from collections import UserDict
from typing import (
Any,
Dict,
List,
)
from galaxy.tools.parameters.basic import (
DataCollectionToolParameter,
DataToolParameter,
SelectToolParameter,
)
from galaxy.tools.parameters.grouping import (
Conditional,
Repeat,
Section,
)
from galaxy.tools.wrappers import (
DatasetCollectionWrapper,
DatasetFilenameWrapper,
DatasetListWrapper,
ElementIdentifierMapper,
InputValueWrapper,
SelectToolParameterWrapper,
)
PARAMS_UNWRAPPED = object()
[docs]class LegacyUnprefixedDict(UserDict):
"""Track and provide access to prefixed and unprefixed tool parameter values."""
# It used to be valid to access members of conditionals without specifying the conditional.
# This dict provides a fallback when dict lookup fails using those old rules
[docs] def __init__(self, dict=None, **kwargs):
self._legacy_mapping: Dict[str, str] = {}
super().__init__(dict, **kwargs)
[docs] def set_legacy_alias(self, new_key: str, old_key: str):
self._legacy_mapping[old_key] = new_key
def __getitem__(self, key):
if key not in self.data and key in self._legacy_mapping:
return super().__getitem__(self._legacy_mapping[key])
return super().__getitem__(key)
def __contains__(self, key: object) -> bool:
if super().__contains__(key):
return True
return key in self._legacy_mapping
def copy_identifiers(source, destination):
if isinstance(source, dict):
for k, v in source.items():
if k.endswith("|__identifier__"):
if isinstance(destination, dict):
destination[k] = v
[docs]class WrappedParameters:
[docs] def __init__(self, trans, tool, incoming, input_datasets=None):
self.trans = trans
self.tool = tool
self.incoming = incoming
self._params = PARAMS_UNWRAPPED
self._input_datasets = input_datasets
@property
def params(self):
if self._params is PARAMS_UNWRAPPED:
params = make_dict_copy(self.incoming)
self.wrap_values(self.tool.inputs, params, skip_missing_values=not self.tool.check_values)
self._params = params
return self._params
[docs] def wrap_values(self, inputs, input_values, skip_missing_values=False):
trans = self.trans
tool = self.tool
incoming = self.incoming
element_identifier_mapper = ElementIdentifierMapper(self._input_datasets)
# Wrap tool inputs as necessary
for input in inputs.values():
if input.name not in input_values and skip_missing_values:
continue
value = input_values[input.name]
copy_identifiers(destination=value, source=input_values)
if isinstance(input, Repeat):
for d in value:
copy_identifiers(destination=d, source=value)
self.wrap_values(input.inputs, d, skip_missing_values=skip_missing_values)
elif isinstance(input, Conditional):
values = value
current = values["__current_case__"]
self.wrap_values(input.cases[current].inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, Section):
values = value
self.wrap_values(input.inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = DatasetListWrapper(
None,
dataset_instances,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
formats=input.formats,
)
elif isinstance(input, DataToolParameter):
wrapper_kwds = dict(
datatypes_registry=trans.app.datatypes_registry, tool=tool, name=input.name, formats=input.formats
)
element_identifier = element_identifier_mapper.identifier(value, input_values)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = DatasetFilenameWrapper(value, **wrapper_kwds)
elif isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(input, value, other_values=incoming)
elif isinstance(input, DataCollectionToolParameter):
input_values[input.name] = DatasetCollectionWrapper(
None,
value,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
)
else:
input_values[input.name] = InputValueWrapper(input, value, incoming, tool.profile)
[docs]def make_dict_copy(from_dict):
"""
Makes a copy of input dictionary from_dict such that all values that are dictionaries
result in creation of a new dictionary ( a sort of deepcopy ). We may need to handle
other complex types ( e.g., lists, etc ), but not sure...
Yes, we need to handle lists (and now are)...
"""
copy_from_dict = {}
for key, value in from_dict.items():
if type(value).__name__ == "dict":
copy_from_dict[key] = make_dict_copy(value)
elif isinstance(value, list):
copy_from_dict[key] = make_list_copy(value)
else:
copy_from_dict[key] = value
return copy_from_dict
def make_list_copy(from_list):
new_list = []
for value in from_list:
if isinstance(value, dict):
new_list.append(make_dict_copy(value))
elif isinstance(value, list):
new_list.append(make_list_copy(value))
else:
new_list.append(value)
return new_list
[docs]def process_key(incoming_key: str, incoming_value: Any, d: Dict[str, Any]):
key_parts = incoming_key.split("|")
if len(key_parts) == 1:
# Regular parameter
if incoming_key in d and not incoming_value:
# In case we get an empty repeat after we already filled in a repeat element
return
d[incoming_key] = incoming_value
elif key_parts[0].rsplit("_", 1)[-1].isdigit():
# Repeat
input_name, _index = key_parts[0].rsplit("_", 1)
index = int(_index)
d.setdefault(input_name, [])
newlist: List[Dict[Any, Any]] = [{} for _ in range(index + 1)]
d[input_name].extend(newlist[len(d[input_name]) :])
subdict = d[input_name][index]
process_key("|".join(key_parts[1:]), incoming_value=incoming_value, d=subdict)
else:
# Section / Conditional
input_name = key_parts[0]
subdict = d.get(input_name, {})
d[input_name] = subdict
process_key("|".join(key_parts[1:]), incoming_value=incoming_value, d=subdict)
[docs]def flat_to_nested_state(incoming: Dict[str, Any]):
nested_state: Dict[str, Any] = {}
for key, value in incoming.items():
process_key(key, value, nested_state)
return nested_state
__all__ = ("LegacyUnprefixedDict", "WrappedParameters", "make_dict_copy", "process_key", "flat_to_nested_state")