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Source code for galaxy.tools.wrappers

import abc
import logging
import os
import shlex
import tempfile
from functools import total_ordering
from typing import (
    Any,
    cast,
    Dict,
    Iterable,
    Iterator,
    KeysView,
    List,
    Optional,
    Sequence,
    Tuple,
    TYPE_CHECKING,
    Union,
)

from galaxy import exceptions
from galaxy.model import (
    DatasetCollection,
    DatasetCollectionElement,
    DatasetCollectionInstance,
    DatasetInstance,
    HasTags,
    HistoryDatasetCollectionAssociation,
)
from galaxy.model.metadata import FileParameter
from galaxy.model.none_like import NoneDataset
from galaxy.security.object_wrapper import wrap_with_safe_string
from galaxy.tools.parameters.basic import BooleanToolParameter
from galaxy.tools.parameters.wrapped_json import (
    data_collection_input_to_staging_path_and_source_path,
    data_input_to_staging_path_and_source_path,
)
from galaxy.util import (
    filesystem_safe_string,
    string_as_bool,
)

if TYPE_CHECKING:
    from galaxy.datatypes.registry import Registry
    from galaxy.job_execution.compute_environment import ComputeEnvironment
    from galaxy.model.metadata import MetadataCollection
    from galaxy.tools import Tool
    from galaxy.tools.parameters.basic import (
        BaseDataToolParameter,
        SelectToolParameter,
        ToolParameter,
    )

log = logging.getLogger(__name__)

# Fields in tool config files corresponding to paths (e.g .loc or .len)
# must have one of the following field names, and all such fields are
# assumed to be paths. This is to allow remote ComputeEnvironments (such
# as one used by Pulsar) to determine what values to rewrite or transfer.
PATH_ATTRIBUTES = ["len_path", "path"]


[docs]class ToolParameterValueWrapper: """ Base class for object that Wraps a Tool Parameter and Value. """ value: Union[str, List[str]] input: "ToolParameter" def __bool__(self) -> bool: return bool(self.value) __nonzero__ = __bool__
[docs] def get_display_text(self, quote: bool = True) -> str: """ Returns a string containing the value that would be displayed to the user in the tool interface. When quote is True (default), the string is escaped for e.g. command-line usage. """ rval = self.input.value_to_display_text(self.value) or "" if quote: return shlex.quote(rval) return rval
[docs]class RawObjectWrapper(ToolParameterValueWrapper): """ Wraps an object so that __str__ returns module_name:class_name. """
[docs] def __init__(self, obj: Any): self.obj = obj
def __bool__(self) -> bool: return bool( self.obj ) # FIXME: would it be safe/backwards compatible to rename .obj to .value, so that we can just inherit this method? __nonzero__ = __bool__ def __str__(self) -> str: try: return f"{self.obj.__module__}:{self.obj.__class__.__name__}" except Exception: # Most likely None, which lacks __module__. return str(self.obj) def __getattr__(self, key: Any) -> Any: return getattr(self.obj, key)
[docs]@total_ordering class InputValueWrapper(ToolParameterValueWrapper): """ Wraps an input so that __str__ gives the "param_dict" representation. """
[docs] def __init__( self, input: "ToolParameter", value: str, other_values: Optional[Dict[str, str]] = None, ) -> None: self.input = input self.value = value self._other_values: Dict[str, str] = other_values or {}
def _get_cast_values(self, other: Any) -> Tuple[Union[str, int, float, bool, None], Any]: if isinstance(self.input, BooleanToolParameter) and isinstance(other, str): if other in (self.input.truevalue, self.input.falsevalue): return str(self), other else: return bool(self), string_as_bool(other) # For backward compatibility, allow `$wrapper != ""` for optional non-text param if self.input.optional and self.value is None: if isinstance(other, str): return str(self), other else: return None, other cast_table = { "text": str, "integer": int, "float": float, "boolean": bool, } return cast(Union[str, int, float, bool], cast_table.get(self.input.type, str)(self)), other def __eq__(self, other: Any) -> bool: casted_self, casted_other = self._get_cast_values(other) return casted_self == casted_other def __ne__(self, other: Any) -> bool: return not self == other def __str__(self) -> str: to_param_dict_string = self.input.to_param_dict_string(self.value, self._other_values) if isinstance(to_param_dict_string, list): return ",".join(to_param_dict_string) else: return to_param_dict_string def __iter__(self) -> Iterable[str]: to_param_dict_string = self.input.to_param_dict_string(self.value, self._other_values) if not isinstance(to_param_dict_string, list): return iter([to_param_dict_string]) else: return iter(to_param_dict_string) def __getattr__(self, key: Any) -> Any: return getattr(self.value, key) def __gt__(self, other: Any) -> bool: casted_self, casted_other = self._get_cast_values(other) return casted_self > casted_other def __int__(self) -> int: return int(float(self)) def __float__(self) -> float: return float(str(self))
[docs]class SelectToolParameterWrapper(ToolParameterValueWrapper): """ Wraps a SelectTooParameter so that __str__ returns the selected value, but all other attributes are accessible. """ input: "SelectToolParameter"
[docs] class SelectToolParameterFieldWrapper: """ Provide access to any field by name or index for this particular value. Only applicable for dynamic_options selects, which have more than simple 'options' defined (name, value, selected). """
[docs] def __init__( self, input: "SelectToolParameter", value: Union[str, List[str]], other_values: Optional[Dict[str, str]], compute_environment: Optional["ComputeEnvironment"], ) -> None: self._input = input self._value = value self._other_values = other_values self._fields: Dict[str, str] = {} self._compute_environment = compute_environment
def __getattr__(self, name: str) -> Any: if name not in self._fields: self._fields[name] = self._input.options.get_field_by_name_for_value( name, self._value, None, self._other_values ) values = map(str, self._fields[name]) if name in PATH_ATTRIBUTES and self._compute_environment: # If we infer this is a path, rewrite it if needed. new_values = [] for value in values: rewrite_value = self._compute_environment.unstructured_path_rewrite(value) if rewrite_value: new_values.append(rewrite_value) else: new_values.append(value) return self._input.separator.join(new_values) return self._input.separator.join(values)
[docs] def __init__( self, input: "SelectToolParameter", value: Union[str, List[str]], other_values: Optional[Dict[str, str]] = None, compute_environment: Optional["ComputeEnvironment"] = None, ): self.input = input self.value = value self.input.value_label = input.value_to_display_text(value) self._other_values = other_values or {} self.compute_environment = compute_environment self.fields = self.SelectToolParameterFieldWrapper(input, value, other_values, self.compute_environment)
def __eq__(self, other: Any) -> bool: if isinstance(other, str): if other == "" and self.value in (None, []): # Allow $wrapper == '' for select (self.value is None) and multiple select (self.value is []) params return True return str(self) == other else: return super().__eq__(other) def __ne__(self, other: Any) -> bool: return not self == other def __str__(self) -> str: # Assuming value is never a path - otherwise would need to pass # along following argument value_map=self._path_rewriter. return str(self.input.to_param_dict_string(self.value, other_values=self._other_values)) def __add__(self, x: Any) -> str: return f"{self}{x}" def __getattr__(self, key: Any) -> Any: return getattr(self.input, key) def __iter__(self) -> Iterable[str]: if not self.input.multiple: raise Exception("Tried to iterate over a non-multiple parameter.") return self.value.__iter__()
[docs]class DatasetFilenameWrapper(ToolParameterValueWrapper): """ Wraps a dataset so that __str__ returns the filename, but all other attributes are accessible. """ false_path: Optional[str]
[docs] class MetadataWrapper: """ Wraps a Metadata Collection to return MetadataParameters wrapped according to the metadata spec. Methods implemented to match behavior of a Metadata Collection. """
[docs] def __init__( self, dataset: DatasetInstance, compute_environment: Optional["ComputeEnvironment"] = None, ) -> None: self.dataset = dataset self.metadata: "MetadataCollection" = dataset.metadata self.compute_environment = compute_environment
def __getattr__(self, name: str) -> Any: rval = self.metadata.get(name, None) if name in self.metadata.spec: if rval is None: rval = self.metadata.spec[name].no_value metadata_param = self.metadata.spec[name].param rval = metadata_param.to_safe_string(rval) if isinstance(metadata_param, FileParameter) and self.compute_environment: rewrite = self.compute_environment.input_metadata_rewrite(self.dataset, rval) if rewrite is not None: rval = rewrite # Store this value, so we don't need to recalculate if needed # again setattr(self, name, rval) else: # escape string value of non-defined metadata value rval = wrap_with_safe_string(rval) return rval def __bool__(self) -> bool: return bool(self.metadata.__nonzero__()) __nonzero__ = __bool__ def __iter__(self) -> Iterator[Any]: return self.metadata.__iter__()
[docs] def element_is_set(self, name: str) -> bool: return self.metadata.element_is_set(name)
[docs] def get(self, key: str, default: Any = None) -> Any: try: return getattr(self, key) except Exception: return default
[docs] def items(self) -> Iterator[Tuple[str, Any]]: return iter((k, self.get(k)) for k, v in self.metadata.items())
[docs] def __init__( self, dataset: Optional[Union[DatasetInstance, DatasetCollectionElement]], datatypes_registry: Optional["Registry"] = None, tool: Optional["Tool"] = None, name: Optional[str] = None, compute_environment: Optional["ComputeEnvironment"] = None, identifier: Optional[str] = None, io_type: str = "input", formats: Optional[List[str]] = None, ) -> None: if not dataset: dataset_instance: Optional[DatasetInstance] = None ext = "data" if tool is not None and name is not None: try: tool_input = tool.inputs[name] if TYPE_CHECKING: assert isinstance(tool_input, BaseDataToolParameter) # TODO: allow this to work when working with grouping ext = tool_input.extensions[0] except Exception: pass self.dataset = cast( DatasetInstance, wrap_with_safe_string( NoneDataset(datatypes_registry=datatypes_registry, ext=ext), no_wrap_classes=ToolParameterValueWrapper, ), ) else: # Tool wrappers should not normally be accessing .dataset directly, # so we will wrap it and keep the original around for file paths # Should we name this .value to maintain consistency with most other ToolParameterValueWrapper? if isinstance(dataset, DatasetCollectionElement): identifier = dataset.element_identifier dataset_instance = dataset.hda else: dataset_instance = dataset assert dataset_instance if formats: direct_match, target_ext, converted_dataset = dataset_instance.find_conversion_destination(formats) if not direct_match and target_ext and converted_dataset: dataset_instance = converted_dataset self.unsanitized: DatasetInstance = dataset_instance self.dataset = wrap_with_safe_string(dataset_instance, no_wrap_classes=ToolParameterValueWrapper) self.metadata = self.MetadataWrapper(dataset_instance, compute_environment) if isinstance(dataset_instance, HasTags): self.groups = {tag.user_value.lower() for tag in dataset_instance.tags if tag.user_tname == "group"} else: # May be a 'FakeDatasetAssociation' self.groups = set() self.compute_environment = compute_environment # TODO: lazy initialize this... self.__io_type = io_type self.false_path: Optional[str] = None if dataset_instance: if self.__io_type == "input": path_rewrite = ( compute_environment and dataset_instance and compute_environment.input_path_rewrite(dataset_instance) ) if path_rewrite: self.false_path = path_rewrite else: path_rewrite = compute_environment and compute_environment.output_path_rewrite(dataset_instance) if path_rewrite: self.false_path = path_rewrite self.datatypes_registry = datatypes_registry self._element_identifier = identifier
@property def element_identifier(self) -> str: identifier = self._element_identifier if identifier is None: identifier = self.name return identifier @property def file_ext(self) -> str: return str( getattr( self.unsanitized.datatype, "file_ext_export_alias", self.dataset.extension, ) ) @property def name_and_ext(self) -> str: return f"{self.element_identifier}.{self.file_ext}"
[docs] def get_staging_path(self, invalid_chars: Sequence[str] = ("/",)) -> str: """ Strip leading dots, unicode null chars, replace `/` with `_`, truncate at 255 characters. Not safe for commandline use, would need additional sanitization. """ max_len = 254 - len(self.file_ext) safe_element_identifier = filesystem_safe_string( self.element_identifier, max_len=max_len, invalid_chars=invalid_chars ) return f"{safe_element_identifier}.{self.file_ext}"
@property def all_metadata_files(self) -> List[Tuple[str, str]]: return self.unsanitized.get_metadata_file_paths_and_extensions() if self else []
[docs] def serialize(self, invalid_chars: Sequence[str] = ("/",)) -> Dict[str, Any]: return data_input_to_staging_path_and_source_path(self, invalid_chars=invalid_chars) if self else {}
@property def is_collection(self) -> bool: return False
[docs] def is_of_type(self, *exts: str) -> bool: datatypes = [] if not self.datatypes_registry: raise Exception("datatypes_registry is required to use 'is_of_type'.") for e in exts: datatype = self.datatypes_registry.get_datatype_by_extension(e) if datatype is not None: datatypes.append(datatype) else: log.warning( f"Datatype class not found for extension '{e}', which is used as parameter of 'is_of_type()' method" ) return self.dataset.datatype.matches_any(datatypes)
def __str__(self) -> str: if self.false_path is not None: return self.false_path else: return str(self.unsanitized.file_name) def __getattr__(self, key: Any) -> Any: if self.false_path is not None and key == "file_name": # Path to dataset was rewritten for this job. return self.false_path elif key == "extra_files_path": if self.__io_type == "input": path_rewrite = self.compute_environment and self.compute_environment.input_extra_files_rewrite( self.unsanitized ) else: path_rewrite = self.compute_environment and self.compute_environment.output_extra_files_rewrite( self.unsanitized ) if path_rewrite: return path_rewrite else: try: # Assume it is an output and that this wrapper # will be set with correct "files_path" for this # job. return self.files_path except AttributeError: # Otherwise, we have an input - delegate to model and # object store to find the static location of this # directory. try: return self.unsanitized.extra_files_path except exceptions.ObjectNotFound: # NestedObjectstore raises an error here # instead of just returning a non-existent # path like DiskObjectStore. raise elif key == "serialize": return self.serialize else: return getattr(self.dataset, key) def __bool__(self) -> bool: return bool(self.dataset) __nonzero__ = __bool__
[docs]class HasDatasets: job_working_directory: Optional[str] @abc.abstractmethod def __iter__(self) -> Iterator[Any]: pass def _dataset_wrapper( self, dataset: Union[DatasetInstance, DatasetCollectionElement], **kwargs: Any ) -> DatasetFilenameWrapper: return DatasetFilenameWrapper(dataset, **kwargs)
[docs] def paths_as_file(self, sep: str = "\n") -> str: contents = sep.join(map(str, self)) with tempfile.NamedTemporaryFile( mode="w+", prefix="gx_file_list", dir=self.job_working_directory, delete=False ) as fh: fh.write(contents) filepath = fh.name return filepath
[docs]class DatasetListWrapper(List[DatasetFilenameWrapper], ToolParameterValueWrapper, HasDatasets): """ """
[docs] def __init__( self, job_working_directory: Optional[str], datasets: Union[ Sequence[ Union[ None, DatasetInstance, DatasetCollectionInstance, DatasetCollectionElement, ] ], DatasetInstance, ], **kwargs: Any, ) -> None: self._dataset_elements_cache: Dict[str, List[DatasetFilenameWrapper]] = {} if not isinstance(datasets, Sequence): datasets = [datasets] def to_wrapper( dataset: Union[ None, DatasetInstance, DatasetCollectionInstance, DatasetCollectionElement, ] ) -> DatasetFilenameWrapper: if isinstance(dataset, DatasetCollectionElement): dataset2 = dataset.dataset_instance kwargs["identifier"] = dataset.element_identifier else: dataset2 = dataset return self._dataset_wrapper(dataset2, **kwargs) list.__init__(self, map(to_wrapper, datasets)) self.job_working_directory = job_working_directory
[docs] @staticmethod def to_dataset_instances( dataset_instance_sources: Any, ) -> List[Union[None, DatasetInstance]]: dataset_instances: List[Optional[DatasetInstance]] = [] if not isinstance(dataset_instance_sources, list): dataset_instance_sources = [dataset_instance_sources] for dataset_instance_source in dataset_instance_sources: if dataset_instance_source is None: dataset_instances.append(dataset_instance_source) elif getattr(dataset_instance_source, "history_content_type", None) == "dataset": dataset_instances.append(dataset_instance_source) elif getattr(dataset_instance_source, "hda", None): dataset_instances.append(dataset_instance_source) elif hasattr(dataset_instance_source, "child_collection"): dataset_instances.extend(dataset_instance_source.child_collection.dataset_elements) else: dataset_instances.extend(dataset_instance_source.collection.dataset_elements) return dataset_instances
[docs] def get_datasets_for_group(self, group: str) -> List[DatasetFilenameWrapper]: group = str(group).lower() if not self._dataset_elements_cache.get(group): wrappers = [] for element in self: if any(t for t in element.tags if t.user_tname.lower() == "group" and t.value.lower() == group): wrappers.append(element) self._dataset_elements_cache[group] = wrappers return self._dataset_elements_cache[group]
[docs] def serialize(self, invalid_chars: Sequence[str] = ("/",)) -> List[Dict[str, Any]]: return [v.serialize(invalid_chars) for v in self]
def __str__(self) -> str: return ",".join(map(str, self)) def __bool__(self) -> bool: # Fail `#if $param` checks in cheetah if optional input is not provided return any(self) __nonzero__ = __bool__
[docs]class DatasetCollectionWrapper(ToolParameterValueWrapper, HasDatasets): name: Optional[str] collection: DatasetCollection
[docs] def __init__( self, job_working_directory: Optional[str], has_collection: Union[None, DatasetCollectionElement, HistoryDatasetCollectionAssociation], datatypes_registry: "Registry", **kwargs: Any, ) -> None: super().__init__() self.job_working_directory = job_working_directory self._dataset_elements_cache: Dict[str, List[DatasetFilenameWrapper]] = {} self._element_identifiers_extensions_paths_and_metadata_files: Optional[List[List[Any]]] = None self.datatypes_registry = datatypes_registry kwargs["datatypes_registry"] = datatypes_registry self.kwargs = kwargs if has_collection is None: self.__input_supplied = False return else: self.__input_supplied = True if isinstance(has_collection, HistoryDatasetCollectionAssociation): collection = has_collection.collection self.name = has_collection.name elif isinstance(has_collection, DatasetCollectionElement): collection = has_collection.child_collection self.name = has_collection.element_identifier else: collection = has_collection self.name = None self.collection = collection elements = collection.elements element_instances = {} element_instance_list = [] for dataset_collection_element in elements: element_object = dataset_collection_element.element_object element_identifier = dataset_collection_element.element_identifier if dataset_collection_element.is_collection: element_wrapper: Union[DatasetCollectionWrapper, DatasetFilenameWrapper] = DatasetCollectionWrapper( job_working_directory, dataset_collection_element, **kwargs ) else: element_wrapper = self._dataset_wrapper(element_object, identifier=element_identifier, **kwargs) element_instances[element_identifier] = element_wrapper element_instance_list.append(element_wrapper) self.__element_instances = element_instances self.__element_instance_list = element_instance_list
[docs] def get_datasets_for_group(self, group: str) -> List[DatasetFilenameWrapper]: group = str(group).lower() if not self._dataset_elements_cache.get(group): wrappers = [] for element in self.collection.dataset_elements: if any( t for t in element.dataset_instance.tags if t.user_tname.lower() == "group" and t.value.lower() == group ): wrappers.append( self._dataset_wrapper( element.element_object, identifier=element.element_identifier, **self.kwargs ) ) self._dataset_elements_cache[group] = wrappers return self._dataset_elements_cache[group]
[docs] def keys(self) -> Union[List[str], KeysView[Any]]: if not self.__input_supplied: return [] return self.__element_instances.keys()
@property def is_collection(self) -> bool: return True @property def element_identifier(self) -> Optional[str]: return self.name @property def all_paths(self) -> List[str]: return [path for _, _, path, _ in self.element_identifiers_extensions_paths_and_metadata_files] @property def all_metadata_files(self) -> List[List[str]]: return [ metadata_files for _, _, _, metadata_files in self.element_identifiers_extensions_paths_and_metadata_files ] @property def element_identifiers_extensions_paths_and_metadata_files( self, ) -> List[List[Any]]: if self._element_identifiers_extensions_paths_and_metadata_files is None: if self.collection: result = self.collection.element_identifiers_extensions_paths_and_metadata_files self._element_identifiers_extensions_paths_and_metadata_files = result return result else: return [] return self._element_identifiers_extensions_paths_and_metadata_files
[docs] def get_all_staging_paths( self, invalid_chars: Sequence[str] = ("/",), include_collection_name: bool = False, ) -> List[str]: safe_element_identifiers = [] for element_identifiers, extension, *_ in self.element_identifiers_extensions_paths_and_metadata_files: datatype = self.datatypes_registry.get_datatype_by_extension(extension) if datatype: extension = getattr(datatype, "file_ext_export_alias", extension) current_element_identifiers = [] for element_identifier in element_identifiers: max_len = 254 - len(extension) if include_collection_name: max_len = max_len - (len(self.name or "") + 1) assert max_len >= 1, "Could not stage element, element identifier is too long" current_element_identifier = filesystem_safe_string( element_identifier, max_len=max_len, invalid_chars=invalid_chars ) if include_collection_name and self.name: current_element_identifier = f"{filesystem_safe_string(self.name, invalid_chars=invalid_chars)}{os.path.sep}{current_element_identifier}" current_element_identifiers.append(current_element_identifier) safe_element_identifiers.append(f"{os.path.sep.join(current_element_identifiers)}.{extension}") return safe_element_identifiers
[docs] def serialize( self, invalid_chars: Sequence[str] = ("/",), include_collection_name: bool = False, ) -> List[Dict[str, Any]]: return data_collection_input_to_staging_path_and_source_path( self, invalid_chars=invalid_chars, include_collection_name=include_collection_name, )
@property def is_input_supplied(self) -> bool: return self.__input_supplied def __getitem__(self, key: Union[str, int]) -> Union[None, "DatasetCollectionWrapper", DatasetFilenameWrapper]: if not self.__input_supplied: return None if isinstance(key, int): return self.__element_instance_list[key] else: return self.__element_instances[key] def __getattr__(self, key: str) -> Union[None, "DatasetCollectionWrapper", DatasetFilenameWrapper]: if not self.__input_supplied: return None try: return self.__element_instances[key] except KeyError: raise AttributeError() def __iter__( self, ) -> Iterator[Union["DatasetCollectionWrapper", DatasetFilenameWrapper]]: if not self.__input_supplied: return [].__iter__() return self.__element_instance_list.__iter__() def __bool__(self) -> bool: # Fail `#if $param` checks in cheetah is optional input # not specified or if resulting collection is empty. return self.__input_supplied and bool(self.__element_instance_list) __nonzero__ = __bool__
[docs]class ElementIdentifierMapper: """Track mapping of dataset collection elements datasets to element identifiers."""
[docs] def __init__(self, input_datasets: Optional[Dict[str, Any]] = None) -> None: if input_datasets is not None: self.identifier_key_dict = {v: f"{k}|__identifier__" for k, v in input_datasets.items()} else: self.identifier_key_dict = {}
[docs] def identifier(self, dataset_value: str, input_values: Dict[str, str]) -> Optional[str]: identifier_key = self.identifier_key_dict.get(dataset_value, None) element_identifier = None if identifier_key: element_identifier = input_values.get(identifier_key, None) return element_identifier