type===X.CHARACTER_TOKEN||t.type===X.NULL_CHARACTER_TOKEN||t.type===X.WHITESPACE_CHARACTER_TOKEN;return(t.type===X.START_TAG_TOKEN&&t.tagName!==x.MGLYPH&&t.tagName!==x.MALIGNMARK||o)&&this._isIntegrationPoint(n,ze.MATHML)||(t.type===X.START_TAG_TOKEN||o)&&this._isIntegrationPoint(n,ze.HTML)?!1:t.type!==X.EOF_TOKEN}_processToken(t){kN[this.insertionMode][t.type](this,t)}_processTokenInBodyMode(t){kN[as][t.type](this,t)}_processTokenInForeignContent(t){t.type===X.CHARACTER_TOKEN?GTe(this,t):t.type===X.NULL_CHARACTER_TOKEN?WTe(this,t):t.type===X.WHITESPACE_CHARACTER_TOKEN?Wo(this,t):t.type===X.COMMENT_TOKEN?Tr(this,t):t.type===X.START_TAG_TOKEN?KTe(this,t):t.type===X.END_TAG_TOKEN&&VTe(this,t)}_processInputToken(t){this._shouldProcessTokenInForeignContent(t)?this._processTokenInForeignContent(t):this._processToken(t),t.type===X.START_TAG_TOKEN&&t.selfClosing&&!t.ackSelfClosing&&this._err(Xr.nonVoidHtmlElementStartTagWithTrailingSolidus)}_isIntegrationPoint(t,n){const r=this.treeAdapter.getTagName(t),o=this.treeAdapter.getNamespaceURI(t),i=this.treeAdapter.getAttrList(t);return Ya.isIntegrationPoint(r,o,i,n)}_reconstructActiveFormattingElements(){const t=this.activeFormattingElements.length;if(t){let n=t,r=null;do if(n--,r=this.activeFormattingElements.entries[n],r.type===_N.MARKER_ENTRY||this.openElements.contains(r.element)){n++;break}while(n>0);for(let o=n;o<t;o++)r=this.activeFormattingElements.entries[o],this._insertElement(r.token,this.treeAdapter.getNamespaceURI(r.element)),r.element=this.openElements.current}}_closeTableCell(){this.openElements.generateImpliedEndTags(),this.openElements.popUntilTableCellPopped(),this.activeFormattingElements.clearToLastMarker(),this.insertionMode=dl}_closePElement(){this.openElements.generateImpliedEndTagsWithExclusion(x.P),this.openElements.popUntilTagNamePopped(x.P)}_resetInsertionMode(){for(let t=this.openElements.stackTop,n=!1;t>=0;t--){let r=this.openElements.items[t];t===0&&(n=!0,this.fragmentContext&&(r=this.fragmentContext));const o=this.treeAdapter.getTagName(r),i=m4e[o];if(i){this.insertionMode=i;break}else if(!n&&(o===x.TD||o===x.TH)){this.insertionMode=jy;break}else if(!n&&o===x.HEAD){this.insertionMode=b1;break}else if(o===x.SELECT){this._resetInsertionModeForSelect(t);break}else if(o===x.TEMPLATE){this.insertionMode=this.currentTmplInsertionMode;break}else if(o===x.HTML){this.insertionMode=this.headElement?My:Py;break}else if(n){this.insertionMode=as;break}}}_resetInsertionModeForSelect(t){if(t>0)for(let n=t-1;n>0;n--){const r=this.openElements.items[n],o=this.treeAdapter.getTagName(r);if(o===x.TEMPLATE)break;if(o===x.TABLE){this.insertionMode=gk;return}}this.insertionMode=mk}_pushTmplInsertionMode(t){this.tmplInsertionModeStack.push(t),this.tmplInsertionModeStackTop++,this.currentTmplInsertionMode=t}_popTmplInsertionMode(){this.tmplInsertionModeStack.pop(),this.tmplInsertionModeStackTop--,this.currentTmplInsertionMode=this.tmplInsertionModeStack[this.tmplInsertionModeStackTop]}_isElementCausesFosterParenting(t){const n=this.treeAdapter.getTagName(t);return n===x.TABLE||n===x.TBODY||n===x.TFOOT||n===x.THEAD||n===x.TR}_shouldFosterParentOnInsertion(){return this.fosterParentingEnabled&&this._isElementCausesFosterParenting(this.openElements.current)}_findFosterParentingLocation(){const t={parent:null,beforeElement:null};for(let n=this.openElements.stackTop;n>=0;n--){const r=this.openElements.items[n],o=this.treeAdapter.getTagName(r),i=this.treeAdapter.getNamespaceURI(r);if(o===x.TEMPLATE&&i===ze.HTML){t.parent=this.treeAdapter.getTemplateContent(r);break}else if(o===x.TABLE){t.parent=this.treeAdapter.getParentNode(r),t.parent?t.beforeElement=r:t.parent=this.openElements.items[n-1];break}}return t.parent||(t.parent=this.openElements.items[0]),t}_fosterParentElement(t){const n=this._findFosterParentingLocation();n.beforeElement?this.treeAdapter.insertBefore(n.parent,t,n.beforeElement):this.treeAdapter.appendChild(n.parent,t)}_fosterParentText(t){const n=this._findFosterParentingLocation();n.beforeElement?this.treeAdapter.insertTextBefore(n.parent,t,n.beforeElement):this.treeAdapter.insertText(n.parent,t)}_isSpecialElement(t){const n=this.treeAdapter.getTagName(t),r=this.treeAdapter.getNamespaceURI(t);return Yc.SPECIAL_ELEMENTS[r][n]}}var b4e=v4e;function y4e(e,t){let n=e.activeFormattingElements.getElementEntryInScopeWithTagName(t.tagName);return n?e.openElements.contains(n.element)?e.openElements.hasInScope(t.tagName)||(n=null):(e.activeFormattingElements.removeEntry(n),n=null):ma(e,t),n}function E4e(e,t){let n=null;for(let r=e.openElements.stackTop;r>=0;r--){const o=e.openElements.items[r];if(o===t.element)break;e._isSpecialElement(o)&&(n=o)}return n||(e.openElements.popUntilElementPopped(t.element),e.activeFormattingElements.removeEntry(t)),n}function _4e(e,t,n){let r=t,o=e.openElements.getCommonAncestor(t);for(let i=0,a=o;a!==n;i++,a=o){o=e.openElements.getCommonAncestor(a);const s=e.activeFormattingElements.getElementEntry(a),l=s&&i>=p4e;!s||l?(l&&e.activeFormattingElements.removeEntry(s),e.openElements.remove(a)):(a=T4e(e,s),r===t&&(e.activeFormattingElements.bookmark=s),e.treeAdapter.detachNode(r),e.treeAdapter.appendChild(a,r),r=a)}return r}function T4e(e,t){const n=e.treeAdapter.getNamespaceURI(t.element),r=e.treeAdapter.createElement(t.token.tagName,n,t.token.attrs);return e.openElements.replace(t.element,r),t.element=r,r}function w4e(e,t,n){if(e._isElementCausesFosterParenting(t))e._fosterParentElement(n);else{const r=e.treeAdapter.getTagName(t),o=e.treeAdapter.getNamespaceURI(t);r===x.TEMPLATE&&o===ze.HTML&&(t=e.treeAdapter.getTemplateContent(t)),e.treeAdapter.appendChild(t,n)}}function k4e(e,t,n){const r=e.treeAdapter.getNamespaceURI(n.element),o=n.token,i=e.treeAdapter.createElement(o.tagName,r,o.attrs);e._adoptNodes(t,i),e.treeAdapter.appendChild(t,i),e.activeFormattingElements.insertElementAfterBookmark(i,n.token),e.activeFormattingElements.removeEntry(n),e.openElements.remove(n.element),e.openElements.insertAfter(t,i)}function eu(e,t){let n;for(let r=0;r<h4e&&(n=y4e(e,t),!!n);r++){const o=E4e(e,n);if(!o)break;e.activeFormattingElements.bookmark=n;const i=_4e(e,o,n.element),a=e.openElements.getCommonAncestor(n.element);e.treeAdapter.detachNode(i),w4e(e,a,i),k4e(e,o,n)}}function Dt(){}function dg(e){e._err(Xr.misplacedDoctype)}function 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| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/extension/azureml_extension.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import re
from promptflow._sdk._serving._errors import InvalidConnectionData, MissingConnectionProvider
from promptflow._sdk._serving.extension.default_extension import AppExtension
from promptflow._sdk._serving.monitor.data_collector import FlowDataCollector
from promptflow._sdk._serving.monitor.flow_monitor import FlowMonitor
from promptflow._sdk._serving.monitor.metrics import MetricsRecorder
from promptflow._sdk._serving.utils import decode_dict, get_pf_serving_env, normalize_connection_name
from promptflow._utils.retry_utils import retry
from promptflow._version import VERSION
from promptflow.contracts.flow import Flow
USER_AGENT = f"promptflow-cloud-serving/{VERSION}"
AML_DEPLOYMENT_RESOURCE_ID_REGEX = "/subscriptions/(.*)/resourceGroups/(.*)/providers/Microsoft.MachineLearningServices/workspaces/(.*)/onlineEndpoints/(.*)/deployments/(.*)" # noqa: E501
AML_CONNECTION_PROVIDER_TEMPLATE = "azureml:/subscriptions/{}/resourceGroups/{}/providers/Microsoft.MachineLearningServices/workspaces/{}" # noqa: E501
class AzureMLExtension(AppExtension):
"""AzureMLExtension is used to create extension for azureml serving."""
def __init__(self, logger, **kwargs):
super().__init__(logger=logger, **kwargs)
self.logger = logger
# parse promptflow project path
project_path: str = get_pf_serving_env("PROMPTFLOW_PROJECT_PATH")
if not project_path:
model_dir = os.getenv("AZUREML_MODEL_DIR", ".")
model_rootdir = os.listdir(model_dir)[0]
self.model_name = model_rootdir
project_path = os.path.join(model_dir, model_rootdir)
self.model_root_path = project_path
# mlflow support in base extension
self.project_path = self._get_mlflow_project_path(project_path)
# initialize connections or connection provider
# TODO: to be deprecated, remove in next major version
self.connections = self._get_env_connections_if_exist()
self.endpoint_name: str = None
self.deployment_name: str = None
self.connection_provider = None
self.credential = _get_managed_identity_credential_with_retry()
if len(self.connections) == 0:
self._initialize_connection_provider()
# initialize metrics common dimensions if exist
self.common_dimensions = {}
if self.endpoint_name:
self.common_dimensions["endpoint"] = self.endpoint_name
if self.deployment_name:
self.common_dimensions["deployment"] = self.deployment_name
env_dimensions = self._get_common_dimensions_from_env()
self.common_dimensions.update(env_dimensions)
# initialize flow monitor
data_collector = FlowDataCollector(self.logger)
metrics_recorder = self._get_metrics_recorder()
self.flow_monitor = FlowMonitor(
self.logger, self.get_flow_name(), data_collector, metrics_recorder=metrics_recorder
)
def get_flow_project_path(self) -> str:
return self.project_path
def get_flow_name(self) -> str:
return os.path.basename(self.model_root_path)
def get_connection_provider(self) -> str:
return self.connection_provider
def get_blueprints(self):
return self._get_default_blueprints()
def get_flow_monitor(self) -> FlowMonitor:
return self.flow_monitor
def get_override_connections(self, flow: Flow) -> (dict, dict):
connection_names = flow.get_connection_names()
connections = {}
connections_name_overrides = {}
for connection_name in connection_names:
# replace " " with "_" in connection name
normalized_name = normalize_connection_name(connection_name)
if normalized_name in os.environ:
override_conn = os.environ[normalized_name]
data_override = False
# try load connection as a json
try:
# data override
conn_data = json.loads(override_conn)
data_override = True
except ValueError:
# name override
self.logger.debug(f"Connection value is not json, enable name override for {connection_name}.")
connections_name_overrides[connection_name] = override_conn
if data_override:
try:
# try best to convert to connection, this is only for azureml deployment.
from promptflow.azure.operations._arm_connection_operations import ArmConnectionOperations
conn = ArmConnectionOperations._convert_to_connection_dict(connection_name, conn_data)
connections[connection_name] = conn
except Exception as e:
self.logger.warn(f"Failed to convert connection data to connection: {e}")
raise InvalidConnectionData(connection_name)
if len(connections_name_overrides) > 0:
self.logger.info(f"Connection name overrides: {connections_name_overrides}")
if len(connections) > 0:
self.logger.info(f"Connections data overrides: {connections.keys()}")
self.connections.update(connections)
return self.connections, connections_name_overrides
def raise_ex_on_invoker_initialization_failure(self, ex: Exception):
from promptflow.azure.operations._arm_connection_operations import UserAuthenticationError
# allow lazy authentication for UserAuthenticationError
return not isinstance(ex, UserAuthenticationError)
def get_user_agent(self) -> str:
return USER_AGENT
def get_metrics_common_dimensions(self):
return self.common_dimensions
def get_credential(self):
return self.credential
def _get_env_connections_if_exist(self):
# For local test app connections will be set.
connections = {}
env_connections = get_pf_serving_env("PROMPTFLOW_ENCODED_CONNECTIONS")
if env_connections:
connections = decode_dict(env_connections)
return connections
def _get_metrics_recorder(self):
# currently only support exporting it to azure monitor(application insights)
# TODO: add support for dynamic loading thus user can customize their own exporter.
custom_dimensions = self.get_metrics_common_dimensions()
try:
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
# check whether azure monitor instrumentation key is set
instrumentation_key = os.getenv("AML_APP_INSIGHTS_KEY") or os.getenv("APPINSIGHTS_INSTRUMENTATIONKEY")
if instrumentation_key:
self.logger.info("Initialize metrics recorder with azure monitor metrics exporter...")
exporter = AzureMonitorMetricExporter(connection_string=f"InstrumentationKey={instrumentation_key}")
reader = PeriodicExportingMetricReader(exporter=exporter, export_interval_millis=60000)
return MetricsRecorder(self.logger, reader=reader, common_dimensions=custom_dimensions)
else:
self.logger.info("Azure monitor metrics exporter is not enabled, metrics will not be collected.")
except ImportError:
self.logger.warning("No metrics exporter module found, metrics will not be collected.")
return None
def _initialize_connection_provider(self):
# parse connection provider
self.connection_provider = get_pf_serving_env("PROMPTFLOW_CONNECTION_PROVIDER")
if not self.connection_provider:
pf_override = os.getenv("PRT_CONFIG_OVERRIDE", None)
if pf_override:
env_conf = pf_override.split(",")
env_conf_list = [setting.split("=") for setting in env_conf]
settings = {setting[0]: setting[1] for setting in env_conf_list}
self.subscription_id = settings.get("deployment.subscription_id", None)
self.resource_group = settings.get("deployment.resource_group", None)
self.workspace_name = settings.get("deployment.workspace_name", None)
self.endpoint_name = settings.get("deployment.endpoint_name", None)
self.deployment_name = settings.get("deployment.deployment_name", None)
else:
deploy_resource_id = os.getenv("AML_DEPLOYMENT_RESOURCE_ID", None)
if deploy_resource_id:
match_result = re.match(AML_DEPLOYMENT_RESOURCE_ID_REGEX, deploy_resource_id)
if len(match_result.groups()) == 5:
self.subscription_id = match_result.group(1)
self.resource_group = match_result.group(2)
self.workspace_name = match_result.group(3)
self.endpoint_name = match_result.group(4)
self.deployment_name = match_result.group(5)
else:
# raise exception if not found any valid connection provider setting
raise MissingConnectionProvider(
message="Missing connection provider, please check whether 'PROMPTFLOW_CONNECTION_PROVIDER' "
"is in your environment variable list."
) # noqa: E501
self.connection_provider = AML_CONNECTION_PROVIDER_TEMPLATE.format(
self.subscription_id, self.resource_group, self.workspace_name
) # noqa: E501
def _get_managed_identity_credential_with_retry(**kwargs):
from azure.identity import ManagedIdentityCredential
class ManagedIdentityCredentialWithRetry(ManagedIdentityCredential):
@retry(Exception)
def get_token(self, *scopes, **kwargs):
return super().get_token(*scopes, **kwargs)
return ManagedIdentityCredentialWithRetry(**kwargs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/extension/extension_factory.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from enum import Enum
from promptflow._sdk._serving.extension.default_extension import AppExtension
class ExtensionType(Enum):
"""Extension type used to identify which extension to load in serving app."""
Default = "local"
AzureML = "azureml"
class ExtensionFactory:
"""ExtensionFactory is used to create extension based on extension type."""
@staticmethod
def create_extension(logger, **kwargs) -> AppExtension:
"""Create extension based on extension type."""
extension_type_str = kwargs.get("extension_type", ExtensionType.Default.value)
if not extension_type_str:
extension_type_str = ExtensionType.Default.value
extension_type = ExtensionType(extension_type_str.lower())
if extension_type == ExtensionType.AzureML:
from promptflow._sdk._serving.extension.azureml_extension import AzureMLExtension
return AzureMLExtension(logger=logger, **kwargs)
else:
from promptflow._sdk._serving.extension.default_extension import DefaultAppExtension
return DefaultAppExtension(logger=logger, **kwargs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/extension/default_extension.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
from abc import ABC, abstractmethod
from pathlib import Path
from promptflow._constants import DEFAULT_ENCODING
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._serving.blueprint.monitor_blueprint import construct_monitor_blueprint
from promptflow._sdk._serving.blueprint.static_web_blueprint import construct_staticweb_blueprint
from promptflow._sdk._serving.monitor.flow_monitor import FlowMonitor
from promptflow._utils.yaml_utils import load_yaml
from promptflow._version import VERSION
from promptflow.contracts.flow import Flow
USER_AGENT = f"promptflow-local-serving/{VERSION}"
DEFAULT_STATIC_PATH = Path(__file__).parent.parent / "static"
class AppExtension(ABC):
def __init__(self, logger, **kwargs):
self.logger = logger
@abstractmethod
def get_flow_project_path(self) -> str:
"""Get flow project path."""
pass
@abstractmethod
def get_flow_name(self) -> str:
"""Get flow name."""
pass
@abstractmethod
def get_connection_provider(self) -> str:
"""Get connection provider."""
pass
@abstractmethod
def get_blueprints(self):
"""Get blueprints for current extension."""
pass
def get_override_connections(self, flow: Flow) -> (dict, dict):
"""
Get override connections for current extension.
:param flow: The flow to execute.
:type flow: ~promptflow._sdk.entities._flow.Flow
:return: The override connections, first dict is for connection data override, second dict is for connection name override. # noqa: E501
:rtype: (dict, dict)
"""
return {}, {}
def raise_ex_on_invoker_initialization_failure(self, ex: Exception):
"""
whether to raise exception when initializing flow invoker failed.
:param ex: The exception when initializing flow invoker.
:type ex: Exception
:return: Whether to raise exception when initializing flow invoker failed.
"""
return True
def get_user_agent(self) -> str:
"""Get user agent used for current extension."""
return USER_AGENT
def get_credential(self):
"""Get credential for current extension."""
return None
def get_metrics_common_dimensions(self):
"""Get common dimensions for metrics if exist."""
return self._get_common_dimensions_from_env()
def get_flow_monitor(self) -> FlowMonitor:
"""Get flow monitor for current extension."""
# default no data collector, no app insights metric exporter
return FlowMonitor(self.logger, self.get_flow_name(), None, metrics_recorder=None)
def _get_mlflow_project_path(self, project_path: str):
# check whether it's mlflow model
mlflow_metadata_file = os.path.join(project_path, "MLmodel")
if os.path.exists(mlflow_metadata_file):
with open(mlflow_metadata_file, "r", encoding=DEFAULT_ENCODING) as fin:
mlflow_metadata = load_yaml(fin)
flow_entry = mlflow_metadata.get("flavors", {}).get("promptflow", {}).get("entry")
if mlflow_metadata:
dag_path = os.path.join(project_path, flow_entry)
return str(Path(dag_path).parent.absolute())
return project_path
def _get_common_dimensions_from_env(self):
common_dimensions_str = os.getenv("PF_SERVING_METRICS_COMMON_DIMENSIONS", None)
if common_dimensions_str:
try:
common_dimensions = json.loads(common_dimensions_str)
return common_dimensions
except Exception as ex:
self.logger.warn(f"Failed to parse common dimensions with value={common_dimensions_str}: {ex}")
return {}
def _get_default_blueprints(self, static_folder=None):
static_web_blueprint = construct_staticweb_blueprint(static_folder)
monitor_print = construct_monitor_blueprint(self.get_flow_monitor())
return [static_web_blueprint, monitor_print]
class DefaultAppExtension(AppExtension):
"""default app extension for local serve."""
def __init__(self, logger, **kwargs):
self.logger = logger
static_folder = kwargs.get("static_folder", None)
self.static_folder = static_folder if static_folder else DEFAULT_STATIC_PATH
logger.info(f"Static_folder: {self.static_folder}")
app_config = kwargs.get("config", None) or {}
pf_config = Configuration(overrides=app_config)
logger.info(f"Promptflow config: {pf_config}")
self.connection_provider = pf_config.get_connection_provider()
def get_flow_project_path(self) -> str:
return os.getenv("PROMPTFLOW_PROJECT_PATH", ".")
def get_flow_name(self) -> str:
project_path = self.get_flow_project_path()
return Path(project_path).stem
def get_connection_provider(self) -> str:
return self.connection_provider
def get_blueprints(self):
return self._get_default_blueprints(self.static_folder)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/extension/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/flow_monitor.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import time
from promptflow._sdk._serving.monitor.data_collector import FlowDataCollector
from promptflow._sdk._serving.monitor.streaming_monitor import StreamingMonitor
from promptflow._sdk._serving.monitor.metrics import MetricsRecorder, ResponseType
from promptflow._sdk._serving.utils import streaming_response_required, get_cost_up_to_now
from promptflow._sdk._serving.flow_result import FlowResult
from promptflow._utils.exception_utils import ErrorResponse
from flask import request, g
class FlowMonitor:
"""FlowMonitor is used to collect metrics & data for promptflow serving."""
def __init__(self,
logger,
default_flow_name,
data_collector: FlowDataCollector,
metrics_recorder: MetricsRecorder):
self.data_collector = data_collector
self.metrics_recorder = metrics_recorder
self.logger = logger
self.flow_name = default_flow_name
def setup_streaming_monitor_if_needed(self, response_creator, data, output):
g.streaming = response_creator.has_stream_field and response_creator.text_stream_specified_explicitly
# set streaming callback functions if the response is streaming
if g.streaming:
streaming_monitor = StreamingMonitor(
self.logger,
flow_id=g.get("flow_id", self.flow_name),
start_time=g.start_time,
inputs=data,
outputs=output,
req_id=g.get("req_id", None),
streaming_field_name=response_creator.stream_field_name,
metric_recorder=self.metrics_recorder,
data_collector=self.data_collector,
)
response_creator._on_stream_start = streaming_monitor.on_stream_start
response_creator._on_stream_end = streaming_monitor.on_stream_end
response_creator._on_stream_event = streaming_monitor.on_stream_event
self.logger.info(f"Finish stream callback setup for flow with streaming={g.streaming}.")
else:
self.logger.info("Flow does not enable streaming response.")
def handle_error(self, ex: Exception, resp_code: int):
if self.metrics_recorder:
flow_id = g.get("flow_id", self.flow_name)
err_code = ErrorResponse.from_exception(ex).innermost_error_code
streaming = g.get("streaming", False)
self.metrics_recorder.record_flow_request(flow_id, resp_code, err_code, streaming)
def start_monitoring(self):
g.start_time = time.time()
g.streaming = streaming_response_required()
g.req_id = request.headers.get("x-request-id", None)
self.logger.info(f"Start monitoring new request, request_id: {g.req_id}")
def finish_monitoring(self, resp_status_code):
data = g.get("data", None)
flow_result: FlowResult = g.get("flow_result", None)
req_id = g.get("req_id", None)
flow_id = g.get("flow_id", self.flow_name)
# collect non-streaming flow request/response data
if self.data_collector and data and flow_result and flow_result.output and not g.streaming:
self.data_collector.collect_flow_data(data, flow_result.output, req_id)
if self.metrics_recorder:
if flow_result:
self.metrics_recorder.record_tracing_metrics(flow_result.run_info, flow_result.node_run_infos)
err_code = g.get("err_code", "None")
self.metrics_recorder.record_flow_request(flow_id, resp_status_code, err_code, g.streaming)
# streaming metrics will be recorded in the streaming callback func
if not g.streaming:
latency = get_cost_up_to_now(g.start_time)
self.metrics_recorder.record_flow_latency(
flow_id, resp_status_code, g.streaming, ResponseType.Default.value, latency
)
self.logger.info(f"Finish monitoring request, request_id: {req_id}.")
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/streaming_monitor.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._sdk._serving.utils import get_cost_up_to_now
from promptflow._sdk._serving.monitor.metrics import ResponseType
class StreamingMonitor:
"""StreamingMonitor is used to collect metrics & data for streaming response."""
def __init__(self,
logger,
flow_id: str,
start_time: float,
inputs: dict,
outputs: dict,
req_id: str,
streaming_field_name: str,
metric_recorder,
data_collector,
) -> None:
self.logger = logger
self.flow_id = flow_id
self.start_time = start_time
self.inputs = inputs
self.outputs = outputs
self.streaming_field_name = streaming_field_name
self.req_id = req_id
self.metric_recorder = metric_recorder
self.data_collector = data_collector
self.response_message = []
def on_stream_start(self):
"""stream start call back function, record flow latency when first byte received."""
self.logger.info("start streaming response...")
if self.metric_recorder:
duration = get_cost_up_to_now(self.start_time)
self.metric_recorder.record_flow_latency(self.flow_id, 200, True, ResponseType.FirstByte.value, duration)
def on_stream_end(self, streaming_resp_duration: float):
"""stream end call back function, record flow latency and streaming response data when last byte received."""
if self.metric_recorder:
duration = get_cost_up_to_now(self.start_time)
self.metric_recorder.record_flow_latency(self.flow_id, 200, True, ResponseType.LastByte.value, duration)
self.metric_recorder.record_flow_streaming_response_duration(self.flow_id, streaming_resp_duration)
if self.data_collector:
response_content = "".join(self.response_message)
if self.streaming_field_name in self.outputs:
self.outputs[self.streaming_field_name] = response_content
self.data_collector.collect_flow_data(self.inputs, self.outputs, self.req_id)
self.logger.info("finish streaming response.")
def on_stream_event(self, message: str):
"""stream event call back function, record streaming response data chunk."""
self.response_message.append(message)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/metrics.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from enum import Enum
from typing import Dict, Sequence, Set, List, Any
from promptflow._utils.exception_utils import ErrorResponse
from promptflow.contracts.run_info import FlowRunInfo, RunInfo, Status
# define metrics dimension keys
FLOW_KEY = "flow"
RUN_STATUS_KEY = "run_status"
NODE_KEY = "node"
LLM_ENGINE_KEY = "llm_engine"
TOKEN_TYPE_KEY = "token_type"
RESPONSE_CODE_KEY = "response_code"
EXCEPTION_TYPE_KEY = "exception"
STREAMING_KEY = "streaming"
API_CALL_KEY = "api_call"
RESPONSE_TYPE_KEY = "response_type" # firstbyte, lastbyte, default
HISTOGRAM_BOUNDARIES: Sequence[float] = (
1.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0,
25000.0,
50000.0,
75000.0,
100000.0,
300000.0,
)
class ResponseType(Enum):
# latency from receiving the request to sending the first byte of response, only applicable to streaming flow
FirstByte = "firstbyte"
# latency from receiving the request to sending the last byte of response, only applicable to streaming flow
LastByte = "lastbyte"
# latency from receiving the request to sending the whole response, only applicable to non-streaming flow
Default = "default"
class LLMTokenType(Enum):
PromptTokens = "prompt_tokens"
CompletionTokens = "completion_tokens"
try:
from opentelemetry import metrics
from opentelemetry.metrics import set_meter_provider
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.view import ExplicitBucketHistogramAggregation, SumAggregation, View
# define meter
meter = metrics.get_meter_provider().get_meter("Promptflow Standard Metrics")
# define metrics
token_consumption = meter.create_counter("Token_Consumption")
flow_latency = meter.create_histogram("Flow_Latency")
node_latency = meter.create_histogram("Node_Latency")
flow_request = meter.create_counter("Flow_Request")
remote_api_call_latency = meter.create_histogram("RPC_Latency")
remote_api_call_request = meter.create_counter("RPC_Request")
node_request = meter.create_counter("Node_Request")
# metrics for streaming
streaming_response_duration = meter.create_histogram("Flow_Streaming_Response_Duration")
# define metrics views
# token view
token_view = View(
instrument_name="Token_Consumption",
description="",
attribute_keys={FLOW_KEY, NODE_KEY, LLM_ENGINE_KEY, TOKEN_TYPE_KEY},
aggregation=SumAggregation(),
)
# latency view
flow_latency_view = View(
instrument_name="Flow_Latency",
description="",
attribute_keys={FLOW_KEY, RESPONSE_CODE_KEY, STREAMING_KEY, RESPONSE_TYPE_KEY},
aggregation=ExplicitBucketHistogramAggregation(boundaries=HISTOGRAM_BOUNDARIES),
)
node_latency_view = View(
instrument_name="Node_Latency",
description="",
attribute_keys={FLOW_KEY, NODE_KEY, RUN_STATUS_KEY},
aggregation=ExplicitBucketHistogramAggregation(boundaries=HISTOGRAM_BOUNDARIES),
)
flow_streaming_response_duration_view = View(
instrument_name="Flow_Streaming_Response_Duration",
description="during between sending the first byte and last byte of the response, only for streaming flow",
attribute_keys={FLOW_KEY},
aggregation=ExplicitBucketHistogramAggregation(boundaries=HISTOGRAM_BOUNDARIES),
)
# request view
request_view = View(
instrument_name="Flow_Request",
description="",
attribute_keys={FLOW_KEY, RESPONSE_CODE_KEY, STREAMING_KEY, EXCEPTION_TYPE_KEY},
aggregation=SumAggregation(),
)
node_request_view = View(
instrument_name="Node_Request",
description="",
attribute_keys={FLOW_KEY, NODE_KEY, RUN_STATUS_KEY, EXCEPTION_TYPE_KEY},
aggregation=SumAggregation(),
)
# Remote API call view
remote_api_call_latency_view = View(
instrument_name="RPC_Latency",
description="",
attribute_keys={FLOW_KEY, NODE_KEY, API_CALL_KEY},
aggregation=ExplicitBucketHistogramAggregation(boundaries=HISTOGRAM_BOUNDARIES),
)
remote_api_call_request_view = View(
instrument_name="RPC_Request",
description="",
attribute_keys={FLOW_KEY, NODE_KEY, API_CALL_KEY, EXCEPTION_TYPE_KEY},
aggregation=SumAggregation(),
)
metrics_enabled = True
except ImportError:
metrics_enabled = False
class MetricsRecorder(object):
"""OpenTelemetry Metrics Recorder"""
def __init__(self, logger, reader=None, common_dimensions: Dict[str, str] = None) -> None:
"""initialize metrics recorder
:param logger: logger
:type logger: Logger
:param reader: metric reader
:type reader: opentelemetry.sdk.metrics.export.MetricReader
:param common_dimensions: common dimensions for all metrics
:type common_dimensions: Dict[str, str]
"""
self.logger = logger
if not metrics_enabled:
logger.warning("OpenTelemetry metric is not enabled, metrics will not be recorded." +
"If you want to collect metrics, please enable 'azureml-serving' extra requirement " +
"for promptflow: 'pip install promptflow[azureml-serving]'")
return
self.common_dimensions = common_dimensions or {}
self.reader = reader
dimension_keys = {key for key in common_dimensions}
self._config_common_monitor(dimension_keys, reader)
logger.info("OpenTelemetry metric is enabled, metrics will be recorded.")
def record_flow_request(self, flow_id: str, response_code: int, exception: str, streaming: bool):
if not metrics_enabled:
return
try:
flow_request.add(
1,
{
FLOW_KEY: flow_id,
RESPONSE_CODE_KEY: str(response_code),
EXCEPTION_TYPE_KEY: exception,
STREAMING_KEY: str(streaming),
**self.common_dimensions,
},
)
except Exception as e:
self.logger.warning("failed to record flow request metrics: %s", e)
def record_flow_latency(
self, flow_id: str, response_code: int, streaming: bool, response_type: str, duration: float
):
if not metrics_enabled:
return
try:
flow_latency.record(
duration,
{
FLOW_KEY: flow_id,
RESPONSE_CODE_KEY: str(response_code),
STREAMING_KEY: str(streaming),
RESPONSE_TYPE_KEY: response_type,
**self.common_dimensions,
},
)
except Exception as e:
self.logger.warning("failed to record flow latency metrics: %s", e)
def record_flow_streaming_response_duration(self, flow_id: str, duration: float):
if not metrics_enabled:
return
try:
streaming_response_duration.record(duration, {FLOW_KEY: flow_id, **self.common_dimensions})
except Exception as e:
self.logger.warning("failed to record streaming duration metrics: %s", e)
def record_tracing_metrics(self, flow_run: FlowRunInfo, node_runs: Dict[str, RunInfo]):
if not metrics_enabled:
return
try:
for _, run in node_runs.items():
flow_id = flow_run.flow_id if flow_run is not None else "default"
if len(run.system_metrics) > 0:
duration = run.system_metrics.get("duration", None)
if duration is not None:
duration = duration * 1000
node_latency.record(
duration,
{
FLOW_KEY: flow_id,
NODE_KEY: run.node,
RUN_STATUS_KEY: run.status.value,
**self.common_dimensions,
},
)
# openai token metrics
inputs = run.inputs or {}
engine = inputs.get("deployment_name") or ""
for token_type in [LLMTokenType.PromptTokens.value, LLMTokenType.CompletionTokens.value]:
count = run.system_metrics.get(token_type, None)
if count:
token_consumption.add(
count,
{
FLOW_KEY: flow_id,
NODE_KEY: run.node,
LLM_ENGINE_KEY: engine,
TOKEN_TYPE_KEY: token_type,
**self.common_dimensions,
},
)
# record node request metric
err = None
if run.status != Status.Completed:
err = "unknown"
if isinstance(run.error, dict):
err = self._get_exact_error(run.error)
elif isinstance(run.error, str):
err = run.error
node_request.add(
1,
{
FLOW_KEY: flow_id,
NODE_KEY: run.node,
RUN_STATUS_KEY: run.status.value,
EXCEPTION_TYPE_KEY: err,
**self.common_dimensions,
},
)
if run.api_calls and len(run.api_calls) > 0:
for api_call in run.api_calls:
# since first layer api_call is the node call itself, we ignore them here
api_calls: List[Dict[str, Any]] = api_call.get("children", None)
if api_calls is None:
continue
self._record_api_call_metrics(flow_id, run.node, api_calls)
except Exception as e:
self.logger.warning(f"failed to record metrics: {e}, flow_run: {flow_run}, node_runs: {node_runs}")
def _record_api_call_metrics(self, flow_id, node, api_calls: List[Dict[str, Any]], prefix: str = None):
if api_calls and len(api_calls) > 0:
for api_call in api_calls:
cur_name = api_call.get("name")
api_name = f"{prefix}_{cur_name}" if prefix else cur_name
# api-call latency metrics
# sample data: {"start_time":1688462182.744916, "end_time":1688462184.280989}
start_time = api_call.get("start_time", None)
end_time = api_call.get("end_time", None)
if start_time and end_time:
api_call_latency_ms = (end_time - start_time) * 1000
remote_api_call_latency.record(
api_call_latency_ms,
{
FLOW_KEY: flow_id,
NODE_KEY: node,
API_CALL_KEY: api_name,
**self.common_dimensions,
},
)
# remote api call request metrics
err = api_call.get("error") or {}
if isinstance(err, dict):
exception_type = self._get_exact_error(err)
else:
exception_type = err
remote_api_call_request.add(
1,
{
FLOW_KEY: flow_id,
NODE_KEY: node,
API_CALL_KEY: api_name,
EXCEPTION_TYPE_KEY: exception_type,
**self.common_dimensions,
},
)
child_api_calls = api_call.get("children", None)
if child_api_calls:
self._record_api_call_metrics(flow_id, node, child_api_calls, api_name)
def _get_exact_error(self, err: Dict):
error_response = ErrorResponse.from_error_dict(err)
return error_response.innermost_error_code
# configure monitor, by default only expose prometheus metrics
def _config_common_monitor(self, common_keys: Set[str] = {}, reader=None):
metrics_views = [
token_view,
flow_latency_view,
node_latency_view,
request_view,
remote_api_call_latency_view,
remote_api_call_request_view,
]
for view in metrics_views:
view._attribute_keys.update(common_keys)
readers = []
if reader:
readers.append(reader)
meter_provider = MeterProvider(
metric_readers=readers,
views=metrics_views,
)
set_meter_provider(meter_provider)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/data_collector.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class FlowDataCollector:
"""FlowDataCollector is used to collect flow data via MDC for monitoring."""
def __init__(self, logger):
self.logger = logger
self._init_success = self._init_data_collector()
logger.info(f"Mdc init status: {self._init_success}")
def _init_data_collector(self) -> bool:
"""init data collector."""
self.logger.info("Init mdc...")
try:
from azureml.ai.monitoring import Collector
self.inputs_collector = Collector(name="model_inputs")
self.outputs_collector = Collector(name="model_outputs")
return True
except ImportError as e:
self.logger.warn(f"Load mdc related module failed: {e}")
return False
except Exception as e:
self.logger.warn(f"Init mdc failed: {e}")
return False
def collect_flow_data(self, input: dict, output: dict, req_id: str = None, client_req_id: str = None):
"""collect flow data via MDC for monitoring."""
if not self._init_success:
return
try:
import pandas as pd
from azureml.ai.monitoring.context import BasicCorrelationContext
# build context
ctx = BasicCorrelationContext(id=req_id)
# collect inputs
coll_input = {k: [v] for k, v in input.items()}
input_df = pd.DataFrame(coll_input)
self.inputs_collector.collect(input_df, ctx)
# collect outputs
coll_output = {k: [v] for k, v in output.items()}
output_df = pd.DataFrame(coll_output)
# collect outputs data, pass in correlation_context so inputs and outputs data can be correlated later
self.outputs_collector.collect(output_df, ctx)
except ImportError as e:
self.logger.warn(f"Load mdc related module failed: {e}")
except Exception as e:
self.logger.warn(f"Collect flow data failed: {e}")
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_flow_context_resolver.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from functools import lru_cache
from os import PathLike
from pathlib import Path
from typing import Dict
from promptflow._sdk._constants import NODES
from promptflow._sdk._utils import parse_variant
from promptflow._sdk.entities import FlowContext
from promptflow._sdk.entities._flow import Flow
from promptflow._utils.flow_utils import load_flow_dag
from promptflow.contracts.flow import Node
from promptflow.exceptions import UserErrorException
# Resolve flow context to invoker
# Resolve flow according to flow context
# Resolve connection, variant, overwrite, store in-memory
# create invoker based on resolved flow
# cache invoker if flow context not changed (define hash function for flow context).
class FlowContextResolver:
"""Flow context resolver."""
def __init__(self, flow_path: PathLike):
from promptflow import PFClient
self.flow_path, self.flow_dag = load_flow_dag(flow_path=Path(flow_path))
self.working_dir = Path(self.flow_path).parent.resolve()
self.node_name_2_node: Dict[str, Node] = {node["name"]: node for node in self.flow_dag[NODES]}
self.client = PFClient()
@classmethod
@lru_cache
def resolve(cls, flow: Flow) -> "FlowInvoker":
"""Resolve flow to flow invoker."""
resolver = cls(flow_path=flow.path)
resolver._resolve(flow_context=flow.context)
return resolver._create_invoker(flow=flow, flow_context=flow.context)
def _resolve(self, flow_context: FlowContext):
"""Resolve flow context."""
# TODO(2813319): support node overrides
# TODO: define priority of the contexts
flow_context._resolve_connections()
self._resolve_variant(flow_context=flow_context)._resolve_connections(
flow_context=flow_context,
)._resolve_overrides(flow_context=flow_context)
def _resolve_variant(self, flow_context: FlowContext) -> "FlowContextResolver":
"""Resolve variant of the flow and store in-memory."""
# TODO: put all varint string parser here
if not flow_context.variant:
return self
else:
tuning_node, variant = parse_variant(flow_context.variant)
from promptflow._sdk._submitter import overwrite_variant
overwrite_variant(
flow_dag=self.flow_dag,
tuning_node=tuning_node,
variant=variant,
)
return self
def _resolve_connections(self, flow_context: FlowContext) -> "FlowContextResolver":
"""Resolve connections of the flow and store in-memory."""
from promptflow._sdk._submitter import overwrite_connections
overwrite_connections(
flow_dag=self.flow_dag,
connections=flow_context.connections,
working_dir=self.working_dir,
)
return self
def _resolve_overrides(self, flow_context: FlowContext) -> "FlowContextResolver":
"""Resolve overrides of the flow and store in-memory."""
from promptflow._sdk._submitter import overwrite_flow
overwrite_flow(
flow_dag=self.flow_dag,
params_overrides=flow_context.overrides,
)
return self
def _resolve_connection_objs(self, flow_context: FlowContext):
# validate connection objs
connections = {}
for key, connection_obj in flow_context._connection_objs.items():
scrubbed_secrets = connection_obj._get_scrubbed_secrets()
if scrubbed_secrets:
raise UserErrorException(
f"Connection {connection_obj} contains scrubbed secrets with key {scrubbed_secrets.keys()}, "
"please make sure connection has decrypted secrets to use in flow execution. "
)
connections[key] = connection_obj._to_execution_connection_dict()
return connections
def _create_invoker(self, flow: Flow, flow_context: FlowContext) -> "FlowInvoker":
from promptflow._sdk._serving.flow_invoker import FlowInvoker
connections = self._resolve_connection_objs(flow_context=flow_context)
# use updated flow dag to create new flow object for invoker
resolved_flow = Flow(code=self.working_dir, dag=self.flow_dag)
invoker = FlowInvoker(
flow=resolved_flow,
connections=connections,
streaming=flow_context.streaming,
)
return invoker
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_flow_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import glob
import json
import os
import shutil
import subprocess
import sys
from importlib.metadata import version
from os import PathLike
from pathlib import Path
from typing import Dict, Iterable, List, Tuple, Union
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import (
CHAT_HISTORY,
DEFAULT_ENCODING,
FLOW_TOOLS_JSON_GEN_TIMEOUT,
LOCAL_MGMT_DB_PATH,
PROMPT_FLOW_DIR_NAME,
)
from promptflow._sdk._load_functions import load_flow
from promptflow._sdk._submitter import TestSubmitter
from promptflow._sdk._submitter.utils import SubmitterHelper
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import (
_get_additional_includes,
_merge_local_code_and_additional_includes,
copy_tree_respect_template_and_ignore_file,
dump_flow_result,
generate_flow_tools_json,
generate_random_string,
logger,
parse_variant,
)
from promptflow._sdk.entities._eager_flow import EagerFlow
from promptflow._sdk.entities._flow import ProtectedFlow
from promptflow._sdk.entities._validation import ValidationResult
from promptflow._utils.context_utils import _change_working_dir
from promptflow._utils.yaml_utils import dump_yaml, load_yaml
from promptflow.exceptions import UserErrorException
class FlowOperations(TelemetryMixin):
"""FlowOperations."""
def __init__(self, client):
self._client = client
super().__init__()
@monitor_operation(activity_name="pf.flows.test", activity_type=ActivityType.PUBLICAPI)
def test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
entry: str = None,
**kwargs,
) -> dict:
"""Test flow or node.
:param flow: path to flow directory to test
:type flow: Union[str, PathLike]
:param inputs: Input data for the flow test
:type inputs: dict
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:type variant: str
:param node: If specified it will only test this node, else it will test the flow.
:type node: str
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: dict
:param entry: Entry function. Required when flow is script.
:type entry: str
:return: The result of flow or node
:rtype: dict
"""
result = self._test(
flow=flow,
inputs=inputs,
variant=variant,
node=node,
environment_variables=environment_variables,
entry=entry,
**kwargs,
)
dump_test_result = kwargs.get("dump_test_result", False)
if dump_test_result:
# Dump flow/node test info
flow = load_flow(flow)
if node:
dump_flow_result(flow_folder=flow.code, node_result=result, prefix=f"flow-{node}.node")
else:
if variant:
tuning_node, node_variant = parse_variant(variant)
prefix = f"flow-{tuning_node}-{node_variant}"
else:
prefix = "flow"
dump_flow_result(flow_folder=flow.code, flow_result=result, prefix=prefix)
additional_output_path = kwargs.get("detail", None)
if additional_output_path:
if not dump_test_result:
flow = load_flow(flow)
if node:
# detail and output
dump_flow_result(
flow_folder=flow.code,
node_result=result,
prefix=f"flow-{node}.node",
custom_path=additional_output_path,
)
# log
log_src_path = Path(flow.code) / PROMPT_FLOW_DIR_NAME / f"{node}.node.log"
log_dst_path = Path(additional_output_path) / f"{node}.node.log"
shutil.copy(log_src_path, log_dst_path)
else:
if variant:
tuning_node, node_variant = parse_variant(variant)
prefix = f"flow-{tuning_node}-{node_variant}"
else:
prefix = "flow"
# detail and output
dump_flow_result(
flow_folder=flow.code,
flow_result=result,
prefix=prefix,
custom_path=additional_output_path,
)
# log
log_src_path = Path(flow.code) / PROMPT_FLOW_DIR_NAME / "flow.log"
log_dst_path = Path(additional_output_path) / "flow.log"
shutil.copy(log_src_path, log_dst_path)
TestSubmitter._raise_error_when_test_failed(result, show_trace=node is not None)
return result.output
def _test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
stream_log: bool = True,
stream_output: bool = True,
allow_generator_output: bool = True,
entry: str = None,
**kwargs,
):
"""Test flow or node.
:param flow: path to flow directory to test
:param inputs: Input data for the flow test
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:param node: If specified it will only test this node, else it will test the flow.
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:param stream_log: Whether streaming the log.
:param stream_output: Whether streaming the outputs.
:param allow_generator_output: Whether return streaming output when flow has streaming output.
:param entry: The entry function, only works when source is a code file.
:return: Executor result
"""
from promptflow._sdk._load_functions import load_flow
inputs = inputs or {}
flow = load_flow(flow, entry=entry)
if isinstance(flow, EagerFlow):
if variant or node:
logger.warning("variant and node are not supported for eager flow, will be ignored")
variant, node = None, None
else:
if entry:
logger.warning("entry is only supported for eager flow, will be ignored")
flow.context.variant = variant
from promptflow._constants import FlowLanguage
from promptflow._sdk._submitter.test_submitter import TestSubmitterViaProxy
if flow.language == FlowLanguage.CSharp:
with TestSubmitterViaProxy(flow=flow, flow_context=flow.context, client=self._client).init() as submitter:
is_chat_flow, chat_history_input_name, _ = self._is_chat_flow(submitter.dataplane_flow)
flow_inputs, dependency_nodes_outputs = submitter.resolve_data(
node_name=node, inputs=inputs, chat_history_name=chat_history_input_name
)
if node:
return submitter.node_test(
node_name=node,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
environment_variables=environment_variables,
stream=True,
)
else:
return submitter.flow_test(
inputs=flow_inputs,
environment_variables=environment_variables,
stream_log=stream_log,
stream_output=stream_output,
allow_generator_output=allow_generator_output and is_chat_flow,
)
with TestSubmitter(flow=flow, flow_context=flow.context, client=self._client).init() as submitter:
if isinstance(flow, EagerFlow):
# TODO(2897153): support chat eager flow
is_chat_flow, chat_history_input_name = False, None
flow_inputs, dependency_nodes_outputs = inputs, None
else:
is_chat_flow, chat_history_input_name, _ = self._is_chat_flow(submitter.dataplane_flow)
flow_inputs, dependency_nodes_outputs = submitter.resolve_data(
node_name=node, inputs=inputs, chat_history_name=chat_history_input_name
)
if node:
return submitter.node_test(
node_name=node,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
environment_variables=environment_variables,
stream=True,
)
else:
return submitter.flow_test(
inputs=flow_inputs,
environment_variables=environment_variables,
stream_log=stream_log,
stream_output=stream_output,
allow_generator_output=allow_generator_output and is_chat_flow,
)
@staticmethod
def _is_chat_flow(flow):
"""
Check if the flow is chat flow.
Check if chat_history in the flow input and only one chat input and
one chat output to determine if it is a chat flow.
"""
chat_inputs = [item for item in flow.inputs.values() if item.is_chat_input]
chat_outputs = [item for item in flow.outputs.values() if item.is_chat_output]
chat_history_input_name = next(
iter([input_name for input_name, value in flow.inputs.items() if value.is_chat_history]), None
)
if (
not chat_history_input_name
and CHAT_HISTORY in flow.inputs
and flow.inputs[CHAT_HISTORY].is_chat_history is not False
):
chat_history_input_name = CHAT_HISTORY
is_chat_flow, error_msg = True, ""
if len(chat_inputs) != 1:
is_chat_flow = False
error_msg = "chat flow does not support multiple chat inputs"
elif len(chat_outputs) != 1:
is_chat_flow = False
error_msg = "chat flow does not support multiple chat outputs"
elif not chat_history_input_name:
is_chat_flow = False
error_msg = "chat_history is required in the inputs of chat flow"
return is_chat_flow, chat_history_input_name, error_msg
@monitor_operation(activity_name="pf.flows._chat", activity_type=ActivityType.INTERNALCALL)
def _chat(
self,
flow,
*,
inputs: dict = None,
variant: str = None,
environment_variables: dict = None,
**kwargs,
) -> List:
"""Interact with Chat Flow. Only chat flow supported.
:param flow: path to flow directory to chat
:param inputs: Input data for the flow to chat
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
"""
from promptflow._sdk._load_functions import load_flow
flow = load_flow(flow)
flow.context.variant = variant
with TestSubmitter(flow=flow, flow_context=flow.context, client=self._client).init() as submitter:
is_chat_flow, chat_history_input_name, error_msg = self._is_chat_flow(submitter.dataplane_flow)
if not is_chat_flow:
raise UserErrorException(f"Only support chat flow in interactive mode, {error_msg}.")
info_msg = f"Welcome to chat flow, {submitter.dataplane_flow.name}."
print("=" * len(info_msg))
print(info_msg)
print("Press Enter to send your message.")
print("You can quit with ctrl+C.")
print("=" * len(info_msg))
submitter._chat_flow(
inputs=inputs,
chat_history_name=chat_history_input_name,
environment_variables=environment_variables,
show_step_output=kwargs.get("show_step_output", False),
)
@monitor_operation(activity_name="pf.flows._chat_with_ui", activity_type=ActivityType.INTERNALCALL)
def _chat_with_ui(self, script):
try:
import bs4 # noqa: F401
import streamlit_quill # noqa: F401
from streamlit.web import cli as st_cli
except ImportError as ex:
raise UserErrorException(
f"Please try 'pip install promptflow[executable]' to install dependency, {ex.msg}."
)
sys.argv = [
"streamlit",
"run",
script,
"--global.developmentMode=false",
"--client.toolbarMode=viewer",
"--browser.gatherUsageStats=false",
]
st_cli.main()
def _build_environment_config(self, flow_dag_path: Path):
flow_info = load_yaml(flow_dag_path)
# standard env object:
# environment:
# image: xxx
# conda_file: xxx
# python_requirements_txt: xxx
# setup_sh: xxx
# TODO: deserialize dag with structured class here to avoid using so many magic strings
env_obj = flow_info.get("environment", {})
env_obj["sdk_version"] = version("promptflow")
# version 0.0.1 is the dev version of promptflow
if env_obj["sdk_version"] == "0.0.1":
del env_obj["sdk_version"]
if not env_obj.get("python_requirements_txt", None) and (flow_dag_path.parent / "requirements.txt").is_file():
env_obj["python_requirements_txt"] = "requirements.txt"
env_obj["conda_env_name"] = "promptflow-serve"
if "conda_file" in env_obj:
conda_file = flow_dag_path.parent / env_obj["conda_file"]
if conda_file.is_file():
conda_obj = yaml.safe_load(conda_file.read_text())
if "name" in conda_obj:
env_obj["conda_env_name"] = conda_obj["name"]
return env_obj
@classmethod
def _refine_connection_name(cls, connection_name: str):
return connection_name.replace(" ", "_")
def _dump_connection(self, connection, output_path: Path):
# connection yaml should be a dict instead of ordered dict
connection_dict = connection._to_dict()
connection_yaml = {
"$schema": f"https://azuremlschemas.azureedge.net/promptflow/"
f"latest/{connection.__class__.__name__}.schema.json",
**connection_dict,
}
if connection.type == "Custom":
secret_dict = connection_yaml["secrets"]
else:
secret_dict = connection_yaml
connection_var_name = self._refine_connection_name(connection.name)
env_var_names = [f"{connection_var_name}_{secret_key}".upper() for secret_key in connection.secrets]
for secret_key, secret_env in zip(connection.secrets, env_var_names):
secret_dict[secret_key] = "${env:" + secret_env + "}"
for key in ["created_date", "last_modified_date"]:
if key in connection_yaml:
del connection_yaml[key]
key_order = ["$schema", "type", "name", "configs", "secrets", "module"]
sorted_connection_dict = {
key: connection_yaml[key]
for key in sorted(
connection_yaml.keys(),
key=lambda x: (0, key_order.index(x)) if x in key_order else (1, x),
)
}
with open(output_path, "w", encoding="utf-8") as f:
f.write(dump_yaml(sorted_connection_dict))
return env_var_names
def _migrate_connections(self, connection_names: List[str], output_dir: Path):
from promptflow._sdk._pf_client import PFClient
output_dir.mkdir(parents=True, exist_ok=True)
local_client = PFClient()
connection_paths, env_var_names = [], {}
for connection_name in connection_names:
connection = local_client.connections.get(name=connection_name, with_secrets=True)
connection_var_name = self._refine_connection_name(connection_name)
connection_paths.append(output_dir / f"{connection_var_name}.yaml")
for env_var_name in self._dump_connection(
connection,
connection_paths[-1],
):
if env_var_name in env_var_names:
raise RuntimeError(
f"environment variable name conflict: connection {connection_name} and "
f"{env_var_names[env_var_name]} on {env_var_name}"
)
env_var_names[env_var_name] = connection_name
return connection_paths, list(env_var_names.keys())
def _export_flow_connections(
self,
built_flow_dag_path: Path,
*,
output_dir: Path,
):
"""Export flow connections to yaml files.
:param built_flow_dag_path: path to built flow dag yaml file. Given this is a built flow, we can assume
that the flow involves no additional includes, symlink, or variant.
:param output_dir: output directory to export connections
"""
flow: ProtectedFlow = load_flow(built_flow_dag_path)
with _change_working_dir(flow.code):
if flow.language == FlowLanguage.CSharp:
from promptflow.batch import CSharpExecutorProxy
return self._migrate_connections(
connection_names=SubmitterHelper.get_used_connection_names(
tools_meta=CSharpExecutorProxy.get_tool_metadata(
flow_file=flow.flow_dag_path,
working_dir=flow.code,
),
flow_dag=flow.dag,
),
output_dir=output_dir,
)
else:
# TODO: avoid using executable here
from promptflow.contracts.flow import Flow as ExecutableFlow
executable = ExecutableFlow.from_yaml(flow_file=flow.path, working_dir=flow.code)
return self._migrate_connections(
connection_names=executable.get_connection_names(),
output_dir=output_dir,
)
def _build_flow(
self,
flow_dag_path: Path,
*,
output: Union[str, PathLike],
tuning_node: str = None,
node_variant: str = None,
update_flow_tools_json: bool = True,
):
# TODO: confirm if we need to import this
from promptflow._sdk._submitter import variant_overwrite_context
flow_copy_target = Path(output)
flow_copy_target.mkdir(parents=True, exist_ok=True)
# resolve additional includes and copy flow directory first to guarantee there is a final flow directory
# TODO: shall we pop "node_variants" unless keep-variants is specified?
with variant_overwrite_context(
flow_dag_path,
tuning_node=tuning_node,
variant=node_variant,
drop_node_variants=True,
) as temp_flow:
# TODO: avoid copy for twice
copy_tree_respect_template_and_ignore_file(temp_flow.code, flow_copy_target)
if update_flow_tools_json:
generate_flow_tools_json(flow_copy_target)
return flow_copy_target / flow_dag_path.name
def _export_to_docker(
self,
flow_dag_path: Path,
output_dir: Path,
*,
env_var_names: List[str],
connection_paths: List[Path],
flow_name: str,
is_csharp_flow: bool = False,
):
(output_dir / "settings.json").write_text(
data=json.dumps({env_var_name: "" for env_var_name in env_var_names}, indent=2),
encoding="utf-8",
)
environment_config = self._build_environment_config(flow_dag_path)
# TODO: make below strings constants
if is_csharp_flow:
source = Path(__file__).parent.parent / "data" / "docker_csharp"
else:
source = Path(__file__).parent.parent / "data" / "docker"
copy_tree_respect_template_and_ignore_file(
source=source,
target=output_dir,
render_context={
"env": environment_config,
"flow_name": f"{flow_name}-{generate_random_string(6)}",
"local_db_rel_path": LOCAL_MGMT_DB_PATH.relative_to(Path.home()).as_posix(),
"connection_yaml_paths": list(map(lambda x: x.relative_to(output_dir).as_posix(), connection_paths)),
},
)
def _build_as_executable(
self,
flow_dag_path: Path,
output_dir: Path,
*,
flow_name: str,
env_var_names: List[str],
):
try:
import bs4 # noqa: F401
import PyInstaller # noqa: F401
import streamlit
import streamlit_quill # noqa: F401
except ImportError as ex:
raise UserErrorException(
f"Please try 'pip install promptflow[executable]' to install dependency, {ex.msg}."
)
from promptflow.contracts.flow import Flow as ExecutableFlow
(output_dir / "settings.json").write_text(
data=json.dumps({env_var_name: "" for env_var_name in env_var_names}, indent=2),
encoding="utf-8",
)
environment_config = self._build_environment_config(flow_dag_path)
hidden_imports = []
if (
environment_config.get("python_requirements_txt", None)
and (flow_dag_path.parent / "requirements.txt").is_file()
):
with open(flow_dag_path.parent / "requirements.txt", "r", encoding="utf-8") as file:
file_content = file.read()
hidden_imports = file_content.splitlines()
runtime_interpreter_path = (Path(streamlit.__file__).parent / "runtime").as_posix()
executable = ExecutableFlow.from_yaml(flow_file=Path(flow_dag_path.name), working_dir=flow_dag_path.parent)
flow_inputs = {
flow_input: (value.default, value.type.value)
for flow_input, value in executable.inputs.items()
if not value.is_chat_history
}
flow_inputs_params = ["=".join([flow_input, flow_input]) for flow_input, _ in flow_inputs.items()]
flow_inputs_params = ",".join(flow_inputs_params)
is_chat_flow, chat_history_input_name, _ = self._is_chat_flow(executable)
label = "Chat" if is_chat_flow else "Run"
copy_tree_respect_template_and_ignore_file(
source=Path(__file__).parent.parent / "data" / "executable",
target=output_dir,
render_context={
"hidden_imports": hidden_imports,
"flow_name": flow_name,
"runtime_interpreter_path": runtime_interpreter_path,
"flow_inputs": flow_inputs,
"flow_inputs_params": flow_inputs_params,
"flow_path": None,
"is_chat_flow": is_chat_flow,
"chat_history_input_name": chat_history_input_name,
"label": label,
},
)
self._run_pyinstaller(output_dir)
def _run_pyinstaller(self, output_dir):
with _change_working_dir(output_dir, mkdir=False):
subprocess.run(["pyinstaller", "app.spec"], check=True)
print("PyInstaller command executed successfully.")
@monitor_operation(activity_name="pf.flows.build", activity_type=ActivityType.PUBLICAPI)
def build(
self,
flow: Union[str, PathLike],
*,
output: Union[str, PathLike],
format: str = "docker",
variant: str = None,
**kwargs,
):
"""
Build flow to other format.
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param format: export format, support "docker" and "executable" only for now
:type format: str
:param output: output directory
:type output: Union[str, PathLike]
:param variant: node variant in format of {node_name}.{variant_name},
will use default variant if not specified.
:type variant: str
:return: no return
:rtype: None
"""
output_dir = Path(output).absolute()
output_dir.mkdir(parents=True, exist_ok=True)
flow: ProtectedFlow = load_flow(flow)
is_csharp_flow = flow.dag.get(LANGUAGE_KEY, "") == FlowLanguage.CSharp
if format not in ["docker", "executable"]:
raise ValueError(f"Unsupported export format: {format}")
if variant:
tuning_node, node_variant = parse_variant(variant)
else:
tuning_node, node_variant = None, None
flow_only = kwargs.pop("flow_only", False)
if flow_only:
output_flow_dir = output_dir
else:
output_flow_dir = output_dir / "flow"
new_flow_dag_path = self._build_flow(
flow_dag_path=flow.flow_dag_path,
output=output_flow_dir,
tuning_node=tuning_node,
node_variant=node_variant,
update_flow_tools_json=False if is_csharp_flow else True,
)
if flow_only:
return
# use new flow dag path below as origin one may miss additional includes
connection_paths, env_var_names = self._export_flow_connections(
built_flow_dag_path=new_flow_dag_path,
output_dir=output_dir / "connections",
)
if format == "docker":
self._export_to_docker(
flow_dag_path=new_flow_dag_path,
output_dir=output_dir,
connection_paths=connection_paths,
flow_name=flow.name,
env_var_names=env_var_names,
is_csharp_flow=is_csharp_flow,
)
elif format == "executable":
self._build_as_executable(
flow_dag_path=new_flow_dag_path,
output_dir=output_dir,
flow_name=flow.name,
env_var_names=env_var_names,
)
@classmethod
@contextlib.contextmanager
def _resolve_additional_includes(cls, flow_dag_path: Path) -> Iterable[Path]:
# TODO: confirm if we need to import this
from promptflow._sdk._submitter import remove_additional_includes
# Eager flow may not contain a yaml file, skip resolving additional includes
def is_yaml_file(file_path):
_, file_extension = os.path.splitext(file_path)
return file_extension.lower() in (".yaml", ".yml")
if is_yaml_file(flow_dag_path) and _get_additional_includes(flow_dag_path):
# Merge the flow folder and additional includes to temp folder.
# TODO: support a flow_dag_path with a name different from flow.dag.yaml
with _merge_local_code_and_additional_includes(code_path=flow_dag_path.parent) as temp_dir:
remove_additional_includes(Path(temp_dir))
yield Path(temp_dir) / flow_dag_path.name
else:
yield flow_dag_path
@monitor_operation(activity_name="pf.flows.validate", activity_type=ActivityType.PUBLICAPI)
def validate(self, flow: Union[str, PathLike], *, raise_error: bool = False, **kwargs) -> ValidationResult:
"""
Validate flow.
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param raise_error: whether raise error when validation failed
:type raise_error: bool
:return: a validation result object
:rtype: ValidationResult
"""
flow_entity: ProtectedFlow = load_flow(source=flow)
# TODO: put off this if we do path existence check in FlowSchema on fields other than additional_includes
validation_result = flow_entity._validate()
source_path_mapping = {}
flow_tools, tools_errors = self._generate_tools_meta(
flow=flow_entity.flow_dag_path,
source_path_mapping=source_path_mapping,
)
flow_entity.tools_meta_path.write_text(
data=json.dumps(flow_tools, indent=4),
encoding=DEFAULT_ENCODING,
)
if tools_errors:
for source_name, message in tools_errors.items():
for yaml_path in source_path_mapping.get(source_name, []):
validation_result.append_error(
yaml_path=yaml_path,
message=message,
)
# flow in control plane is read-only, so resolve location makes sense even in SDK experience
validation_result.resolve_location_for_diagnostics(flow_entity.flow_dag_path.as_posix())
flow_entity._try_raise(
validation_result,
raise_error=raise_error,
)
return validation_result
@monitor_operation(activity_name="pf.flows._generate_tools_meta", activity_type=ActivityType.INTERNALCALL)
def _generate_tools_meta(
self,
flow: Union[str, PathLike],
*,
source_name: str = None,
source_path_mapping: Dict[str, List[str]] = None,
timeout: int = FLOW_TOOLS_JSON_GEN_TIMEOUT,
) -> Tuple[dict, dict]:
"""Generate flow tools meta for a specific flow or a specific node in the flow.
This is a private interface for vscode extension, so do not change the interface unless necessary.
Usage:
from promptflow import PFClient
PFClient().flows._generate_tools_meta(flow="flow.dag.yaml", source_name="convert_to_dict.py")
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param source_name: source name to generate tools meta. If not specified, generate tools meta for all sources.
:type source_name: str
:param source_path_mapping: If passed in None, do nothing; if passed in a dict, will record all reference yaml
paths for each source in the dict passed in.
:type source_path_mapping: Dict[str, List[str]]
:param timeout: timeout for generating tools meta
:type timeout: int
:return: dict of tools meta and dict of tools errors
:rtype: Tuple[dict, dict]
"""
flow: ProtectedFlow = load_flow(source=flow)
if not isinstance(flow, ProtectedFlow):
# No tools meta for eager flow
return {}, {}
with self._resolve_additional_includes(flow.flow_dag_path) as new_flow_dag_path:
flow_tools = generate_flow_tools_json(
flow_directory=new_flow_dag_path.parent,
dump=False,
raise_error=False,
include_errors_in_output=True,
target_source=source_name,
used_packages_only=True,
source_path_mapping=source_path_mapping,
timeout=timeout,
)
flow_tools_meta = flow_tools.pop("code", {})
tools_errors = {}
nodes_with_error = [node_name for node_name, message in flow_tools_meta.items() if isinstance(message, str)]
for node_name in nodes_with_error:
tools_errors[node_name] = flow_tools_meta.pop(node_name)
additional_includes = _get_additional_includes(flow.flow_dag_path)
if additional_includes:
additional_files = {}
for include in additional_includes:
include_path = Path(include) if Path(include).is_absolute() else flow.code / include
if include_path.is_file():
file_name = Path(include).name
additional_files[Path(file_name)] = os.path.relpath(include_path, flow.code)
else:
if not Path(include).is_absolute():
include = flow.code / include
files = glob.glob(os.path.join(include, "**"), recursive=True)
additional_files.update(
{
Path(os.path.relpath(path, include.parent)): os.path.relpath(path, flow.code)
for path in files
}
)
for tool in flow_tools_meta.values():
source = tool.get("source", None)
if source and Path(source) in additional_files:
tool["source"] = additional_files[Path(source)]
flow_tools["code"] = flow_tools_meta
return flow_tools, tools_errors
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_local_azure_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import re
from typing import List
from promptflow._sdk._constants import AZURE_WORKSPACE_REGEX_FORMAT, MAX_LIST_CLI_RESULTS
from promptflow._sdk._telemetry import ActivityType, WorkspaceTelemetryMixin, monitor_operation
from promptflow._sdk._utils import interactive_credential_disabled, is_from_cli, is_github_codespaces, print_red_error
from promptflow._sdk.entities._connection import _Connection
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.azure._utils.gerneral import get_arm_token
logger = get_cli_sdk_logger()
class LocalAzureConnectionOperations(WorkspaceTelemetryMixin):
def __init__(self, connection_provider, **kwargs):
self._subscription_id, self._resource_group, self._workspace_name = self._extract_workspace(connection_provider)
self._credential = kwargs.pop("credential", None) or self._get_credential()
super().__init__(
subscription_id=self._subscription_id,
resource_group_name=self._resource_group,
workspace_name=self._workspace_name,
**kwargs,
)
# Lazy init client as ml_client initialization require workspace read permission
self._pfazure_client = None
self._user_agent = kwargs.pop("user_agent", None)
@property
def _client(self):
if self._pfazure_client is None:
from promptflow.azure._pf_client import PFClient as PFAzureClient
self._pfazure_client = PFAzureClient(
# TODO: disable interactive credential when starting as a service
credential=self._credential,
subscription_id=self._subscription_id,
resource_group_name=self._resource_group,
workspace_name=self._workspace_name,
user_agent=self._user_agent,
)
return self._pfazure_client
@classmethod
def _get_credential(cls):
from azure.ai.ml._azure_environments import AzureEnvironments, EndpointURLS, _get_cloud, _get_default_cloud_name
from azure.identity import DefaultAzureCredential, DeviceCodeCredential
if is_from_cli():
try:
# Try getting token for cli without interactive login
cloud_name = _get_default_cloud_name()
if cloud_name != AzureEnvironments.ENV_DEFAULT:
cloud = _get_cloud(cloud=cloud_name)
authority = cloud.get(EndpointURLS.ACTIVE_DIRECTORY_ENDPOINT)
credential = DefaultAzureCredential(authority=authority, exclude_shared_token_cache_credential=True)
else:
credential = DefaultAzureCredential()
get_arm_token(credential=credential)
except Exception:
print_red_error(
"Please run 'az login' or 'az login --use-device-code' to set up account. "
"See https://docs.microsoft.com/cli/azure/authenticate-azure-cli for more details."
)
exit(1)
if interactive_credential_disabled():
return DefaultAzureCredential(exclude_interactive_browser_credential=True)
if is_github_codespaces():
# For code spaces, append device code credential as the fallback option.
credential = DefaultAzureCredential()
credential.credentials = (*credential.credentials, DeviceCodeCredential())
return credential
return DefaultAzureCredential(exclude_interactive_browser_credential=False)
@classmethod
def _extract_workspace(cls, connection_provider):
match = re.match(AZURE_WORKSPACE_REGEX_FORMAT, connection_provider)
if not match or len(match.groups()) != 5:
raise ValueError(
"Malformed connection provider string, expected azureml:/subscriptions/<subscription_id>/"
"resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/"
f"workspaces/<workspace_name>, got {connection_provider}"
)
subscription_id = match.group(1)
resource_group = match.group(3)
workspace_name = match.group(5)
return subscription_id, resource_group, workspace_name
@monitor_operation(activity_name="pf.connections.azure.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: int = MAX_LIST_CLI_RESULTS,
all_results: bool = False,
) -> List[_Connection]:
"""List connections.
:return: List of run objects.
:rtype: List[~promptflow.sdk.entities._connection._Connection]
"""
if max_results != MAX_LIST_CLI_RESULTS or all_results:
logger.warning(
"max_results and all_results are not supported for workspace connection and will be ignored."
)
return self._client._connections.list()
@monitor_operation(activity_name="pf.connections.azure.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str, **kwargs) -> _Connection:
"""Get a connection entity.
:param name: Name of the connection.
:type name: str
:return: connection object retrieved from the database.
:rtype: ~promptflow.sdk.entities._connection._Connection
"""
with_secrets = kwargs.get("with_secrets", False)
if with_secrets:
# Do not use pfazure_client here as it requires workspace read permission
# Get secrets from arm only requires workspace listsecrets permission
from promptflow.azure.operations._arm_connection_operations import ArmConnectionOperations
return ArmConnectionOperations._direct_get(
name, self._subscription_id, self._resource_group, self._workspace_name, self._credential
)
return self._client._connections.get(name)
@monitor_operation(activity_name="pf.connections.azure.delete", activity_type=ActivityType.PUBLICAPI)
def delete(self, name: str) -> None:
"""Delete a connection entity.
:param name: Name of the connection.
:type name: str
"""
raise NotImplementedError(
"Delete workspace connection is not supported in promptflow, "
"please manage it in workspace portal, az ml cli or AzureML SDK."
)
@monitor_operation(activity_name="pf.connections.azure.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, connection: _Connection, **kwargs):
"""Create or update a connection.
:param connection: Run object to create or update.
:type connection: ~promptflow.sdk.entities._connection._Connection
"""
raise NotImplementedError(
"Create or update workspace connection is not supported in promptflow, "
"please manage it in workspace portal, az ml cli or AzureML SDK."
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from datetime import datetime
from typing import List
from promptflow._sdk._constants import MAX_LIST_CLI_RESULTS
from promptflow._sdk._orm import Connection as ORMConnection
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import safe_parse_object_list
from promptflow._sdk.entities._connection import _Connection
class ConnectionOperations(TelemetryMixin):
"""ConnectionOperations."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
@monitor_operation(activity_name="pf.connections.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: int = MAX_LIST_CLI_RESULTS,
all_results: bool = False,
) -> List[_Connection]:
"""List connections.
:param max_results: Max number of results to return.
:type max_results: int
:param all_results: Return all results.
:type all_results: bool
:return: List of run objects.
:rtype: List[~promptflow.sdk.entities._connection._Connection]
"""
orm_connections = ORMConnection.list(max_results=max_results, all_results=all_results)
return safe_parse_object_list(
obj_list=orm_connections,
parser=_Connection._from_orm_object,
message_generator=lambda x: f"Failed to load connection {x.connectionName}, skipped.",
)
@monitor_operation(activity_name="pf.connections.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str, **kwargs) -> _Connection:
"""Get a connection entity.
:param name: Name of the connection.
:type name: str
:return: connection object retrieved from the database.
:rtype: ~promptflow.sdk.entities._connection._Connection
"""
return self._get(name, **kwargs)
def _get(self, name: str, **kwargs) -> _Connection:
with_secrets = kwargs.get("with_secrets", False)
raise_error = kwargs.get("raise_error", True)
orm_connection = ORMConnection.get(name, raise_error)
if orm_connection is None:
return None
if with_secrets:
return _Connection._from_orm_object_with_secrets(orm_connection)
return _Connection._from_orm_object(orm_connection)
@monitor_operation(activity_name="pf.connections.delete", activity_type=ActivityType.PUBLICAPI)
def delete(self, name: str) -> None:
"""Delete a connection entity.
:param name: Name of the connection.
:type name: str
"""
ORMConnection.delete(name)
@monitor_operation(activity_name="pf.connections.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, connection: _Connection, **kwargs):
"""Create or update a connection.
:param connection: Run object to create or update.
:type connection: ~promptflow.sdk.entities._connection._Connection
"""
orm_object = connection._to_orm_object()
now = datetime.now().isoformat()
if orm_object.createdDate is None:
orm_object.createdDate = now
orm_object.lastModifiedDate = now
ORMConnection.create_or_update(orm_object)
return self.get(connection.name)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_run_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import os.path
import sys
import time
from dataclasses import asdict
from typing import Any, Dict, List, Optional, Union
from promptflow._constants import LANGUAGE_KEY, AvailableIDE, FlowLanguage
from promptflow._sdk._constants import (
MAX_RUN_LIST_RESULTS,
MAX_SHOW_DETAILS_RESULTS,
FlowRunProperties,
ListViewType,
RunInfoSources,
RunStatus,
)
from promptflow._sdk._errors import InvalidRunStatusError, RunExistsError, RunNotFoundError, RunOperationParameterError
from promptflow._sdk._orm import RunInfo as ORMRun
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import incremental_print, print_red_error, safe_parse_object_list
from promptflow._sdk._visualize_functions import dump_html, generate_html_string
from promptflow._sdk.entities import Run
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import load_yaml_string
from promptflow.contracts._run_management import RunDetail, RunMetadata, RunVisualization, VisualizationConfig
from promptflow.exceptions import UserErrorException
RUNNING_STATUSES = RunStatus.get_running_statuses()
logger = get_cli_sdk_logger()
class RunOperations(TelemetryMixin):
"""RunOperations."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
@monitor_operation(activity_name="pf.runs.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: Optional[int] = MAX_RUN_LIST_RESULTS,
*,
list_view_type: ListViewType = ListViewType.ACTIVE_ONLY,
) -> List[Run]:
"""List runs.
:param max_results: Max number of results to return. Default: MAX_RUN_LIST_RESULTS.
:type max_results: Optional[int]
:param list_view_type: View type for including/excluding (for example) archived runs. Default: ACTIVE_ONLY.
:type include_archived: Optional[ListViewType]
:return: List of run objects.
:rtype: List[~promptflow.entities.Run]
"""
orm_runs = ORMRun.list(max_results=max_results, list_view_type=list_view_type)
return safe_parse_object_list(
obj_list=orm_runs,
parser=Run._from_orm_object,
message_generator=lambda x: f"Error parsing run {x.name!r}, skipped.",
)
@monitor_operation(activity_name="pf.runs.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str) -> Run:
"""Get a run entity.
:param name: Name of the run.
:type name: str
:return: run object retrieved from the database.
:rtype: ~promptflow.entities.Run
"""
return self._get(name)
def _get(self, name: str) -> Run:
name = Run._validate_and_return_run_name(name)
try:
return Run._from_orm_object(ORMRun.get(name))
except RunNotFoundError as e:
raise e
@monitor_operation(activity_name="pf.runs.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, run: Run, **kwargs) -> Run:
"""Create or update a run.
:param run: Run object to create or update.
:type run: ~promptflow.entities.Run
:return: Run object created or updated.
:rtype: ~promptflow.entities.Run
"""
# create run from an existing run folder
if run._run_source == RunInfoSources.EXISTING_RUN:
return self._create_run_from_existing_run_folder(run=run, **kwargs)
# TODO: change to async
stream = kwargs.pop("stream", False)
try:
from promptflow._sdk._submitter import RunSubmitter
created_run = RunSubmitter(run_operations=self).submit(run=run, **kwargs)
if stream:
self.stream(created_run)
return created_run
except RunExistsError:
raise RunExistsError(f"Run {run.name!r} already exists.")
def _create_run_from_existing_run_folder(self, run: Run, **kwargs) -> Run:
"""Create run from existing run folder."""
try:
self.get(run.name)
except RunNotFoundError:
pass
else:
raise RunExistsError(f"Run {run.name!r} already exists.")
try:
run._dump()
return run
except Exception as e:
raise UserErrorException(
f"Failed to create run {run.name!r} from existing run folder {run.source!r}: {str(e)}"
) from e
def _print_run_summary(self, run: Run) -> None:
print("======= Run Summary =======\n")
duration = str(run._end_time - run._created_on)
print(
f'Run name: "{run.name}"\n'
f'Run status: "{run.status}"\n'
f'Start time: "{run._created_on}"\n'
f'Duration: "{duration}"\n'
f'Output path: "{run._output_path}"\n'
)
@monitor_operation(activity_name="pf.runs.stream", activity_type=ActivityType.PUBLICAPI)
def stream(self, name: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream run logs to the console.
:param name: Name of the run, or run object.
:type name: Union[str, ~promptflow.sdk.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: Run object.
:rtype: ~promptflow.entities.Run
"""
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
local_storage = LocalStorageOperations(run=run)
file_handler = sys.stdout
try:
printed = 0
run = self.get(run.name)
while run.status in RUNNING_STATUSES or run.status == RunStatus.FINALIZING:
file_handler.flush()
available_logs = local_storage.logger.get_logs()
printed = incremental_print(available_logs, printed, file_handler)
time.sleep(10)
run = self.get(run.name)
# ensure all logs are printed
file_handler.flush()
available_logs = local_storage.logger.get_logs()
incremental_print(available_logs, printed, file_handler)
self._print_run_summary(run)
except KeyboardInterrupt:
error_message = "The output streaming for the run was interrupted, but the run is still executing."
print(error_message)
if run.status == RunStatus.FAILED or run.status == RunStatus.CANCELED:
if run.status == RunStatus.FAILED:
error_message = local_storage.load_exception().get("message", "Run fails with unknown error.")
else:
error_message = "Run is canceled."
if raise_on_error:
raise InvalidRunStatusError(error_message)
else:
print_red_error(error_message)
return run
@monitor_operation(activity_name="pf.runs.archive", activity_type=ActivityType.PUBLICAPI)
def archive(self, name: Union[str, Run]) -> Run:
"""Archive a run.
:param name: Name of the run.
:type name: str
:return: archived run object.
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
ORMRun.get(name).archive()
return self.get(name)
@monitor_operation(activity_name="pf.runs.restore", activity_type=ActivityType.PUBLICAPI)
def restore(self, name: Union[str, Run]) -> Run:
"""Restore a run.
:param name: Name of the run.
:type name: str
:return: restored run object.
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
ORMRun.get(name).restore()
return self.get(name)
@monitor_operation(activity_name="pf.runs.update", activity_type=ActivityType.PUBLICAPI)
def update(
self,
name: Union[str, Run],
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs,
) -> Run:
"""Update run status.
:param name: run name
:param display_name: display name to update
:param description: description to update
:param tags: tags to update
:param kwargs: other fields to update, fields not supported will be directly dropped.
:return: updated run object
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
# the kwargs is to support update run status scenario but keep it private
ORMRun.get(name).update(display_name=display_name, description=description, tags=tags, **kwargs)
return self.get(name)
@monitor_operation(activity_name="pf.runs.delete", activity_type=ActivityType.PUBLICAPI)
def delete(
self,
name: str,
) -> None:
"""Delete run permanently.
Caution: This operation will delete the run permanently from your local disk.
Both run entity and output data will be deleted.
:param name: run name to delete
:return: None
"""
valid_run = self.get(name)
LocalStorageOperations(valid_run).delete()
ORMRun.delete(name)
@monitor_operation(activity_name="pf.runs.get_details", activity_type=ActivityType.PUBLICAPI)
def get_details(
self, name: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False
) -> "DataFrame":
"""Get the details from the run.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param name: The run name or run object
:type name: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
from pandas import DataFrame
# if all_results is True, set max_results to sys.maxsize
if all_results:
max_results = sys.maxsize
if not isinstance(max_results, int) or max_results < 1:
raise RunOperationParameterError(f"'max_results' must be a positive integer, got {max_results!r}")
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
local_storage = LocalStorageOperations(run=run)
inputs, outputs = local_storage.load_inputs_and_outputs()
inputs = inputs.to_dict("list")
outputs = outputs.to_dict("list")
data = {}
columns = []
for k in inputs:
new_k = f"inputs.{k}"
data[new_k] = copy.deepcopy(inputs[k])
columns.append(new_k)
for k in outputs:
new_k = f"outputs.{k}"
data[new_k] = copy.deepcopy(outputs[k])
columns.append(new_k)
df = DataFrame(data).head(max_results).reindex(columns=columns)
return df
@monitor_operation(activity_name="pf.runs.get_metrics", activity_type=ActivityType.PUBLICAPI)
def get_metrics(self, name: Union[str, Run]) -> Dict[str, Any]:
"""Get run metrics.
:param name: name of the run.
:type name: str
:return: Run metrics.
:rtype: Dict[str, Any]
"""
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
run._check_run_status_is_completed()
local_storage = LocalStorageOperations(run=run)
return local_storage.load_metrics()
def _visualize(self, runs: List[Run], html_path: Optional[str] = None) -> None:
details: List[RunDetail] = []
metadatas: List[RunMetadata] = []
configs: List[VisualizationConfig] = []
for run in runs:
# check run status first
# if run status is not compeleted, there might be unexpected error during parse data
# so we directly raise error if there is any incomplete run
run._check_run_status_is_completed()
local_storage = LocalStorageOperations(run)
# nan, inf and -inf are not JSON serializable, which will lead to JavaScript parse error
# so specify `parse_const_as_str` as True to parse them as string
detail = local_storage.load_detail(parse_const_as_str=True)
# ad-hoc step: make logs field empty to avoid too big HTML file
# we don't provide logs view in visualization page for now
# when we enable, we will save those big data (e.g. logs) in separate file(s)
# JS load can be faster than static HTML
for i in range(len(detail["node_runs"])):
detail["node_runs"][i]["logs"] = {"stdout": "", "stderr": ""}
metadata = RunMetadata(
name=run.name,
display_name=run.display_name,
create_time=run.created_on,
flow_path=run.properties.get(FlowRunProperties.FLOW_PATH, None),
output_path=run.properties[FlowRunProperties.OUTPUT_PATH],
tags=run.tags,
lineage=run.run,
metrics=self.get_metrics(name=run.name),
dag=local_storage.load_dag_as_string(),
flow_tools_json=local_storage.load_flow_tools_json(),
mode="eager" if local_storage.eager_mode else "",
)
details.append(copy.deepcopy(detail))
metadatas.append(asdict(metadata))
# TODO: add language to run metadata
flow_dag = load_yaml_string(metadata.dag) or {}
configs.append(
VisualizationConfig(
[AvailableIDE.VS_CODE]
if flow_dag.get(LANGUAGE_KEY, FlowLanguage.Python) == FlowLanguage.Python
else [AvailableIDE.VS]
)
)
data_for_visualize = RunVisualization(
detail=details,
metadata=metadatas,
config=configs,
)
html_string = generate_html_string(asdict(data_for_visualize))
# if html_path is specified, not open it in webbrowser(as it comes from VSC)
dump_html(html_string, html_path=html_path, open_html=html_path is None)
@monitor_operation(activity_name="pf.runs.visualize", activity_type=ActivityType.PUBLICAPI)
def visualize(self, runs: Union[str, Run, List[str], List[Run]], **kwargs) -> None:
"""Visualize run(s).
:param runs: List of run objects, or names of the runs.
:type runs: Union[str, ~promptflow.sdk.entities.Run, List[str], List[~promptflow.sdk.entities.Run]]
"""
if not isinstance(runs, list):
runs = [runs]
validated_runs = []
for run in runs:
run_name = Run._validate_and_return_run_name(run)
validated_runs.append(self.get(name=run_name))
html_path = kwargs.pop("html_path", None)
try:
self._visualize(validated_runs, html_path=html_path)
except InvalidRunStatusError as e:
error_message = f"Cannot visualize non-completed run. {str(e)}"
logger.error(error_message)
def _get_outputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]:
"""Get the outputs of the run, load from local storage."""
local_storage = self._get_local_storage(run)
return local_storage.load_outputs()
def _get_inputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]:
"""Get the outputs of the run, load from local storage."""
local_storage = self._get_local_storage(run)
return local_storage.load_inputs()
def _get_outputs_path(self, run: Union[str, Run]) -> str:
"""Get the outputs file path of the run."""
local_storage = self._get_local_storage(run)
return local_storage._outputs_path if local_storage.load_outputs() else None
def _get_data_path(self, run: Union[str, Run]) -> str:
"""Get the outputs file path of the run."""
local_storage = self._get_local_storage(run)
# TODO: what if the data is deleted?
if local_storage._data_path and not os.path.exists(local_storage._data_path):
raise UserErrorException(
f"Data path {local_storage._data_path} for run {run.name} does not exist. "
"Please make sure it exists and not deleted."
)
return local_storage._data_path
def _get_local_storage(self, run: Union[str, Run]) -> LocalStorageOperations:
"""Get the local storage of the run."""
if isinstance(run, str):
run = self.get(name=run)
return LocalStorageOperations(run)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_local_storage_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import datetime
import json
import logging
import shutil
from dataclasses import asdict, dataclass
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, NewType, Optional, Tuple, Union
from filelock import FileLock
from promptflow import load_flow
from promptflow._sdk._constants import (
HOME_PROMPT_FLOW_DIR,
LINE_NUMBER,
LOCAL_STORAGE_BATCH_SIZE,
PROMPT_FLOW_DIR_NAME,
LocalStorageFilenames,
RunInfoSources,
)
from promptflow._sdk._errors import BulkRunException, InvalidRunError
from promptflow._sdk._utils import (
PromptflowIgnoreFile,
generate_flow_tools_json,
json_dump,
json_load,
pd_read_json,
read_open,
write_open,
)
from promptflow._sdk.entities import Run
from promptflow._sdk.entities._eager_flow import EagerFlow
from promptflow._sdk.entities._flow import Flow
from promptflow._utils.dataclass_serializer import serialize
from promptflow._utils.exception_utils import PromptflowExceptionPresenter
from promptflow._utils.logger_utils import LogContext, get_cli_sdk_logger
from promptflow._utils.multimedia_utils import get_file_reference_encoder
from promptflow._utils.yaml_utils import load_yaml
from promptflow.batch._result import BatchResult
from promptflow.contracts.multimedia import Image
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
from promptflow.contracts.run_info import Status
from promptflow.contracts.run_mode import RunMode
from promptflow.exceptions import UserErrorException
from promptflow.storage import AbstractRunStorage
logger = get_cli_sdk_logger()
RunInputs = NewType("RunInputs", Dict[str, List[Any]])
RunOutputs = NewType("RunOutputs", Dict[str, List[Any]])
RunMetrics = NewType("RunMetrics", Dict[str, Any])
@dataclass
class LoggerOperations(LogContext):
stream: bool = False
@property
def log_path(self) -> str:
return str(self.file_path)
def get_logs(self) -> str:
with read_open(self.file_path) as f:
return f.read()
def _get_execute_loggers_list(cls) -> List[logging.Logger]:
result = super()._get_execute_loggers_list()
result.append(logger)
return result
def get_initializer(self):
return partial(
LoggerOperations,
file_path=self.file_path,
run_mode=self.run_mode,
credential_list=self.credential_list,
stream=self.stream,
)
def __enter__(self):
log_path = Path(self.log_path)
log_path.parent.mkdir(parents=True, exist_ok=True)
if self.run_mode == RunMode.Batch:
log_path.touch(exist_ok=True)
else:
if log_path.exists():
# for non batch run, clean up previous log content
try:
with write_open(log_path) as file:
file.truncate(0)
except Exception as e:
logger.warning(f"Failed to clean up the previous log content because {e}")
else:
log_path.touch()
for _logger in self._get_execute_loggers_list():
for handler in _logger.handlers:
if self.stream is False and isinstance(handler, logging.StreamHandler):
handler.setLevel(logging.CRITICAL)
super().__enter__()
def __exit__(self, *args):
super().__exit__(*args)
for _logger in self._get_execute_loggers_list():
for handler in _logger.handlers:
if self.stream is False and isinstance(handler, logging.StreamHandler):
handler.setLevel(logging.CRITICAL)
@dataclass
class NodeRunRecord:
NodeName: str
line_number: int
run_info: str
start_time: datetime
end_time: datetime
status: str
@staticmethod
def from_run_info(node_run_info: NodeRunInfo) -> "NodeRunRecord":
return NodeRunRecord(
NodeName=node_run_info.node,
line_number=node_run_info.index,
run_info=serialize(node_run_info),
start_time=node_run_info.start_time.isoformat(),
end_time=node_run_info.end_time.isoformat(),
status=node_run_info.status.value,
)
def dump(self, path: Path, run_name: str) -> None:
# for nodes in first line run and all reduce nodes, the target filename is 000000000.jsonl
# so we need to handle concurrent write with file lock
filename_need_lock = "0".zfill(LocalStorageOperations.LINE_NUMBER_WIDTH) + ".jsonl"
if path.name == filename_need_lock:
file_lock_path = (HOME_PROMPT_FLOW_DIR / f"{run_name}.{self.NodeName}.lock").resolve()
lock = FileLock(file_lock_path)
lock.acquire()
try:
json_dump(asdict(self), path)
finally:
lock.release()
else:
# for normal nodes in other line runs, directly write
json_dump(asdict(self), path)
@dataclass
class LineRunRecord:
line_number: int
run_info: str
start_time: datetime.datetime
end_time: datetime.datetime
name: str
description: str
status: str
tags: str
@staticmethod
def from_flow_run_info(flow_run_info: FlowRunInfo) -> "LineRunRecord":
return LineRunRecord(
line_number=flow_run_info.index,
run_info=serialize(flow_run_info),
start_time=flow_run_info.start_time.isoformat(),
end_time=flow_run_info.end_time.isoformat(),
name=flow_run_info.name,
description=flow_run_info.description,
status=flow_run_info.status.value,
tags=flow_run_info.tags,
)
def dump(self, path: Path) -> None:
json_dump(asdict(self), path)
class LocalStorageOperations(AbstractRunStorage):
"""LocalStorageOperations."""
LINE_NUMBER_WIDTH = 9
def __init__(self, run: Run, stream=False, run_mode=RunMode.Test):
self._run = run
self.path = self._prepare_folder(self._run._output_path)
self.logger = LoggerOperations(
file_path=self.path / LocalStorageFilenames.LOG, stream=stream, run_mode=run_mode
)
# snapshot
self._snapshot_folder_path = self._prepare_folder(self.path / LocalStorageFilenames.SNAPSHOT_FOLDER)
self._dag_path = self._snapshot_folder_path / LocalStorageFilenames.DAG
self._flow_tools_json_path = (
self._snapshot_folder_path / PROMPT_FLOW_DIR_NAME / LocalStorageFilenames.FLOW_TOOLS_JSON
)
self._inputs_path = self.path / LocalStorageFilenames.INPUTS # keep this for other usages
# below inputs and outputs are dumped by SDK
self._sdk_inputs_path = self._inputs_path
self._sdk_output_path = self.path / LocalStorageFilenames.OUTPUTS
# metrics
self._metrics_path = self.path / LocalStorageFilenames.METRICS
# legacy files: detail.json and outputs.jsonl(not the one in flow_outputs folder)
self._detail_path = self.path / LocalStorageFilenames.DETAIL
self._legacy_outputs_path = self.path / LocalStorageFilenames.OUTPUTS
# for line run records, store per line
# for normal node run records, store per node per line;
# for reduce node run records, store centralized in 000000000.jsonl per node
self.outputs_folder = self._prepare_folder(self.path / "flow_outputs")
self._outputs_path = self.outputs_folder / "output.jsonl" # dumped by executor
self._node_infos_folder = self._prepare_folder(self.path / "node_artifacts")
self._run_infos_folder = self._prepare_folder(self.path / "flow_artifacts")
self._data_path = Path(run.data) if run.data is not None else None
self._meta_path = self.path / LocalStorageFilenames.META
self._exception_path = self.path / LocalStorageFilenames.EXCEPTION
self._dump_meta_file()
self._eager_mode = self._calculate_eager_mode(run)
@property
def eager_mode(self) -> bool:
return self._eager_mode
@classmethod
def _calculate_eager_mode(cls, run: Run) -> bool:
if run._run_source == RunInfoSources.LOCAL:
try:
flow_obj = load_flow(source=run.flow)
return isinstance(flow_obj, EagerFlow)
except Exception as e:
# For run with incomplete flow snapshot, ignore load flow error to make sure it can still show.
logger.debug(f"Failed to load flow from {run.flow} due to {e}.")
return False
elif run._run_source in [RunInfoSources.INDEX_SERVICE, RunInfoSources.RUN_HISTORY]:
return run._properties.get("azureml.promptflow.run_mode") == "Eager"
# TODO(2901279): support eager mode for run created from run folder
return False
def delete(self) -> None:
def on_rmtree_error(func, path, exc_info):
raise InvalidRunError(f"Failed to delete run {self.path} due to {exc_info[1]}.")
shutil.rmtree(path=self.path, onerror=on_rmtree_error)
def _dump_meta_file(self) -> None:
json_dump({"batch_size": LOCAL_STORAGE_BATCH_SIZE}, self._meta_path)
def dump_snapshot(self, flow: Flow) -> None:
"""Dump flow directory to snapshot folder, input file will be dumped after the run."""
patterns = [pattern for pattern in PromptflowIgnoreFile.IGNORE_FILE]
# ignore current output parent folder to avoid potential recursive copy
patterns.append(self._run._output_path.parent.name)
shutil.copytree(
flow.code.as_posix(),
self._snapshot_folder_path,
ignore=shutil.ignore_patterns(*patterns),
dirs_exist_ok=True,
)
# replace DAG file with the overwrite one
if not self._eager_mode:
self._dag_path.unlink()
shutil.copy(flow.path, self._dag_path)
def load_dag_as_string(self) -> str:
if self._eager_mode:
return ""
with read_open(self._dag_path) as f:
return f.read()
def load_flow_tools_json(self) -> dict:
if self._eager_mode:
# no tools json for eager mode
return {}
if not self._flow_tools_json_path.is_file():
return generate_flow_tools_json(self._snapshot_folder_path, dump=False)
else:
return json_load(self._flow_tools_json_path)
def load_io_spec(self) -> Tuple[Dict[str, Dict[str, str]], Dict[str, Dict[str, str]]]:
"""Load input/output spec from DAG."""
# TODO(2898455): support eager mode
with read_open(self._dag_path) as f:
flow_dag = load_yaml(f)
return flow_dag["inputs"], flow_dag["outputs"]
def load_inputs(self) -> RunInputs:
df = pd_read_json(self._inputs_path)
return df.to_dict("list")
def load_outputs(self) -> RunOutputs:
# for legacy run, simply read the output file and return as list of dict
if not self._outputs_path.is_file():
df = pd_read_json(self._legacy_outputs_path)
return df.to_dict("list")
df = pd_read_json(self._outputs_path)
if len(df) > 0:
df = df.set_index(LINE_NUMBER)
return df.to_dict("list")
def dump_inputs_and_outputs(self) -> None:
inputs, outputs = self._collect_io_from_debug_info()
with write_open(self._sdk_inputs_path) as f:
inputs.to_json(f, orient="records", lines=True, force_ascii=False)
with write_open(self._sdk_output_path) as f:
outputs.to_json(f, orient="records", lines=True, force_ascii=False)
def dump_metrics(self, metrics: Optional[RunMetrics]) -> None:
metrics = metrics or dict()
json_dump(metrics, self._metrics_path)
def dump_exception(self, exception: Exception, batch_result: BatchResult) -> None:
"""Dump exception to local storage.
:param exception: Exception raised during bulk run.
:param batch_result: Bulk run outputs. If exception not raised, store line run error messages.
"""
# extract line run errors
errors = []
if batch_result:
for line_error in batch_result.error_summary.error_list:
errors.append(line_error.to_dict())
# collect aggregation node error
for node_name, aggr_error in batch_result.error_summary.aggr_error_dict.items():
errors.append({"error": aggr_error, "aggregation_node_name": node_name})
if errors:
try:
# use first line run error message as exception message if no exception raised
error = errors[0]
message = error["error"]["message"]
except Exception:
message = (
"Failed to extract error message from line runs. "
f"Please check {self._outputs_path} for more info."
)
elif exception and isinstance(exception, UserErrorException):
# SystemError will be raised above and users can see it, so we don't need to dump it.
message = str(exception)
else:
return
if not isinstance(exception, BulkRunException):
# If other errors raised, pass it into PromptflowException
exception = BulkRunException(
message=message,
error=exception,
failed_lines=batch_result.failed_lines if batch_result else "unknown",
total_lines=batch_result.total_lines if batch_result else "unknown",
errors={"errors": errors},
)
json_dump(PromptflowExceptionPresenter.create(exception).to_dict(include_debug_info=True), self._exception_path)
def load_exception(self) -> Dict:
try:
return json_load(self._exception_path)
except Exception:
return {}
def load_detail(self, parse_const_as_str: bool = False) -> Dict[str, list]:
if self._detail_path.is_file():
# legacy run with local file detail.json, then directly load from the file
return json_load(self._detail_path)
else:
# nan, inf and -inf are not JSON serializable
# according to https://docs.python.org/3/library/json.html#json.loads
# `parse_constant` will be called to handle these values
# so if parse_const_as_str is True, we will parse these values as str with a lambda function
json_loads = json.loads if not parse_const_as_str else partial(json.loads, parse_constant=lambda x: str(x))
# collect from local files and concat in the memory
flow_runs, node_runs = [], []
for line_run_record_file in sorted(self._run_infos_folder.iterdir()):
# In addition to the output jsonl files, there may be multimedia files in the output folder,
# so we should skip them.
if line_run_record_file.suffix.lower() != ".jsonl":
continue
with read_open(line_run_record_file) as f:
new_runs = [json_loads(line)["run_info"] for line in list(f)]
flow_runs += new_runs
for node_folder in sorted(self._node_infos_folder.iterdir()):
for node_run_record_file in sorted(node_folder.iterdir()):
if node_run_record_file.suffix.lower() != ".jsonl":
continue
with read_open(node_run_record_file) as f:
new_runs = [json_loads(line)["run_info"] for line in list(f)]
node_runs += new_runs
return {"flow_runs": flow_runs, "node_runs": node_runs}
def load_metrics(self) -> Dict[str, Union[int, float, str]]:
return json_load(self._metrics_path)
def persist_node_run(self, run_info: NodeRunInfo) -> None:
"""Persist node run record to local storage."""
node_folder = self._prepare_folder(self._node_infos_folder / run_info.node)
self._persist_run_multimedia(run_info, node_folder)
node_run_record = NodeRunRecord.from_run_info(run_info)
# for reduce nodes, the line_number is None, store the info in the 000000000.jsonl
# align with AzureMLRunStorageV2, which is a storage contract with PFS
line_number = 0 if node_run_record.line_number is None else node_run_record.line_number
filename = f"{str(line_number).zfill(self.LINE_NUMBER_WIDTH)}.jsonl"
node_run_record.dump(node_folder / filename, run_name=self._run.name)
def persist_flow_run(self, run_info: FlowRunInfo) -> None:
"""Persist line run record to local storage."""
if not Status.is_terminated(run_info.status):
logger.info("Line run is not terminated, skip persisting line run record.")
return
self._persist_run_multimedia(run_info, self._run_infos_folder)
line_run_record = LineRunRecord.from_flow_run_info(run_info)
# calculate filename according to the batch size
# note that if batch_size > 1, need to well handle concurrent write scenario
lower_bound = line_run_record.line_number // LOCAL_STORAGE_BATCH_SIZE * LOCAL_STORAGE_BATCH_SIZE
upper_bound = lower_bound + LOCAL_STORAGE_BATCH_SIZE - 1
filename = (
f"{str(lower_bound).zfill(self.LINE_NUMBER_WIDTH)}_"
f"{str(upper_bound).zfill(self.LINE_NUMBER_WIDTH)}.jsonl"
)
line_run_record.dump(self._run_infos_folder / filename)
def persist_result(self, result: Optional[BatchResult]) -> None:
"""Persist metrics from return of executor."""
if result is None:
return
self.dump_inputs_and_outputs()
self.dump_metrics(result.metrics)
def _persist_run_multimedia(self, run_info: Union[FlowRunInfo, NodeRunInfo], folder_path: Path):
if run_info.inputs:
run_info.inputs = self._serialize_multimedia(run_info.inputs, folder_path)
if run_info.output:
run_info.output = self._serialize_multimedia(run_info.output, folder_path)
run_info.result = None
if run_info.api_calls:
run_info.api_calls = self._serialize_multimedia(run_info.api_calls, folder_path)
def _serialize_multimedia(self, value, folder_path: Path, relative_path: Path = None):
pfbytes_file_reference_encoder = get_file_reference_encoder(folder_path, relative_path, use_absolute_path=True)
serialization_funcs = {Image: partial(Image.serialize, **{"encoder": pfbytes_file_reference_encoder})}
return serialize(value, serialization_funcs=serialization_funcs)
@staticmethod
def _prepare_folder(path: Union[str, Path]) -> Path:
path = Path(path)
path.mkdir(parents=True, exist_ok=True)
return path
@staticmethod
def _outputs_padding(df: "DataFrame", inputs_line_numbers: List[int]) -> "DataFrame":
import pandas as pd
if len(df) == len(inputs_line_numbers):
return df
missing_lines = []
lines_set = set(df[LINE_NUMBER].values)
for i in inputs_line_numbers:
if i not in lines_set:
missing_lines.append({LINE_NUMBER: i})
df_to_append = pd.DataFrame(missing_lines)
res = pd.concat([df, df_to_append], ignore_index=True)
res = res.sort_values(by=LINE_NUMBER, ascending=True)
return res
def load_inputs_and_outputs(self) -> Tuple["DataFrame", "DataFrame"]:
if not self._sdk_inputs_path.is_file() or not self._sdk_output_path.is_file():
inputs, outputs = self._collect_io_from_debug_info()
else:
inputs = pd_read_json(self._sdk_inputs_path)
outputs = pd_read_json(self._sdk_output_path)
# if all line runs are failed, no need to fill
if len(outputs) > 0:
outputs = self._outputs_padding(outputs, inputs[LINE_NUMBER].tolist())
outputs.fillna(value="(Failed)", inplace=True) # replace nan with explicit prompt
outputs = outputs.set_index(LINE_NUMBER)
return inputs, outputs
def _collect_io_from_debug_info(self) -> Tuple["DataFrame", "DataFrame"]:
import pandas as pd
inputs, outputs = [], []
for line_run_record_file in sorted(self._run_infos_folder.iterdir()):
if line_run_record_file.suffix.lower() != ".jsonl":
continue
with read_open(line_run_record_file) as f:
datas = [json.loads(line) for line in list(f)]
for data in datas:
line_number: int = data[LINE_NUMBER]
line_run_info: dict = data["run_info"]
current_inputs = line_run_info.get("inputs")
current_outputs = line_run_info.get("output")
inputs.append(copy.deepcopy(current_inputs))
if current_outputs is not None:
current_outputs[LINE_NUMBER] = line_number
outputs.append(copy.deepcopy(current_outputs))
return pd.DataFrame(inputs), pd.DataFrame(outputs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_sdk | promptflow_repo/promptflow/src/promptflow/promptflow/_sdk/operations/_tool_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import importlib.util
import inspect
import io
import json
import logging
import pkgutil
from dataclasses import asdict
from os import PathLike
from pathlib import Path
from types import ModuleType
from typing import Union
import jsonschema
from promptflow._core.tool_meta_generator import (
ToolValidationError,
_parse_tool_from_function,
asdict_without_none,
is_tool,
)
from promptflow._core.tools_manager import PACKAGE_TOOLS_ENTRY, collect_package_tools
from promptflow._sdk._constants import ICON, ICON_DARK, ICON_LIGHT, LOGGER_NAME, SKIP_FUNC_PARAMS, TOOL_SCHEMA
from promptflow._sdk._telemetry import ActivityType, monitor_operation
from promptflow._sdk.entities._validation import ValidationResult, ValidationResultBuilder
from promptflow._utils.multimedia_utils import convert_multimedia_data_to_base64
from promptflow.contracts.multimedia import Image
from promptflow.exceptions import UserErrorException
TOTAL_COUNT = "total_count"
INVALID_COUNT = "invalid_count"
logger = logging.getLogger(LOGGER_NAME)
class ToolOperations:
"""ToolOperations."""
def __init__(self):
self._tool_schema_dict = None
@property
def _tool_schema(self):
if not self._tool_schema_dict:
with open(TOOL_SCHEMA, "r") as f:
self._tool_schema_dict = json.load(f)
return self._tool_schema_dict
def _merge_validate_result(self, target, source):
target.merge_with(source)
target._set_extra_info(
TOTAL_COUNT,
target._get_extra_info(TOTAL_COUNT, 0) + source._get_extra_info(TOTAL_COUNT, 0),
)
target._set_extra_info(
INVALID_COUNT,
target._get_extra_info(INVALID_COUNT, 0) + source._get_extra_info(INVALID_COUNT, 0),
)
def _list_tools_in_package(self, package_name: str, raise_error: bool = False):
"""
List the meta of all tools in the package. Raise user error if raise_error=True and found incorrect tools.
:param package_name: Package name
:type package_name: str
:param raise_error: Whether to raise the error.
:type raise_error: bool
:return: Dict of tools meta
:rtype: Dict[str, Dict]
"""
package_tools, validate_result = self._list_tool_meta_in_package(package_name=package_name)
if not validate_result.passed:
if raise_error:
def tool_validate_error_func(msg, _):
return ToolValidationError(message=msg, validate_result=validate_result)
validate_result.try_raise(raise_error=raise_error, error_func=tool_validate_error_func)
else:
logger.warning(f"Found invalid tool(s):\n {repr(validate_result)}")
return package_tools
def _list_tool_meta_in_package(self, package_name: str):
"""
List the meta of all tools in the package.
:param package_name: Package name
:type package_name: str
:return: Dict of tools meta, validation result
:rtype: Dict[str, Dict], ValidationResult
"""
package_tools = {}
validate_result = ValidationResultBuilder.success()
try:
package = __import__(package_name)
module_list = pkgutil.walk_packages(package.__path__, prefix=package.__name__ + ".")
for module in module_list:
module_tools, module_validate_result = self._generate_tool_meta(importlib.import_module(module.name))
package_tools.update(module_tools)
self._merge_validate_result(validate_result, module_validate_result)
except ImportError as e:
raise UserErrorException(f"Cannot find the package {package_name}, {e}.")
return package_tools, validate_result
def _generate_tool_meta(self, tool_module):
"""
Generate tools meta in the module.
:param tool_module: The module needs to generate tools meta
:type tool_module: object
:return: Dict of tools meta, validation result
:rtype: Dict[str, Dict], ValidationResult
"""
tool_functions = self._collect_tool_functions_in_module(tool_module)
tool_methods = self._collect_tool_class_methods_in_module(tool_module)
construct_tools = {}
invalid_tool_count = 0
tool_validate_result = ValidationResultBuilder.success()
for f in tool_functions:
tool, input_settings, extra_info = self._parse_tool_from_func(f)
construct_tool, validate_result = self._serialize_tool(tool, input_settings, extra_info, f)
if validate_result.passed:
tool_name = self._get_tool_name(tool)
construct_tools[tool_name] = construct_tool
else:
invalid_tool_count = invalid_tool_count + 1
tool_validate_result.merge_with(validate_result)
for (f, initialize_inputs) in tool_methods:
tool, input_settings, extra_info = self._parse_tool_from_func(f, initialize_inputs)
construct_tool, validate_result = self._serialize_tool(tool, input_settings, extra_info, f)
if validate_result.passed:
tool_name = self._get_tool_name(tool)
construct_tools[tool_name] = construct_tool
else:
invalid_tool_count = invalid_tool_count + 1
tool_validate_result.merge_with(validate_result)
# The generated dict cannot be dumped as yaml directly since yaml cannot handle string enum.
tools = json.loads(json.dumps(construct_tools))
tool_validate_result._set_extra_info(TOTAL_COUNT, len(tool_functions) + len(tool_methods))
tool_validate_result._set_extra_info(INVALID_COUNT, invalid_tool_count)
return tools, tool_validate_result
@staticmethod
def _collect_tool_functions_in_module(tool_module):
tools = []
for _, obj in inspect.getmembers(tool_module):
if is_tool(obj):
# Note that the tool should be in defined in exec but not imported in exec,
# so it should also have the same module with the current function.
if getattr(obj, "__module__", "") != tool_module.__name__:
continue
tools.append(obj)
return tools
@staticmethod
def _collect_tool_class_methods_in_module(tool_module):
from promptflow._core.tool import ToolProvider
tools = []
for _, obj in inspect.getmembers(tool_module):
if isinstance(obj, type) and issubclass(obj, ToolProvider) and obj.__module__ == tool_module.__name__:
for _, method in inspect.getmembers(obj):
if is_tool(method):
initialize_inputs = obj.get_initialize_inputs()
tools.append((method, initialize_inputs))
return tools
def _get_tool_name(self, tool):
tool_name = (
f"{tool.module}.{tool.class_name}.{tool.function}"
if tool.class_name is not None
else f"{tool.module}.{tool.function}"
)
return tool_name
def _parse_tool_from_func(self, tool_func, initialize_inputs=None):
"""
Parse tool from tool function
:param tool_func: The tool function
:type tool_func: callable
:param initialize_inputs: Initialize inputs of tool
:type initialize_inputs: Dict[str, obj]
:return: tool object, tool input settings, extra info about the tool
:rtype: Tool, Dict[str, InputSetting], Dict[str, obj]
"""
tool = _parse_tool_from_function(
tool_func, initialize_inputs=initialize_inputs, gen_custom_type_conn=True, skip_prompt_template=True
)
extra_info = getattr(tool_func, "__extra_info")
input_settings = getattr(tool_func, "__input_settings")
return tool, input_settings, extra_info
def _validate_tool_function(self, tool, input_settings, extra_info, func_name=None, func_path=None):
"""
Check whether the icon and input settings of the tool are legitimate.
:param tool: The tool object
:type tool: Tool
:param input_settings: Input settings of the tool
:type input_settings: Dict[str, InputSetting]
:param extra_info: Extra info about the tool
:type extra_info: Dict[str, obj]
:param func_name: Function name of the tool
:type func_name: str
:param func_path: Script path of the tool
:type func_path: str
:return: Validation result of the tool
:rtype: ValidationResult
"""
validate_result = ValidationResultBuilder.success()
if extra_info:
if ICON in extra_info:
if ICON_LIGHT in extra_info or ICON_DARK in extra_info:
validate_result.append_error(
yaml_path=None,
message=f"Cannot provide both `icon` and `{ICON_LIGHT}` or `{ICON_DARK}`.",
function_name=func_name,
location=func_path,
key="function_name",
)
if input_settings:
input_settings_validate_result = self._validate_input_settings(
tool.inputs, input_settings, func_name, func_path
)
validate_result.merge_with(input_settings_validate_result)
return validate_result
def _validate_tool_schema(self, tool_dict, func_name=None, func_path=None):
"""
Check whether the generated schema of the tool are legitimate.
:param tool_dict: The generated tool dict
:type tool_dict: Dict[str, obj]
:param func_name: Function name of the tool
:type func_name: str
:param func_path: Script path of the tool
:type func_path: str
:return: Validation result of the tool
:rtype: ValidationResult
"""
validate_result = ValidationResultBuilder.success()
try:
jsonschema.validate(instance=tool_dict, schema=self._tool_schema)
except jsonschema.exceptions.ValidationError as e:
validate_result.append_error(
message=str(e), yaml_path=None, function_name=func_name, location=func_path, key="function_name"
)
return validate_result
def _validate_input_settings(self, tool_inputs, input_settings, func_name=None, func_path=None):
"""
Check whether input settings of the tool are legitimate.
:param tool_inputs: Tool inputs
:type tool_inputs: Dict[str, obj]
:param input_settings: Input settings of the tool
:type input_settings: Dict[str, InputSetting]
:param extra_info: Extra info about the tool
:type extra_info: Dict[str, obj]
:param func_name: Function name of the tool
:type func_name: str
:param func_path: Script path of the tool
:type func_path: str
:return: Validation result of the tool
:rtype: ValidationResult
"""
validate_result = ValidationResultBuilder.success()
for input_name, settings in input_settings.items():
if input_name not in tool_inputs:
validate_result.append_error(
yaml_path=None,
message=f"Cannot find {input_name} in tool inputs.",
function_name=func_name,
location=func_path,
key="function_name",
)
if settings.enabled_by and settings.enabled_by not in tool_inputs:
validate_result.append_error(
yaml_path=None,
message=f'Cannot find the input "{settings.enabled_by}" for the enabled_by of {input_name}.',
function_name=func_name,
location=func_path,
key="function_name",
)
if settings.dynamic_list:
dynamic_func_inputs = inspect.signature(settings.dynamic_list._func_obj).parameters
has_kwargs = any([param.kind == param.VAR_KEYWORD for param in dynamic_func_inputs.values()])
required_inputs = [
k
for k, v in dynamic_func_inputs.items()
if v.default is inspect.Parameter.empty and v.kind != v.VAR_KEYWORD and k not in SKIP_FUNC_PARAMS
]
if settings.dynamic_list._input_mapping:
# Validate input mapping in dynamic_list
for func_input, reference_input in settings.dynamic_list._input_mapping.items():
# Check invalid input name of dynamic list function
if not has_kwargs and func_input not in dynamic_func_inputs:
validate_result.append_error(
yaml_path=None,
message=f"Cannot find {func_input} in the inputs of "
f"dynamic_list func {settings.dynamic_list.func_path}",
function_name=func_name,
location=func_path,
key="function_name",
)
# Check invalid input name of tool
if reference_input not in tool_inputs:
validate_result.append_error(
yaml_path=None,
message=f"Cannot find {reference_input} in the tool inputs.",
function_name=func_name,
location=func_path,
key="function_name",
)
if func_input in required_inputs:
required_inputs.remove(func_input)
# Check required input of dynamic_list function
if len(required_inputs) != 0:
validate_result.append_error(
yaml_path=None,
message=f"Missing required input(s) of dynamic_list function: {required_inputs}",
function_name=func_name,
location=func_path,
key="function_name",
)
return validate_result
def _serialize_tool(self, tool, input_settings, extra_info, tool_func):
"""
Serialize tool obj to dict.
:param tool_func: Package tool function
:type tool_func: callable
:param initialize_inputs: Initialize inputs of package tool
:type initialize_inputs: Dict[str, obj]
:return: package tool name, serialized tool
:rtype: str, Dict[str, str]
"""
tool_func_name = tool_func.__name__
tool_script_path = inspect.getsourcefile(getattr(tool_func, "__original_function", tool_func))
validate_result = self._validate_tool_function(
tool, input_settings, extra_info, tool_func_name, tool_script_path
)
if validate_result.passed:
construct_tool = asdict(tool, dict_factory=lambda x: {k: v for (k, v) in x if v})
if extra_info:
if ICON in extra_info:
extra_info[ICON] = self._serialize_icon_data(extra_info["icon"])
if ICON_LIGHT in extra_info:
icon = extra_info.get("icon", {})
icon["light"] = self._serialize_icon_data(extra_info[ICON_LIGHT])
extra_info[ICON] = icon
if ICON_DARK in extra_info:
icon = extra_info.get("icon", {})
icon["dark"] = self._serialize_icon_data(extra_info[ICON_DARK])
extra_info[ICON] = icon
construct_tool.update(extra_info)
# Update tool input settings
if input_settings:
tool_inputs = construct_tool.get("inputs", {})
generated_by_inputs = {}
for input_name, settings in input_settings.items():
tool_inputs[input_name].update(asdict_without_none(settings))
if settings.generated_by:
generated_by_inputs.update(settings.generated_by._input_settings)
tool_inputs.update(generated_by_inputs)
schema_validate_result = self._validate_tool_schema(construct_tool, tool_func_name, tool_script_path)
validate_result.merge_with(schema_validate_result)
return construct_tool, validate_result
else:
return {}, validate_result
def _serialize_icon_data(self, icon):
if not Path(icon).exists():
raise UserErrorException(f"Cannot find the icon path {icon}.")
return self._serialize_image_data(icon)
@staticmethod
def _serialize_image_data(image_path):
"""Serialize image to base64."""
from PIL import Image as PIL_Image
with open(image_path, "rb") as image_file:
# Create a BytesIO object from the image file
image_data = io.BytesIO(image_file.read())
# Open the image and resize it
img = PIL_Image.open(image_data)
if img.size != (16, 16):
img = img.resize((16, 16), PIL_Image.Resampling.LANCZOS)
buffered = io.BytesIO()
img.save(buffered, format="PNG")
icon_image = Image(buffered.getvalue(), mime_type="image/png")
image_url = convert_multimedia_data_to_base64(icon_image, with_type=True)
return image_url
@staticmethod
def _is_package_tool(package) -> bool:
import pkg_resources
try:
distribution = pkg_resources.get_distribution(package.__name__)
entry_points = distribution.get_entry_map()
return PACKAGE_TOOLS_ENTRY in entry_points
except Exception as e:
logger.debug(f"Failed to check {package.__name__} is a package tool, raise {e}")
return False
@monitor_operation(activity_name="pf.tools.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
flow: Union[str, PathLike] = None,
):
"""
List all package tools in the environment and code tools in the flow.
:param flow: path to the flow directory
:type flow: Union[str, PathLike]
:return: Dict of package tools and code tools info.
:rtype: Dict[str, Dict]
"""
from promptflow._sdk._pf_client import PFClient
local_client = PFClient()
package_tools = collect_package_tools()
if flow:
tools, _ = local_client.flows._generate_tools_meta(flow)
else:
tools = {"package": {}, "code": {}}
tools["package"].update(package_tools)
return tools
@monitor_operation(activity_name="pf.tools.validate", activity_type=ActivityType.PUBLICAPI)
def validate(
self, source: Union[str, callable, PathLike], *, raise_error: bool = False, **kwargs
) -> ValidationResult:
"""
Validate tool.
:param source: path to the package tool directory or tool script
:type source: Union[str, callable, PathLike]
:param raise_error: whether raise error when validation failed
:type raise_error: bool
:return: a validation result object
:rtype: ValidationResult
"""
def validate_tool_function(tool_func, init_inputs=None):
tool, input_settings, extra_info = self._parse_tool_from_func(tool_func, init_inputs)
_, validate_result = self._serialize_tool(tool, input_settings, extra_info, source)
validate_result._set_extra_info(TOTAL_COUNT, 1)
validate_result._set_extra_info(INVALID_COUNT, 0 if validate_result.passed else 1)
return validate_result
if callable(source):
from promptflow._core.tool import ToolProvider
if isinstance(source, type) and issubclass(source, ToolProvider):
# Validate tool class
validate_result = ValidationResultBuilder.success()
for _, method in inspect.getmembers(source):
if is_tool(method):
initialize_inputs = source.get_initialize_inputs()
func_validate_result = validate_tool_function(method, initialize_inputs)
self._merge_validate_result(validate_result, func_validate_result)
else:
# Validate tool function
validate_result = validate_tool_function(source)
elif isinstance(source, (str, PathLike)):
# Validate tool script
if not Path(source).exists():
raise UserErrorException(f"Cannot find the tool script {source}")
# Load the module from the file path
module_name = Path(source).stem
spec = importlib.util.spec_from_file_location(module_name, source)
module = importlib.util.module_from_spec(spec)
# Load the module's code
spec.loader.exec_module(module)
_, validate_result = self._generate_tool_meta(module)
elif isinstance(source, ModuleType):
# Validate package tool
if not self._is_package_tool(source):
raise UserErrorException("Invalid package tool.")
_, validate_result = self._list_tool_meta_in_package(package_name=source.__name__)
else:
raise UserErrorException(
"Provide invalid source, tool validation source supports script tool, "
"package tool and tool script path."
)
def tool_validate_error_func(msg, _):
return ToolValidationError(message=msg)
validate_result.try_raise(raise_error=raise_error, error_func=tool_validate_error_func)
return validate_result
| 0 |
Subsets and Splits