async def test_run(self):
passwords = [str(random.random()) for _ in range(0, 20)]
# Orchestrate the running of these operations
async with MemoryOrchestrator.basic_config(*OPIMPS) as orchestrator:
definitions = Operation.definitions(*OPERATIONS)
passwords = [
Input(value=password,
definition=definitions['UnhashedPassword'],
parents=None) for password in passwords
]
output_spec = Input(value=['ScryptPassword'],
definition=definitions['get_single_spec'],
parents=None)
async with orchestrator() as octx:
# Add our inputs to the input network with the context being the URL
for password in passwords:
await octx.ictx.add(
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext(password.value),
inputs=[password, output_spec])))
try:
async for _ctx, results in octx.run_operations(
strict=True):
self.assertTrue(results)
except AttributeError as error:
if "module 'hashlib' has no attribute 'scrypt'" \
in str(error):
return
raise
async def multicomm_dataflow(self, config, request):
# Seed the network with inputs given by caller
# TODO(p0,security) allowlist of valid definitions to seed (set
# Input.origin to something other than seed)
inputs = []
# If data was sent add those inputs
if request.method == "POST":
# Accept a list of input data
# TODO validate that input data is dict of list of inputs each item
# has definition and value properties
for ctx, client_inputs in (await request.json()).items():
for input_data in client_inputs:
if (
not input_data["definition"]
in config.dataflow.definitions
):
return web.json_response(
{
"error": f"Missing definition for {input_data['definition']} in dataflow"
},
status=HTTPStatus.NOT_FOUND,
)
inputs.append(
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext(ctx),
inputs=[
Input(
value=input_data["value"],
definition=config.dataflow.definitions[
input_data["definition"]
],
)
for input_data in client_inputs
],
)
)
)
# Run the operation in an orchestrator
# TODO(dfass) Create the orchestrator on startup of the HTTP API itself
async with MemoryOrchestrator.basic_config() as orchestrator:
# TODO(dfass) Create octx on dataflow registration
async with orchestrator(config.dataflow) as octx:
results = {
str(ctx): result async for ctx, result in octx.run(*inputs)
}
# TODO Implement input and presentation stages?
"""
if config.presentation == "blob":
return web.Response(body=results)
elif config.presentation == "text":
return web.Response(text=results)
else:
"""
return web.json_response(results)
async def test_run(self):
calc_strings_check = {"add 40 and 2": 42, "multiply 42 and 10": 420}
# TODO(p0) Implement and test asyncgenerator
callstyles_no_expand = [
"asyncgenerator",
"dict",
"dict_custom_input_set_context",
]
callstyles = {
"dict": {
to_calc: [
Input(value=to_calc,
definition=parse_line.op.inputs["line"]),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
]
for to_calc in calc_strings_check.keys()
},
"dict_custom_input_set_context": {
CustomInputSetContext(to_calc): [
Input(value=to_calc,
definition=parse_line.op.inputs["line"]),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
]
for to_calc in calc_strings_check.keys()
},
"list_input_sets": [
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext(to_calc),
inputs=[
Input(
value=to_calc,
definition=parse_line.op.inputs["line"],
),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
],
)) for to_calc in calc_strings_check.keys()
],
"uctx": [[
Input(value=to_calc, definition=parse_line.op.inputs["line"]),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
] for to_calc in calc_strings_check.keys()],
}
async with self.create_octx() as octx:
for callstyle, inputs in callstyles.items():
with self.subTest(callstyle=callstyle):
if callstyle in callstyles_no_expand:
run_coro = self.run_dataflow(octx, inputs)
else:
run_coro = self.run_dataflow(octx, *inputs)
async for ctx, results in run_coro:
ctx_str = (await ctx.handle()).as_string()
if callstyle == "uctx":
self.assertIn(
results[add.op.outputs["sum"].name],
dict(
zip(
calc_strings_check.values(),
calc_strings_check.keys(),
)),
)
else:
if callstyle == "dict_custom_input_set_context":
self.assertTrue(
isinstance(ctx, CustomInputSetContext))
self.assertEqual(
calc_strings_check[ctx_str],
results[add.op.outputs["sum"].name],
)
async def _multicomm_dataflow(self, config, request):
# Seed the network with inputs given by caller
# TODO(p0,security) allowlist of valid definitions to seed (set
# Input.origin to something other than seed)
inputs = []
# If data was sent add those inputs
if request.method == "POST":
# Accept a list of input data according to config.input_mode
if config.input_mode == "default":
# TODO validate that input data is dict of list of inputs each item
# has definition and value properties
for ctx, client_inputs in (await request.json()).items():
for input_data in client_inputs:
if (not input_data["definition"]
in config.dataflow.definitions):
return web.json_response(
{
"error":
f"Missing definition for {input_data['definition']} in dataflow"
},
status=HTTPStatus.NOT_FOUND,
)
inputs.append(
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext(ctx),
inputs=[
Input(
value=input_data["value"],
definition=config.dataflow.definitions[
input_data["definition"]],
) for input_data in client_inputs
] + ([
Input(
value=request.headers,
definition=config.dataflow.definitions[
config.forward_headers],
)
] if config.forward_headers else []),
)))
elif ":" in config.input_mode:
preprocess_mode, *input_def = config.input_mode.split(":")
input_def = ":".join(input_def)
if input_def not in config.dataflow.definitions:
return web.json_response(
{
"error":
f"Missing definition for {input_def} in dataflow"
},
status=HTTPStatus.NOT_FOUND,
)
if preprocess_mode == "json":
value = await request.json()
elif preprocess_mode == "text":
value = await request.text()
elif preprocess_mode == "bytes":
value = await request.read()
elif preprocess_mode == "stream":
value = request.content
else:
return web.json_response(
{
"error":
f"preprocess tag must be one of {self.IO_MODES}, got {preprocess_mode}"
},
status=HTTPStatus.NOT_FOUND,
)
inputs.append(
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext("post_input"),
inputs=[
Input(
value=value,
definition=config.dataflow.
definitions[input_def],
)
] + ([
Input(
value=request.headers,
definition=config.dataflow.definitions[
config.forward_headers],
)
] if config.forward_headers else []),
)))
else:
raise NotImplementedError(
"Input modes other than default,preprocess:definition_name not yet implemented"
)
# Run the operation in an orchestrator
# TODO(dfass) Create the orchestrator on startup of the HTTP API itself
async with MemoryOrchestrator() as orchestrator:
# TODO(dfass) Create octx on dataflow registration
async with orchestrator(config.dataflow) as octx:
results = {
str(ctx): result
async for ctx, result in octx.run(*inputs)
}
if config.output_mode == "json":
return web.json_response(results)
# content_info is a List[str] ([content_type,output_keys])
# in case of stream,bytes and string in others
postprocess_mode, *content_info = config.output_mode.split(":")
if postprocess_mode == "stream":
# stream:text/plain:get_single.beef
raise NotImplementedError(
"output mode not yet implemented")
elif postprocess_mode == "bytes":
content_type, output_keys = content_info
output_data = traverse_get(results, output_keys)
return web.Response(body=output_data)
elif postprocess_mode == "text":
output_data = traverse_get(results, content_info[0])
return web.Response(text=output_data)
else:
return web.json_response(
{"error": f"output mode not valid"},
status=HTTPStatus.NOT_FOUND,
)
async def test_run(self):
calc_strings_check = {"add 40 and 2": 42, "multiply 42 and 10": 420}
dataflow = DataFlow.auto(*OPIMPS)
# TODO(p0) Implement and test asyncgenerator
callstyles_no_expand = ["asyncgenerator", "dict"]
callstyles = {
"dict": {
to_calc: [
Input(value=to_calc,
definition=parse_line.op.inputs["line"]),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
]
for to_calc in calc_strings_check.keys()
},
"list_input_sets": [
MemoryInputSet(
MemoryInputSetConfig(
ctx=StringInputSetContext(to_calc),
inputs=[
Input(
value=to_calc,
definition=parse_line.op.inputs["line"],
),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
],
)) for to_calc in calc_strings_check.keys()
],
"uctx": [[
Input(value=to_calc, definition=parse_line.op.inputs["line"]),
Input(
value=[add.op.outputs["sum"].name],
definition=GetSingle.op.inputs["spec"],
),
] for to_calc in calc_strings_check.keys()],
}
async with MemoryOrchestrator.withconfig({}) as orchestrator:
async with orchestrator(dataflow) as octx:
for callstyle, inputs in callstyles.items():
with self.subTest(callstyle=callstyle):
if callstyle in callstyles_no_expand:
run_coro = octx.run(inputs)
else:
run_coro = octx.run(*inputs)
async for ctx, results in run_coro:
ctx_str = (await ctx.handle()).as_string()
if callstyle == "uctx":
self.assertIn(
results[add.op.outputs["sum"].name],
dict(
zip(
calc_strings_check.values(),
calc_strings_check.keys(),
)),
)
else:
self.assertEqual(
calc_strings_check[ctx_str],
results[add.op.outputs["sum"].name],
)