Source code for galaxy.tools.parameters.wrapped

from collections import UserDict
from typing import (
    Any,
    Dict,
    List,
    Sequence,
    Union,
)

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,
)
from galaxy.util.permutations import (
    looks_like_flattened_repeat_key,
    split_flattened_repeat_key,
)

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 looks_like_flattened_repeat_key(key_parts[0]):
        # Repeat
        input_name, index = split_flattened_repeat_key(key_parts[0])
        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 nested_key_to_path(key: str) -> Sequence[Union[str, int]]:
    """
    Convert a tool state key that is separated with '|' and '_n' into path iterable.
    E.g. "cond|repeat_0|paramA" -> ["cond", "repeat", 0, "paramA"].
    Return value can be used with `boltons.iterutils.get_path`.
    """
    path: List[Union[str, int]] = []
    key_parts = key.split("|")
    if len(key_parts) == 1:
        return key_parts
    for key_part in key_parts:
        if "_" in key_part:
            input_name, _index = key_part.rsplit("_", 1)
            if _index.isdigit():
                path.extend((input_name, int(_index)))
                continue
        path.append(key_part)
    return path


[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",
    "nested_key_to_path",
)