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@@ -504,3 +504,4 @@ material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/wrapt/_wrapp
 material/dataset/Datasets/Electron[[:space:]]Microscopy[[:space:]]Image[[:space:]]Masks/TiO2_Masks_Manual_4connected_4Classes/1908250_cm.tif filter=lfs diff=lfs merge=lfs -text
 material/dataset/Datasets/Electron[[:space:]]Microscopy[[:space:]]Image[[:space:]]Masks/TiO2_Masks_TSEM/Image_Registration/Output/1908272/1908272.tif filter=lfs diff=lfs merge=lfs -text
 material/dataset/Datasets/Electron[[:space:]]Microscopy[[:space:]]Image[[:space:]]Masks/TiO2_Masks_Manual_4connected/1908308_m.tif filter=lfs diff=lfs merge=lfs -text
+material/dataset/Datasets/Electron[[:space:]]Microscopy[[:space:]]Image[[:space:]]Masks/TiO2_Masks_Manual_4connected_4Classes/1908284_cm.tif filter=lfs diff=lfs merge=lfs -text

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/__init__.py

@@ -0,0 +1,70 @@
+# This file also re-exports symbols for wider use. We configure mypy and flake8
+# to be aware that this file does this.
+
+from jedi.inference.compiled.value import CompiledValue, CompiledName, \
+    CompiledValueFilter, CompiledValueName, create_from_access_path
+from jedi.inference.base_value import LazyValueWrapper
+
+
+def builtin_from_name(inference_state, string):
+    typing_builtins_module = inference_state.builtins_module
+    if string in ('None', 'True', 'False'):
+        builtins, = typing_builtins_module.non_stub_value_set
+        filter_ = next(builtins.get_filters())
+    else:
+        filter_ = next(typing_builtins_module.get_filters())
+    name, = filter_.get(string)
+    value, = name.infer()
+    return value
+
+
+class ExactValue(LazyValueWrapper):
+    """
+    This class represents exact values, that makes operations like additions
+    and exact boolean values possible, while still being a "normal" stub.
+    """
+    def __init__(self, compiled_value):
+        self.inference_state = compiled_value.inference_state
+        self._compiled_value = compiled_value
+
+    def __getattribute__(self, name):
+        if name in ('get_safe_value', 'execute_operation', 'access_handle',
+                    'negate', 'py__bool__', 'is_compiled'):
+            return getattr(self._compiled_value, name)
+        return super().__getattribute__(name)
+
+    def _get_wrapped_value(self):
+        instance, = builtin_from_name(
+            self.inference_state, self._compiled_value.name.string_name).execute_with_values()
+        return instance
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._compiled_value)
+
+
+def create_simple_object(inference_state, obj):
+    """
+    Only allows creations of objects that are easily picklable across Python
+    versions.
+    """
+    assert type(obj) in (int, float, str, bytes, slice, complex, bool), repr(obj)
+    compiled_value = create_from_access_path(
+        inference_state,
+        inference_state.compiled_subprocess.create_simple_object(obj)
+    )
+    return ExactValue(compiled_value)
+
+
+def get_string_value_set(inference_state):
+    return builtin_from_name(inference_state, 'str').execute_with_values()
+
+
+def load_module(inference_state, dotted_name, **kwargs):
+    # Temporary, some tensorflow builtins cannot be loaded, so it's tried again
+    # and again and it's really slow.
+    if dotted_name.startswith('tensorflow.'):
+        return None
+    access_path = inference_state.compiled_subprocess.load_module(dotted_name=dotted_name, **kwargs)
+    if access_path is None:
+        return None
+    return create_from_access_path(inference_state, access_path)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/access.py

@@ -0,0 +1,562 @@
+import inspect
+import types
+import traceback
+import sys
+import operator as op
+from collections import namedtuple
+import warnings
+import re
+import builtins
+import typing
+from pathlib import Path
+from typing import Optional, Tuple
+
+from jedi.inference.compiled.getattr_static import getattr_static
+
+ALLOWED_GETITEM_TYPES = (str, list, tuple, bytes, bytearray, dict)
+
+MethodDescriptorType = type(str.replace)
+# These are not considered classes and access is granted even though they have
+# a __class__ attribute.
+NOT_CLASS_TYPES = (
+    types.BuiltinFunctionType,
+    types.CodeType,
+    types.FrameType,
+    types.FunctionType,
+    types.GeneratorType,
+    types.GetSetDescriptorType,
+    types.LambdaType,
+    types.MemberDescriptorType,
+    types.MethodType,
+    types.ModuleType,
+    types.TracebackType,
+    MethodDescriptorType,
+    types.MappingProxyType,
+    types.SimpleNamespace,
+    types.DynamicClassAttribute,
+)
+
+# Those types don't exist in typing.
+MethodDescriptorType = type(str.replace)
+WrapperDescriptorType = type(set.__iter__)
+# `object.__subclasshook__` is an already executed descriptor.
+object_class_dict = type.__dict__["__dict__"].__get__(object)  # type: ignore[index]
+ClassMethodDescriptorType = type(object_class_dict['__subclasshook__'])
+
+_sentinel = object()
+
+# Maps Python syntax to the operator module.
+COMPARISON_OPERATORS = {
+    '==': op.eq,
+    '!=': op.ne,
+    'is': op.is_,
+    'is not': op.is_not,
+    '<': op.lt,
+    '<=': op.le,
+    '>': op.gt,
+    '>=': op.ge,
+}
+
+_OPERATORS = {
+    '+': op.add,
+    '-': op.sub,
+}
+_OPERATORS.update(COMPARISON_OPERATORS)
+
+ALLOWED_DESCRIPTOR_ACCESS = (
+    types.FunctionType,
+    types.GetSetDescriptorType,
+    types.MemberDescriptorType,
+    MethodDescriptorType,
+    WrapperDescriptorType,
+    ClassMethodDescriptorType,
+    staticmethod,
+    classmethod,
+)
+
+
+def safe_getattr(obj, name, default=_sentinel):
+    try:
+        attr, is_get_descriptor = getattr_static(obj, name)
+    except AttributeError:
+        if default is _sentinel:
+            raise
+        return default
+    else:
+        if isinstance(attr, ALLOWED_DESCRIPTOR_ACCESS):
+            # In case of descriptors that have get methods we cannot return
+            # it's value, because that would mean code execution.
+            # Since it's an isinstance call, code execution is still possible,
+            # but this is not really a security feature, but much more of a
+            # safety feature. Code execution is basically always possible when
+            # a module is imported. This is here so people don't shoot
+            # themselves in the foot.
+            return getattr(obj, name)
+    return attr
+
+
+SignatureParam = namedtuple(
+    'SignatureParam',
+    'name has_default default default_string has_annotation annotation annotation_string kind_name'
+)
+
+
+def shorten_repr(func):
+    def wrapper(self):
+        r = func(self)
+        if len(r) > 50:
+            r = r[:50] + '..'
+        return r
+    return wrapper
+
+
+def create_access(inference_state, obj):
+    return inference_state.compiled_subprocess.get_or_create_access_handle(obj)
+
+
+def load_module(inference_state, dotted_name, sys_path):
+    temp, sys.path = sys.path, sys_path
+    try:
+        __import__(dotted_name)
+    except ImportError:
+        # If a module is "corrupt" or not really a Python module or whatever.
+        warnings.warn(
+            "Module %s not importable in path %s." % (dotted_name, sys_path),
+            UserWarning,
+            stacklevel=2,
+        )
+        return None
+    except Exception:
+        # Since __import__ pretty much makes code execution possible, just
+        # catch any error here and print it.
+        warnings.warn(
+            "Cannot import:\n%s" % traceback.format_exc(), UserWarning, stacklevel=2
+        )
+        return None
+    finally:
+        sys.path = temp
+
+    # Just access the cache after import, because of #59 as well as the very
+    # complicated import structure of Python.
+    module = sys.modules[dotted_name]
+    return create_access_path(inference_state, module)
+
+
+class AccessPath:
+    def __init__(self, accesses):
+        self.accesses = accesses
+
+
+def create_access_path(inference_state, obj) -> AccessPath:
+    access = create_access(inference_state, obj)
+    return AccessPath(access.get_access_path_tuples())
+
+
+def get_api_type(obj):
+    if inspect.isclass(obj):
+        return 'class'
+    elif inspect.ismodule(obj):
+        return 'module'
+    elif inspect.isbuiltin(obj) or inspect.ismethod(obj) \
+            or inspect.ismethoddescriptor(obj) or inspect.isfunction(obj):
+        return 'function'
+    # Everything else...
+    return 'instance'
+
+
+class DirectObjectAccess:
+    def __init__(self, inference_state, obj):
+        self._inference_state = inference_state
+        self._obj = obj
+
+    def __repr__(self):
+        return '%s(%s)' % (self.__class__.__name__, self.get_repr())
+
+    def _create_access(self, obj):
+        return create_access(self._inference_state, obj)
+
+    def _create_access_path(self, obj) -> AccessPath:
+        return create_access_path(self._inference_state, obj)
+
+    def py__bool__(self):
+        return bool(self._obj)
+
+    def py__file__(self) -> Optional[Path]:
+        try:
+            return Path(self._obj.__file__)
+        except AttributeError:
+            return None
+
+    def py__doc__(self):
+        return inspect.getdoc(self._obj) or ''
+
+    def py__name__(self):
+        if not _is_class_instance(self._obj) or \
+                inspect.ismethoddescriptor(self._obj):  # slots
+            cls = self._obj
+        else:
+            try:
+                cls = self._obj.__class__
+            except AttributeError:
+                # happens with numpy.core.umath._UFUNC_API (you get it
+                # automatically by doing `import numpy`.
+                return None
+
+        try:
+            return cls.__name__
+        except AttributeError:
+            return None
+
+    def py__mro__accesses(self):
+        return tuple(self._create_access_path(cls) for cls in self._obj.__mro__[1:])
+
+    def py__getitem__all_values(self):
+        if isinstance(self._obj, dict):
+            return [self._create_access_path(v) for v in self._obj.values()]
+        if isinstance(self._obj, (list, tuple)):
+            return [self._create_access_path(v) for v in self._obj]
+
+        if self.is_instance():
+            cls = DirectObjectAccess(self._inference_state, self._obj.__class__)
+            return cls.py__getitem__all_values()
+
+        try:
+            getitem = self._obj.__getitem__
+        except AttributeError:
+            pass
+        else:
+            annotation = DirectObjectAccess(self._inference_state, getitem).get_return_annotation()
+            if annotation is not None:
+                return [annotation]
+        return None
+
+    def py__simple_getitem__(self, index, *, safe=True):
+        if safe and type(self._obj) not in ALLOWED_GETITEM_TYPES:
+            # Get rid of side effects, we won't call custom `__getitem__`s.
+            return None
+
+        return self._create_access_path(self._obj[index])
+
+    def py__iter__list(self):
+        try:
+            iter_method = self._obj.__iter__
+        except AttributeError:
+            return None
+        else:
+            p = DirectObjectAccess(self._inference_state, iter_method).get_return_annotation()
+            if p is not None:
+                return [p]
+
+        if type(self._obj) not in ALLOWED_GETITEM_TYPES:
+            # Get rid of side effects, we won't call custom `__getitem__`s.
+            return []
+
+        lst = []
+        for i, part in enumerate(self._obj):
+            if i > 20:
+                # Should not go crazy with large iterators
+                break
+            lst.append(self._create_access_path(part))
+        return lst
+
+    def py__class__(self):
+        return self._create_access_path(self._obj.__class__)
+
+    def py__bases__(self):
+        return [self._create_access_path(base) for base in self._obj.__bases__]
+
+    def py__path__(self):
+        paths = getattr(self._obj, '__path__', None)
+        # Avoid some weird hacks that would just fail, because they cannot be
+        # used by pickle.
+        if not isinstance(paths, list) \
+                or not all(isinstance(p, str) for p in paths):
+            return None
+        return paths
+
+    @shorten_repr
+    def get_repr(self):
+        if inspect.ismodule(self._obj):
+            return repr(self._obj)
+        # Try to avoid execution of the property.
+        if safe_getattr(self._obj, '__module__', default='') == 'builtins':
+            return repr(self._obj)
+
+        type_ = type(self._obj)
+        if type_ == type:
+            return type.__repr__(self._obj)
+
+        if safe_getattr(type_, '__module__', default='') == 'builtins':
+            # Allow direct execution of repr for builtins.
+            return repr(self._obj)
+        return object.__repr__(self._obj)
+
+    def is_class(self):
+        return inspect.isclass(self._obj)
+
+    def is_function(self):
+        return inspect.isfunction(self._obj) or inspect.ismethod(self._obj)
+
+    def is_module(self):
+        return inspect.ismodule(self._obj)
+
+    def is_instance(self):
+        return _is_class_instance(self._obj)
+
+    def ismethoddescriptor(self):
+        return inspect.ismethoddescriptor(self._obj)
+
+    def get_qualified_names(self):
+        def try_to_get_name(obj):
+            return getattr(obj, '__qualname__', getattr(obj, '__name__', None))
+
+        if self.is_module():
+            return ()
+        name = try_to_get_name(self._obj)
+        if name is None:
+            name = try_to_get_name(type(self._obj))
+            if name is None:
+                return ()
+        return tuple(name.split('.'))
+
+    def dir(self):
+        return dir(self._obj)
+
+    def has_iter(self):
+        try:
+            iter(self._obj)
+            return True
+        except TypeError:
+            return False
+
+    def is_allowed_getattr(self, name, safe=True) -> Tuple[bool, bool, Optional[AccessPath]]:
+        # TODO this API is ugly.
+        try:
+            attr, is_get_descriptor = getattr_static(self._obj, name)
+        except AttributeError:
+            if not safe:
+                # Unsafe is mostly used to check for __getattr__/__getattribute__.
+                # getattr_static works for properties, but the underscore methods
+                # are just ignored (because it's safer and avoids more code
+                # execution). See also GH #1378.
+
+                # Avoid warnings, see comment in the next function.
+                with warnings.catch_warnings(record=True):
+                    warnings.simplefilter("always")
+                    try:
+                        return hasattr(self._obj, name), False, None
+                    except Exception:
+                        # Obviously has an attribute (probably a property) that
+                        # gets executed, so just avoid all exceptions here.
+                        pass
+            return False, False, None
+        else:
+            if is_get_descriptor and type(attr) not in ALLOWED_DESCRIPTOR_ACCESS:
+                if isinstance(attr, property):
+                    if hasattr(attr.fget, '__annotations__'):
+                        a = DirectObjectAccess(self._inference_state, attr.fget)
+                        return True, True, a.get_return_annotation()
+                # In case of descriptors that have get methods we cannot return
+                # it's value, because that would mean code execution.
+                return True, True, None
+        return True, False, None
+
+    def getattr_paths(self, name, default=_sentinel):
+        try:
+            # Make sure no warnings are printed here, this is autocompletion,
+            # warnings should not be shown. See also GH #1383.
+            with warnings.catch_warnings(record=True):
+                warnings.simplefilter("always")
+                return_obj = getattr(self._obj, name)
+        except Exception as e:
+            if default is _sentinel:
+                if isinstance(e, AttributeError):
+                    # Happens e.g. in properties of
+                    # PyQt4.QtGui.QStyleOptionComboBox.currentText
+                    # -> just set it to None
+                    raise
+                # Just in case anything happens, return an AttributeError. It
+                # should not crash.
+                raise AttributeError
+            return_obj = default
+        access = self._create_access(return_obj)
+        if inspect.ismodule(return_obj):
+            return [access]
+
+        try:
+            module = return_obj.__module__
+        except AttributeError:
+            pass
+        else:
+            if module is not None and isinstance(module, str):
+                try:
+                    __import__(module)
+                    # For some modules like _sqlite3, the __module__ for classes is
+                    # different, in this case it's sqlite3. So we have to try to
+                    # load that "original" module, because it's not loaded yet. If
+                    # we don't do that, we don't really have a "parent" module and
+                    # we would fall back to builtins.
+                except ImportError:
+                    pass
+
+        module = inspect.getmodule(return_obj)
+        if module is None:
+            module = inspect.getmodule(type(return_obj))
+            if module is None:
+                module = builtins
+        return [self._create_access(module), access]
+
+    def get_safe_value(self):
+        if type(self._obj) in (bool, bytes, float, int, str, slice) or self._obj is None:
+            return self._obj
+        raise ValueError("Object is type %s and not simple" % type(self._obj))
+
+    def get_api_type(self):
+        return get_api_type(self._obj)
+
+    def get_array_type(self):
+        if isinstance(self._obj, dict):
+            return 'dict'
+        return None
+
+    def get_key_paths(self):
+        def iter_partial_keys():
+            # We could use list(keys()), but that might take a lot more memory.
+            for (i, k) in enumerate(self._obj.keys()):
+                # Limit key listing at some point. This is artificial, but this
+                # way we don't get stalled because of slow completions
+                if i > 50:
+                    break
+                yield k
+
+        return [self._create_access_path(k) for k in iter_partial_keys()]
+
+    def get_access_path_tuples(self):
+        accesses = [create_access(self._inference_state, o) for o in self._get_objects_path()]
+        return [(access.py__name__(), access) for access in accesses]
+
+    def _get_objects_path(self):
+        def get():
+            obj = self._obj
+            yield obj
+            try:
+                obj = obj.__objclass__
+            except AttributeError:
+                pass
+            else:
+                yield obj
+
+            try:
+                # Returns a dotted string path.
+                imp_plz = obj.__module__
+            except AttributeError:
+                # Unfortunately in some cases like `int` there's no __module__
+                if not inspect.ismodule(obj):
+                    yield builtins
+            else:
+                if imp_plz is None:
+                    # Happens for example in `(_ for _ in []).send.__module__`.
+                    yield builtins
+                else:
+                    try:
+                        yield sys.modules[imp_plz]
+                    except KeyError:
+                        # __module__ can be something arbitrary that doesn't exist.
+                        yield builtins
+
+        return list(reversed(list(get())))
+
+    def execute_operation(self, other_access_handle, operator):
+        other_access = other_access_handle.access
+        op = _OPERATORS[operator]
+        return self._create_access_path(op(self._obj, other_access._obj))
+
+    def get_annotation_name_and_args(self):
+        """
+        Returns Tuple[Optional[str], Tuple[AccessPath, ...]]
+        """
+        name = None
+        args = ()
+        if safe_getattr(self._obj, '__module__', default='') == 'typing':
+            m = re.match(r'typing.(\w+)\[', repr(self._obj))
+            if m is not None:
+                name = m.group(1)
+
+                import typing
+                if sys.version_info >= (3, 8):
+                    args = typing.get_args(self._obj)
+                else:
+                    args = safe_getattr(self._obj, '__args__', default=None)
+        return name, tuple(self._create_access_path(arg) for arg in args)
+
+    def needs_type_completions(self):
+        return inspect.isclass(self._obj) and self._obj != type
+
+    def _annotation_to_str(self, annotation):
+        return inspect.formatannotation(annotation)
+
+    def get_signature_params(self):
+        return [
+            SignatureParam(
+                name=p.name,
+                has_default=p.default is not p.empty,
+                default=self._create_access_path(p.default),
+                default_string=repr(p.default),
+                has_annotation=p.annotation is not p.empty,
+                annotation=self._create_access_path(p.annotation),
+                annotation_string=self._annotation_to_str(p.annotation),
+                kind_name=str(p.kind)
+            ) for p in self._get_signature().parameters.values()
+        ]
+
+    def _get_signature(self):
+        obj = self._obj
+        try:
+            return inspect.signature(obj)
+        except (RuntimeError, TypeError):
+            # Reading the code of the function in Python 3.6 implies there are
+            # at least these errors that might occur if something is wrong with
+            # the signature. In that case we just want a simple escape for now.
+            raise ValueError
+
+    def get_return_annotation(self) -> Optional[AccessPath]:
+        try:
+            o = self._obj.__annotations__.get('return')
+        except AttributeError:
+            return None
+
+        if o is None:
+            return None
+
+        try:
+            o = typing.get_type_hints(self._obj).get('return')
+        except Exception:
+            pass
+
+        return self._create_access_path(o)
+
+    def negate(self):
+        return self._create_access_path(-self._obj)
+
+    def get_dir_infos(self):
+        """
+        Used to return a couple of infos that are needed when accessing the sub
+        objects of an objects
+        """
+        tuples = dict(
+            (name, self.is_allowed_getattr(name))
+            for name in self.dir()
+        )
+        return self.needs_type_completions(), tuples
+
+
+def _is_class_instance(obj):
+    """Like inspect.* methods."""
+    try:
+        cls = obj.__class__
+    except AttributeError:
+        return False
+    else:
+        # The isinstance check for cls is just there so issubclass doesn't
+        # raise an exception.
+        return cls != type and isinstance(cls, type) and not issubclass(cls, NOT_CLASS_TYPES)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/getattr_static.py

@@ -0,0 +1,121 @@
+"""
+A static version of getattr.
+This is a backport of the Python 3 code with a little bit of additional
+information returned to enable Jedi to make decisions.
+"""
+
+import types
+
+from jedi import debug
+
+_sentinel = object()
+
+
+def _check_instance(obj, attr):
+    instance_dict = {}
+    try:
+        instance_dict = object.__getattribute__(obj, "__dict__")
+    except AttributeError:
+        pass
+    return dict.get(instance_dict, attr, _sentinel)
+
+
+def _check_class(klass, attr):
+    for entry in _static_getmro(klass):
+        if _shadowed_dict(type(entry)) is _sentinel:
+            try:
+                return entry.__dict__[attr]
+            except KeyError:
+                pass
+    return _sentinel
+
+
+def _is_type(obj):
+    try:
+        _static_getmro(obj)
+    except TypeError:
+        return False
+    return True
+
+
+def _shadowed_dict(klass):
+    dict_attr = type.__dict__["__dict__"]
+    for entry in _static_getmro(klass):
+        try:
+            class_dict = dict_attr.__get__(entry)["__dict__"]
+        except KeyError:
+            pass
+        else:
+            if not (type(class_dict) is types.GetSetDescriptorType
+                    and class_dict.__name__ == "__dict__"
+                    and class_dict.__objclass__ is entry):
+                return class_dict
+    return _sentinel
+
+
+def _static_getmro(klass):
+    mro = type.__dict__['__mro__'].__get__(klass)
+    if not isinstance(mro, (tuple, list)):
+        # There are unfortunately no tests for this, I was not able to
+        # reproduce this in pure Python. However should still solve the issue
+        # raised in GH #1517.
+        debug.warning('mro of %s returned %s, should be a tuple' % (klass, mro))
+        return ()
+    return mro
+
+
+def _safe_hasattr(obj, name):
+    return _check_class(type(obj), name) is not _sentinel
+
+
+def _safe_is_data_descriptor(obj):
+    return _safe_hasattr(obj, '__set__') or _safe_hasattr(obj, '__delete__')
+
+
+def getattr_static(obj, attr, default=_sentinel):
+    """Retrieve attributes without triggering dynamic lookup via the
+       descriptor protocol,  __getattr__ or __getattribute__.
+
+       Note: this function may not be able to retrieve all attributes
+       that getattr can fetch (like dynamically created attributes)
+       and may find attributes that getattr can't (like descriptors
+       that raise AttributeError). It can also return descriptor objects
+       instead of instance members in some cases. See the
+       documentation for details.
+
+       Returns a tuple `(attr, is_get_descriptor)`. is_get_descripter means that
+       the attribute is a descriptor that has a `__get__` attribute.
+    """
+    instance_result = _sentinel
+    if not _is_type(obj):
+        klass = type(obj)
+        dict_attr = _shadowed_dict(klass)
+        if (dict_attr is _sentinel or type(dict_attr) is types.MemberDescriptorType):
+            instance_result = _check_instance(obj, attr)
+    else:
+        klass = obj
+
+    klass_result = _check_class(klass, attr)
+
+    if instance_result is not _sentinel and klass_result is not _sentinel:
+        if _safe_hasattr(klass_result, '__get__') \
+                and _safe_is_data_descriptor(klass_result):
+            # A get/set descriptor has priority over everything.
+            return klass_result, True
+
+    if instance_result is not _sentinel:
+        return instance_result, False
+    if klass_result is not _sentinel:
+        return klass_result, _safe_hasattr(klass_result, '__get__')
+
+    if obj is klass:
+        # for types we check the metaclass too
+        for entry in _static_getmro(type(klass)):
+            if _shadowed_dict(type(entry)) is _sentinel:
+                try:
+                    return entry.__dict__[attr], False
+                except KeyError:
+                    pass
+    if default is not _sentinel:
+        return default, False
+    raise AttributeError(attr)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/mixed.py

@@ -0,0 +1,309 @@
+"""
+Used only for REPL Completion.
+"""
+
+import inspect
+from pathlib import Path
+
+from jedi.parser_utils import get_cached_code_lines
+
+from jedi import settings
+from jedi.cache import memoize_method
+from jedi.inference import compiled
+from jedi.file_io import FileIO
+from jedi.inference.names import NameWrapper
+from jedi.inference.base_value import ValueSet, ValueWrapper, NO_VALUES
+from jedi.inference.value import ModuleValue
+from jedi.inference.cache import inference_state_function_cache, \
+    inference_state_method_cache
+from jedi.inference.compiled.access import ALLOWED_GETITEM_TYPES, get_api_type
+from jedi.inference.gradual.conversion import to_stub
+from jedi.inference.context import CompiledContext, CompiledModuleContext, \
+    TreeContextMixin
+
+_sentinel = object()
+
+
+class MixedObject(ValueWrapper):
+    """
+    A ``MixedObject`` is used in two ways:
+
+    1. It uses the default logic of ``parser.python.tree`` objects,
+    2. except for getattr calls and signatures. The names dicts are generated
+       in a fashion like ``CompiledValue``.
+
+    This combined logic makes it possible to provide more powerful REPL
+    completion. It allows side effects that are not noticable with the default
+    parser structure to still be completable.
+
+    The biggest difference from CompiledValue to MixedObject is that we are
+    generally dealing with Python code and not with C code. This will generate
+    fewer special cases, because we in Python you don't have the same freedoms
+    to modify the runtime.
+    """
+    def __init__(self, compiled_value, tree_value):
+        super().__init__(tree_value)
+        self.compiled_value = compiled_value
+        self.access_handle = compiled_value.access_handle
+
+    def get_filters(self, *args, **kwargs):
+        yield MixedObjectFilter(
+            self.inference_state, self.compiled_value, self._wrapped_value)
+
+    def get_signatures(self):
+        # Prefer `inspect.signature` over somehow analyzing Python code. It
+        # should be very precise, especially for stuff like `partial`.
+        return self.compiled_value.get_signatures()
+
+    @inference_state_method_cache(default=NO_VALUES)
+    def py__call__(self, arguments):
+        # Fallback to the wrapped value if to stub returns no values.
+        values = to_stub(self._wrapped_value)
+        if not values:
+            values = self._wrapped_value
+        return values.py__call__(arguments)
+
+    def get_safe_value(self, default=_sentinel):
+        if default is _sentinel:
+            return self.compiled_value.get_safe_value()
+        else:
+            return self.compiled_value.get_safe_value(default)
+
+    @property
+    def array_type(self):
+        return self.compiled_value.array_type
+
+    def get_key_values(self):
+        return self.compiled_value.get_key_values()
+
+    def py__simple_getitem__(self, index):
+        python_object = self.compiled_value.access_handle.access._obj
+        if type(python_object) in ALLOWED_GETITEM_TYPES:
+            return self.compiled_value.py__simple_getitem__(index)
+        return self._wrapped_value.py__simple_getitem__(index)
+
+    def negate(self):
+        return self.compiled_value.negate()
+
+    def _as_context(self):
+        if self.parent_context is None:
+            return MixedModuleContext(self)
+        return MixedContext(self)
+
+    def __repr__(self):
+        return '<%s: %s; %s>' % (
+            type(self).__name__,
+            self.access_handle.get_repr(),
+            self._wrapped_value,
+        )
+
+
+class MixedContext(CompiledContext, TreeContextMixin):
+    @property
+    def compiled_value(self):
+        return self._value.compiled_value
+
+
+class MixedModuleContext(CompiledModuleContext, MixedContext):
+    pass
+
+
+class MixedName(NameWrapper):
+    """
+    The ``CompiledName._compiled_value`` is our MixedObject.
+    """
+    def __init__(self, wrapped_name, parent_tree_value):
+        super().__init__(wrapped_name)
+        self._parent_tree_value = parent_tree_value
+
+    @property
+    def start_pos(self):
+        values = list(self.infer())
+        if not values:
+            # This means a start_pos that doesn't exist (compiled objects).
+            return 0, 0
+        return values[0].name.start_pos
+
+    @memoize_method
+    def infer(self):
+        compiled_value = self._wrapped_name.infer_compiled_value()
+        tree_value = self._parent_tree_value
+        if tree_value.is_instance() or tree_value.is_class():
+            tree_values = tree_value.py__getattribute__(self.string_name)
+            if compiled_value.is_function():
+                return ValueSet({MixedObject(compiled_value, v) for v in tree_values})
+
+        module_context = tree_value.get_root_context()
+        return _create(self._inference_state, compiled_value, module_context)
+
+
+class MixedObjectFilter(compiled.CompiledValueFilter):
+    def __init__(self, inference_state, compiled_value, tree_value):
+        super().__init__(inference_state, compiled_value)
+        self._tree_value = tree_value
+
+    def _create_name(self, *args, **kwargs):
+        return MixedName(
+            super()._create_name(*args, **kwargs),
+            self._tree_value,
+        )
+
+
+@inference_state_function_cache()
+def _load_module(inference_state, path):
+    return inference_state.parse(
+        path=path,
+        cache=True,
+        diff_cache=settings.fast_parser,
+        cache_path=settings.cache_directory
+    ).get_root_node()
+
+
+def _get_object_to_check(python_object):
+    """Check if inspect.getfile has a chance to find the source."""
+    try:
+        python_object = inspect.unwrap(python_object)
+    except ValueError:
+        # Can return a ValueError when it wraps around
+        pass
+
+    if (inspect.ismodule(python_object)
+            or inspect.isclass(python_object)
+            or inspect.ismethod(python_object)
+            or inspect.isfunction(python_object)
+            or inspect.istraceback(python_object)
+            or inspect.isframe(python_object)
+            or inspect.iscode(python_object)):
+        return python_object
+
+    try:
+        return python_object.__class__
+    except AttributeError:
+        raise TypeError  # Prevents computation of `repr` within inspect.
+
+
+def _find_syntax_node_name(inference_state, python_object):
+    original_object = python_object
+    try:
+        python_object = _get_object_to_check(python_object)
+        path = inspect.getsourcefile(python_object)
+    except (OSError, TypeError):
+        # The type might not be known (e.g. class_with_dict.__weakref__)
+        return None
+    path = None if path is None else Path(path)
+    try:
+        if path is None or not path.exists():
+            # The path might not exist or be e.g. <stdin>.
+            return None
+    except OSError:
+        # Might raise an OSError on Windows:
+        #
+        #     [WinError 123] The filename, directory name, or volume label
+        #     syntax is incorrect: '<string>'
+        return None
+
+    file_io = FileIO(path)
+    module_node = _load_module(inference_state, path)
+
+    if inspect.ismodule(python_object):
+        # We don't need to check names for modules, because there's not really
+        # a way to write a module in a module in Python (and also __name__ can
+        # be something like ``email.utils``).
+        code_lines = get_cached_code_lines(inference_state.grammar, path)
+        return module_node, module_node, file_io, code_lines
+
+    try:
+        name_str = python_object.__name__
+    except AttributeError:
+        # Stuff like python_function.__code__.
+        return None
+
+    if name_str == '<lambda>':
+        return None  # It's too hard to find lambdas.
+
+    # Doesn't always work (e.g. os.stat_result)
+    names = module_node.get_used_names().get(name_str, [])
+    # Only functions and classes are relevant. If a name e.g. points to an
+    # import, it's probably a builtin (like collections.deque) and needs to be
+    # ignored.
+    names = [
+        n for n in names
+        if n.parent.type in ('funcdef', 'classdef') and n.parent.name == n
+    ]
+    if not names:
+        return None
+
+    try:
+        code = python_object.__code__
+        # By using the line number of a code object we make the lookup in a
+        # file pretty easy. There's still a possibility of people defining
+        # stuff like ``a = 3; foo(a); a = 4`` on the same line, but if people
+        # do so we just don't care.
+        line_nr = code.co_firstlineno
+    except AttributeError:
+        pass
+    else:
+        line_names = [name for name in names if name.start_pos[0] == line_nr]
+        # There's a chance that the object is not available anymore, because
+        # the code has changed in the background.
+        if line_names:
+            names = line_names
+
+    code_lines = get_cached_code_lines(inference_state.grammar, path)
+    # It's really hard to actually get the right definition, here as a last
+    # resort we just return the last one. This chance might lead to odd
+    # completions at some points but will lead to mostly correct type
+    # inference, because people tend to define a public name in a module only
+    # once.
+    tree_node = names[-1].parent
+    if tree_node.type == 'funcdef' and get_api_type(original_object) == 'instance':
+        # If an instance is given and we're landing on a function (e.g.
+        # partial in 3.5), something is completely wrong and we should not
+        # return that.
+        return None
+    return module_node, tree_node, file_io, code_lines
+
+
+@inference_state_function_cache()
+def _create(inference_state, compiled_value, module_context):
+    # TODO accessing this is bad, but it probably doesn't matter that much,
+    # because we're working with interpreters only here.
+    python_object = compiled_value.access_handle.access._obj
+    result = _find_syntax_node_name(inference_state, python_object)
+    if result is None:
+        # TODO Care about generics from stuff like `[1]` and don't return like this.
+        if type(python_object) in (dict, list, tuple):
+            return ValueSet({compiled_value})
+
+        tree_values = to_stub(compiled_value)
+        if not tree_values:
+            return ValueSet({compiled_value})
+    else:
+        module_node, tree_node, file_io, code_lines = result
+
+        if module_context is None or module_context.tree_node != module_node:
+            root_compiled_value = compiled_value.get_root_context().get_value()
+            # TODO this __name__ might be wrong.
+            name = root_compiled_value.py__name__()
+            string_names = tuple(name.split('.'))
+            module_value = ModuleValue(
+                inference_state, module_node,
+                file_io=file_io,
+                string_names=string_names,
+                code_lines=code_lines,
+                is_package=root_compiled_value.is_package(),
+            )
+            if name is not None:
+                inference_state.module_cache.add(string_names, ValueSet([module_value]))
+            module_context = module_value.as_context()
+
+        tree_values = ValueSet({module_context.create_value(tree_node)})
+        if tree_node.type == 'classdef':
+            if not compiled_value.is_class():
+                # Is an instance, not a class.
+                tree_values = tree_values.execute_with_values()
+
+    return ValueSet(
+        MixedObject(compiled_value, tree_value=tree_value)
+        for tree_value in tree_values
+    )

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/subprocess/functions.py

@@ -0,0 +1,257 @@
+import sys
+import os
+import inspect
+import importlib
+from pathlib import Path
+from zipfile import ZipFile
+from zipimport import zipimporter, ZipImportError
+from importlib.machinery import all_suffixes
+
+from jedi.inference.compiled import access
+from jedi import debug
+from jedi import parser_utils
+from jedi.file_io import KnownContentFileIO, ZipFileIO
+
+
+def get_sys_path():
+    return sys.path
+
+
+def load_module(inference_state, **kwargs):
+    return access.load_module(inference_state, **kwargs)
+
+
+def get_compiled_method_return(inference_state, id, attribute, *args, **kwargs):
+    handle = inference_state.compiled_subprocess.get_access_handle(id)
+    return getattr(handle.access, attribute)(*args, **kwargs)
+
+
+def create_simple_object(inference_state, obj):
+    return access.create_access_path(inference_state, obj)
+
+
+def get_module_info(inference_state, sys_path=None, full_name=None, **kwargs):
+    """
+    Returns Tuple[Union[NamespaceInfo, FileIO, None], Optional[bool]]
+    """
+    if sys_path is not None:
+        sys.path, temp = sys_path, sys.path
+    try:
+        return _find_module(full_name=full_name, **kwargs)
+    except ImportError:
+        return None, None
+    finally:
+        if sys_path is not None:
+            sys.path = temp
+
+
+def get_builtin_module_names(inference_state):
+    return sys.builtin_module_names
+
+
+def _test_raise_error(inference_state, exception_type):
+    """
+    Raise an error to simulate certain problems for unit tests.
+    """
+    raise exception_type
+
+
+def _test_print(inference_state, stderr=None, stdout=None):
+    """
+    Force some prints in the subprocesses. This exists for unit tests.
+    """
+    if stderr is not None:
+        print(stderr, file=sys.stderr)
+        sys.stderr.flush()
+    if stdout is not None:
+        print(stdout)
+        sys.stdout.flush()
+
+
+def _get_init_path(directory_path):
+    """
+    The __init__ file can be searched in a directory. If found return it, else
+    None.
+    """
+    for suffix in all_suffixes():
+        path = os.path.join(directory_path, '__init__' + suffix)
+        if os.path.exists(path):
+            return path
+    return None
+
+
+def safe_literal_eval(inference_state, value):
+    return parser_utils.safe_literal_eval(value)
+
+
+def iter_module_names(*args, **kwargs):
+    return list(_iter_module_names(*args, **kwargs))
+
+
+def _iter_module_names(inference_state, paths):
+    # Python modules/packages
+    for path in paths:
+        try:
+            dir_entries = ((entry.name, entry.is_dir()) for entry in os.scandir(path))
+        except OSError:
+            try:
+                zip_import_info = zipimporter(path)
+                # Unfortunately, there is no public way to access zipimporter's
+                # private _files member. We therefore have to use a
+                # custom function to iterate over the files.
+                dir_entries = _zip_list_subdirectory(
+                    zip_import_info.archive, zip_import_info.prefix)
+            except ZipImportError:
+                # The file might not exist or reading it might lead to an error.
+                debug.warning("Not possible to list directory: %s", path)
+                continue
+        for name, is_dir in dir_entries:
+            # First Namespaces then modules/stubs
+            if is_dir:
+                # pycache is obviously not an interesting namespace. Also the
+                # name must be a valid identifier.
+                if name != '__pycache__' and name.isidentifier():
+                    yield name
+            else:
+                if name.endswith('.pyi'):  # Stub files
+                    modname = name[:-4]
+                else:
+                    modname = inspect.getmodulename(name)
+
+                if modname and '.' not in modname:
+                    if modname != '__init__':
+                        yield modname
+
+
+def _find_module(string, path=None, full_name=None, is_global_search=True):
+    """
+    Provides information about a module.
+
+    This function isolates the differences in importing libraries introduced with
+    python 3.3 on; it gets a module name and optionally a path. It will return a
+    tuple containin an open file for the module (if not builtin), the filename
+    or the name of the module if it is a builtin one and a boolean indicating
+    if the module is contained in a package.
+    """
+    spec = None
+    loader = None
+
+    for finder in sys.meta_path:
+        if is_global_search and finder != importlib.machinery.PathFinder:
+            p = None
+        else:
+            p = path
+        try:
+            find_spec = finder.find_spec
+        except AttributeError:
+            # These are old-school clases that still have a different API, just
+            # ignore those.
+            continue
+
+        spec = find_spec(string, p)
+        if spec is not None:
+            if spec.origin == "frozen":
+                continue
+
+            loader = spec.loader
+
+            if loader is None and not spec.has_location:
+                # This is a namespace package.
+                full_name = string if not path else full_name
+                implicit_ns_info = ImplicitNSInfo(full_name, spec.submodule_search_locations._path)
+                return implicit_ns_info, True
+            break
+
+    return _find_module_py33(string, path, loader)
+
+
+def _find_module_py33(string, path=None, loader=None, full_name=None, is_global_search=True):
+    if not loader:
+        spec = importlib.machinery.PathFinder.find_spec(string, path)
+        if spec is not None:
+            loader = spec.loader
+
+    if loader is None and path is None:  # Fallback to find builtins
+        try:
+            spec = importlib.util.find_spec(string)
+            if spec is not None:
+                loader = spec.loader
+        except ValueError as e:
+            # See #491. Importlib might raise a ValueError, to avoid this, we
+            # just raise an ImportError to fix the issue.
+            raise ImportError("Originally  " + repr(e))
+
+    if loader is None:
+        raise ImportError("Couldn't find a loader for {}".format(string))
+
+    return _from_loader(loader, string)
+
+
+def _from_loader(loader, string):
+    try:
+        is_package_method = loader.is_package
+    except AttributeError:
+        is_package = False
+    else:
+        is_package = is_package_method(string)
+    try:
+        get_filename = loader.get_filename
+    except AttributeError:
+        return None, is_package
+    else:
+        module_path = get_filename(string)
+
+    # To avoid unicode and read bytes, "overwrite" loader.get_source if
+    # possible.
+    try:
+        f = type(loader).get_source
+    except AttributeError:
+        raise ImportError("get_source was not defined on loader")
+
+    if f is not importlib.machinery.SourceFileLoader.get_source:
+        # Unfortunately we are reading unicode here, not bytes.
+        # It seems hard to get bytes, because the zip importer
+        # logic just unpacks the zip file and returns a file descriptor
+        # that we cannot as easily access. Therefore we just read it as
+        # a string in the cases where get_source was overwritten.
+        code = loader.get_source(string)
+    else:
+        code = _get_source(loader, string)
+
+    if code is None:
+        return None, is_package
+    if isinstance(loader, zipimporter):
+        return ZipFileIO(module_path, code, Path(loader.archive)), is_package
+
+    return KnownContentFileIO(module_path, code), is_package
+
+
+def _get_source(loader, fullname):
+    """
+    This method is here as a replacement for SourceLoader.get_source. That
+    method returns unicode, but we prefer bytes.
+    """
+    path = loader.get_filename(fullname)
+    try:
+        return loader.get_data(path)
+    except OSError:
+        raise ImportError('source not available through get_data()',
+                          name=fullname)
+
+
+def _zip_list_subdirectory(zip_path, zip_subdir_path):
+    zip_file = ZipFile(zip_path)
+    zip_subdir_path = Path(zip_subdir_path)
+    zip_content_file_paths = zip_file.namelist()
+    for raw_file_name in zip_content_file_paths:
+        file_path = Path(raw_file_name)
+        if file_path.parent == zip_subdir_path:
+            file_path = file_path.relative_to(zip_subdir_path)
+            yield file_path.name, raw_file_name.endswith("/")
+
+
+class ImplicitNSInfo:
+    """Stores information returned from an implicit namespace spec"""
+    def __init__(self, name, paths):
+        self.name = name
+        self.paths = paths

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jedi/inference/compiled/value.py

@@ -0,0 +1,626 @@
+"""
+Imitate the parser representation.
+"""
+import re
+from functools import partial
+from inspect import Parameter
+from pathlib import Path
+from typing import Optional
+
+from jedi import debug
+from jedi.inference.utils import to_list
+from jedi.cache import memoize_method
+from jedi.inference.filters import AbstractFilter
+from jedi.inference.names import AbstractNameDefinition, ValueNameMixin, \
+    ParamNameInterface
+from jedi.inference.base_value import Value, ValueSet, NO_VALUES
+from jedi.inference.lazy_value import LazyKnownValue
+from jedi.inference.compiled.access import _sentinel
+from jedi.inference.cache import inference_state_function_cache
+from jedi.inference.helpers import reraise_getitem_errors
+from jedi.inference.signature import BuiltinSignature
+from jedi.inference.context import CompiledContext, CompiledModuleContext
+
+
+class CheckAttribute:
+    """Raises :exc:`AttributeError` if the attribute X is not available."""
+    def __init__(self, check_name=None):
+        # Remove the py in front of e.g. py__call__.
+        self.check_name = check_name
+
+    def __call__(self, func):
+        self.func = func
+        if self.check_name is None:
+            self.check_name = func.__name__[2:]
+        return self
+
+    def __get__(self, instance, owner):
+        if instance is None:
+            return self
+
+        # This might raise an AttributeError. That's wanted.
+        instance.access_handle.getattr_paths(self.check_name)
+        return partial(self.func, instance)
+
+
+class CompiledValue(Value):
+    def __init__(self, inference_state, access_handle, parent_context=None):
+        super().__init__(inference_state, parent_context)
+        self.access_handle = access_handle
+
+    def py__call__(self, arguments):
+        return_annotation = self.access_handle.get_return_annotation()
+        if return_annotation is not None:
+            return create_from_access_path(
+                self.inference_state,
+                return_annotation
+            ).execute_annotation()
+
+        try:
+            self.access_handle.getattr_paths('__call__')
+        except AttributeError:
+            return super().py__call__(arguments)
+        else:
+            if self.access_handle.is_class():
+                from jedi.inference.value import CompiledInstance
+                return ValueSet([
+                    CompiledInstance(self.inference_state, self.parent_context, self, arguments)
+                ])
+            else:
+                return ValueSet(self._execute_function(arguments))
+
+    @CheckAttribute()
+    def py__class__(self):
+        return create_from_access_path(self.inference_state, self.access_handle.py__class__())
+
+    @CheckAttribute()
+    def py__mro__(self):
+        return (self,) + tuple(
+            create_from_access_path(self.inference_state, access)
+            for access in self.access_handle.py__mro__accesses()
+        )
+
+    @CheckAttribute()
+    def py__bases__(self):
+        return tuple(
+            create_from_access_path(self.inference_state, access)
+            for access in self.access_handle.py__bases__()
+        )
+
+    def get_qualified_names(self):
+        return self.access_handle.get_qualified_names()
+
+    def py__bool__(self):
+        return self.access_handle.py__bool__()
+
+    def is_class(self):
+        return self.access_handle.is_class()
+
+    def is_function(self):
+        return self.access_handle.is_function()
+
+    def is_module(self):
+        return self.access_handle.is_module()
+
+    def is_compiled(self):
+        return True
+
+    def is_stub(self):
+        return False
+
+    def is_instance(self):
+        return self.access_handle.is_instance()
+
+    def py__doc__(self):
+        return self.access_handle.py__doc__()
+
+    @to_list
+    def get_param_names(self):
+        try:
+            signature_params = self.access_handle.get_signature_params()
+        except ValueError:  # Has no signature
+            params_str, ret = self._parse_function_doc()
+            if not params_str:
+                tokens = []
+            else:
+                tokens = params_str.split(',')
+            if self.access_handle.ismethoddescriptor():
+                tokens.insert(0, 'self')
+            for p in tokens:
+                name, _, default = p.strip().partition('=')
+                yield UnresolvableParamName(self, name, default)
+        else:
+            for signature_param in signature_params:
+                yield SignatureParamName(self, signature_param)
+
+    def get_signatures(self):
+        _, return_string = self._parse_function_doc()
+        return [BuiltinSignature(self, return_string)]
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self.access_handle.get_repr())
+
+    @memoize_method
+    def _parse_function_doc(self):
+        doc = self.py__doc__()
+        if doc is None:
+            return '', ''
+
+        return _parse_function_doc(doc)
+
+    @property
+    def api_type(self):
+        return self.access_handle.get_api_type()
+
+    def get_filters(self, is_instance=False, origin_scope=None):
+        yield self._ensure_one_filter(is_instance)
+
+    @memoize_method
+    def _ensure_one_filter(self, is_instance):
+        return CompiledValueFilter(self.inference_state, self, is_instance)
+
+    def py__simple_getitem__(self, index):
+        with reraise_getitem_errors(IndexError, KeyError, TypeError):
+            try:
+                access = self.access_handle.py__simple_getitem__(
+                    index,
+                    safe=not self.inference_state.allow_unsafe_executions
+                )
+            except AttributeError:
+                return super().py__simple_getitem__(index)
+        if access is None:
+            return super().py__simple_getitem__(index)
+
+        return ValueSet([create_from_access_path(self.inference_state, access)])
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        all_access_paths = self.access_handle.py__getitem__all_values()
+        if all_access_paths is None:
+            # This means basically that no __getitem__ has been defined on this
+            # object.
+            return super().py__getitem__(index_value_set, contextualized_node)
+        return ValueSet(
+            create_from_access_path(self.inference_state, access)
+            for access in all_access_paths
+        )
+
+    def py__iter__(self, contextualized_node=None):
+        if not self.access_handle.has_iter():
+            yield from super().py__iter__(contextualized_node)
+
+        access_path_list = self.access_handle.py__iter__list()
+        if access_path_list is None:
+            # There is no __iter__ method on this object.
+            return
+
+        for access in access_path_list:
+            yield LazyKnownValue(create_from_access_path(self.inference_state, access))
+
+    def py__name__(self):
+        return self.access_handle.py__name__()
+
+    @property
+    def name(self):
+        name = self.py__name__()
+        if name is None:
+            name = self.access_handle.get_repr()
+        return CompiledValueName(self, name)
+
+    def _execute_function(self, params):
+        from jedi.inference import docstrings
+        from jedi.inference.compiled import builtin_from_name
+        if self.api_type != 'function':
+            return
+
+        for name in self._parse_function_doc()[1].split():
+            try:
+                # TODO wtf is this? this is exactly the same as the thing
+                # below. It uses getattr as well.
+                self.inference_state.builtins_module.access_handle.getattr_paths(name)
+            except AttributeError:
+                continue
+            else:
+                bltn_obj = builtin_from_name(self.inference_state, name)
+                yield from self.inference_state.execute(bltn_obj, params)
+        yield from docstrings.infer_return_types(self)
+
+    def get_safe_value(self, default=_sentinel):
+        try:
+            return self.access_handle.get_safe_value()
+        except ValueError:
+            if default == _sentinel:
+                raise
+            return default
+
+    def execute_operation(self, other, operator):
+        try:
+            return ValueSet([create_from_access_path(
+                self.inference_state,
+                self.access_handle.execute_operation(other.access_handle, operator)
+            )])
+        except TypeError:
+            return NO_VALUES
+
+    def execute_annotation(self):
+        if self.access_handle.get_repr() == 'None':
+            # None as an annotation doesn't need to be executed.
+            return ValueSet([self])
+
+        name, args = self.access_handle.get_annotation_name_and_args()
+        arguments = [
+            ValueSet([create_from_access_path(self.inference_state, path)])
+            for path in args
+        ]
+        if name == 'Union':
+            return ValueSet.from_sets(arg.execute_annotation() for arg in arguments)
+        elif name:
+            # While with_generics only exists on very specific objects, we
+            # should probably be fine, because we control all the typing
+            # objects.
+            return ValueSet([
+                v.with_generics(arguments)
+                for v in self.inference_state.typing_module.py__getattribute__(name)
+            ]).execute_annotation()
+        return super().execute_annotation()
+
+    def negate(self):
+        return create_from_access_path(self.inference_state, self.access_handle.negate())
+
+    def get_metaclasses(self):
+        return NO_VALUES
+
+    def _as_context(self):
+        return CompiledContext(self)
+
+    @property
+    def array_type(self):
+        return self.access_handle.get_array_type()
+
+    def get_key_values(self):
+        return [
+            create_from_access_path(self.inference_state, k)
+            for k in self.access_handle.get_key_paths()
+        ]
+
+    def get_type_hint(self, add_class_info=True):
+        if self.access_handle.get_repr() in ('None', "<class 'NoneType'>"):
+            return 'None'
+        return None
+
+
+class CompiledModule(CompiledValue):
+    file_io = None  # For modules
+
+    def _as_context(self):
+        return CompiledModuleContext(self)
+
+    def py__path__(self):
+        return self.access_handle.py__path__()
+
+    def is_package(self):
+        return self.py__path__() is not None
+
+    @property
+    def string_names(self):
+        # For modules
+        name = self.py__name__()
+        if name is None:
+            return ()
+        return tuple(name.split('.'))
+
+    def py__file__(self) -> Optional[Path]:
+        return self.access_handle.py__file__()  # type: ignore[no-any-return]
+
+
+class CompiledName(AbstractNameDefinition):
+    def __init__(self, inference_state, parent_value, name, is_descriptor):
+        self._inference_state = inference_state
+        self.parent_context = parent_value.as_context()
+        self._parent_value = parent_value
+        self.string_name = name
+        self.is_descriptor = is_descriptor
+
+    def py__doc__(self):
+        return self.infer_compiled_value().py__doc__()
+
+    def _get_qualified_names(self):
+        parent_qualified_names = self.parent_context.get_qualified_names()
+        if parent_qualified_names is None:
+            return None
+        return parent_qualified_names + (self.string_name,)
+
+    def get_defining_qualified_value(self):
+        context = self.parent_context
+        if context.is_module() or context.is_class():
+            return self.parent_context.get_value()  # Might be None
+
+        return None
+
+    def __repr__(self):
+        try:
+            name = self.parent_context.name  # __name__ is not defined all the time
+        except AttributeError:
+            name = None
+        return '<%s: (%s).%s>' % (self.__class__.__name__, name, self.string_name)
+
+    @property
+    def api_type(self):
+        if self.is_descriptor:
+            # In case of properties we want to avoid executions as much as
+            # possible. Since the api_type can be wrong for other reasons
+            # anyway, we just return instance here.
+            return "instance"
+        return self.infer_compiled_value().api_type
+
+    def infer(self):
+        return ValueSet([self.infer_compiled_value()])
+
+    @memoize_method
+    def infer_compiled_value(self):
+        return create_from_name(self._inference_state, self._parent_value, self.string_name)
+
+
+class SignatureParamName(ParamNameInterface, AbstractNameDefinition):
+    def __init__(self, compiled_value, signature_param):
+        self.parent_context = compiled_value.parent_context
+        self._signature_param = signature_param
+
+    @property
+    def string_name(self):
+        return self._signature_param.name
+
+    def to_string(self):
+        s = self._kind_string() + self.string_name
+        if self._signature_param.has_annotation:
+            s += ': ' + self._signature_param.annotation_string
+        if self._signature_param.has_default:
+            s += '=' + self._signature_param.default_string
+        return s
+
+    def get_kind(self):
+        return getattr(Parameter, self._signature_param.kind_name)
+
+    def infer(self):
+        p = self._signature_param
+        inference_state = self.parent_context.inference_state
+        values = NO_VALUES
+        if p.has_default:
+            values = ValueSet([create_from_access_path(inference_state, p.default)])
+        if p.has_annotation:
+            annotation = create_from_access_path(inference_state, p.annotation)
+            values |= annotation.execute_with_values()
+        return values
+
+
+class UnresolvableParamName(ParamNameInterface, AbstractNameDefinition):
+    def __init__(self, compiled_value, name, default):
+        self.parent_context = compiled_value.parent_context
+        self.string_name = name
+        self._default = default
+
+    def get_kind(self):
+        return Parameter.POSITIONAL_ONLY
+
+    def to_string(self):
+        string = self.string_name
+        if self._default:
+            string += '=' + self._default
+        return string
+
+    def infer(self):
+        return NO_VALUES
+
+
+class CompiledValueName(ValueNameMixin, AbstractNameDefinition):
+    def __init__(self, value, name):
+        self.string_name = name
+        self._value = value
+        self.parent_context = value.parent_context
+
+
+class EmptyCompiledName(AbstractNameDefinition):
+    """
+    Accessing some names will raise an exception. To avoid not having any
+    completions, just give Jedi the option to return this object. It infers to
+    nothing.
+    """
+    def __init__(self, inference_state, name):
+        self.parent_context = inference_state.builtins_module
+        self.string_name = name
+
+    def infer(self):
+        return NO_VALUES
+
+
+class CompiledValueFilter(AbstractFilter):
+    def __init__(self, inference_state, compiled_value, is_instance=False):
+        self._inference_state = inference_state
+        self.compiled_value = compiled_value
+        self.is_instance = is_instance
+
+    def get(self, name):
+        access_handle = self.compiled_value.access_handle
+        safe = not self._inference_state.allow_unsafe_executions
+        return self._get(
+            name,
+            lambda name: access_handle.is_allowed_getattr(name, safe=safe),
+            lambda name: name in access_handle.dir(),
+            check_has_attribute=True
+        )
+
+    def _get(self, name, allowed_getattr_callback, in_dir_callback, check_has_attribute=False):
+        """
+        To remove quite a few access calls we introduced the callback here.
+        """
+        has_attribute, is_descriptor, property_return_annotation = allowed_getattr_callback(
+            name,
+        )
+        if property_return_annotation is not None:
+            values = create_from_access_path(
+                self._inference_state,
+                property_return_annotation
+            ).execute_annotation()
+            if values:
+                return [CompiledValueName(v, name) for v in values]
+
+        if check_has_attribute and not has_attribute:
+            return []
+
+        if (is_descriptor or not has_attribute) \
+                and not self._inference_state.allow_unsafe_executions:
+            return [self._get_cached_name(name, is_empty=True)]
+
+        if self.is_instance and not in_dir_callback(name):
+            return []
+        return [self._get_cached_name(name, is_descriptor=is_descriptor)]
+
+    @memoize_method
+    def _get_cached_name(self, name, is_empty=False, *, is_descriptor=False):
+        if is_empty:
+            return EmptyCompiledName(self._inference_state, name)
+        else:
+            return self._create_name(name, is_descriptor=is_descriptor)
+
+    def values(self):
+        from jedi.inference.compiled import builtin_from_name
+        names = []
+        needs_type_completions, dir_infos = self.compiled_value.access_handle.get_dir_infos()
+        # We could use `safe=False` here as well, especially as a parameter to
+        # get_dir_infos. But this would lead to a lot of property executions
+        # that are probably not wanted. The drawback for this is that we
+        # have a different name for `get` and `values`. For `get` we always
+        # execute.
+        for name in dir_infos:
+            names += self._get(
+                name,
+                lambda name: dir_infos[name],
+                lambda name: name in dir_infos,
+            )
+
+        # ``dir`` doesn't include the type names.
+        if not self.is_instance and needs_type_completions:
+            for filter in builtin_from_name(self._inference_state, 'type').get_filters():
+                names += filter.values()
+        return names
+
+    def _create_name(self, name, is_descriptor):
+        return CompiledName(
+            self._inference_state,
+            self.compiled_value,
+            name,
+            is_descriptor,
+        )
+
+    def __repr__(self):
+        return "<%s: %s>" % (self.__class__.__name__, self.compiled_value)
+
+
+docstr_defaults = {
+    'floating point number': 'float',
+    'character': 'str',
+    'integer': 'int',
+    'dictionary': 'dict',
+    'string': 'str',
+}
+
+
+def _parse_function_doc(doc):
+    """
+    Takes a function and returns the params and return value as a tuple.
+    This is nothing more than a docstring parser.
+
+    TODO docstrings like utime(path, (atime, mtime)) and a(b [, b]) -> None
+    TODO docstrings like 'tuple of integers'
+    """
+    # parse round parentheses: def func(a, (b,c))
+    try:
+        count = 0
+        start = doc.index('(')
+        for i, s in enumerate(doc[start:]):
+            if s == '(':
+                count += 1
+            elif s == ')':
+                count -= 1
+            if count == 0:
+                end = start + i
+                break
+        param_str = doc[start + 1:end]
+    except (ValueError, UnboundLocalError):
+        # ValueError for doc.index
+        # UnboundLocalError for undefined end in last line
+        debug.dbg('no brackets found - no param')
+        end = 0
+        param_str = ''
+    else:
+        # remove square brackets, that show an optional param ( = None)
+        def change_options(m):
+            args = m.group(1).split(',')
+            for i, a in enumerate(args):
+                if a and '=' not in a:
+                    args[i] += '=None'
+            return ','.join(args)
+
+        while True:
+            param_str, changes = re.subn(r' ?\[([^\[\]]+)\]',
+                                         change_options, param_str)
+            if changes == 0:
+                break
+    param_str = param_str.replace('-', '_')  # see: isinstance.__doc__
+
+    # parse return value
+    r = re.search('-[>-]* ', doc[end:end + 7])
+    if r is None:
+        ret = ''
+    else:
+        index = end + r.end()
+        # get result type, which can contain newlines
+        pattern = re.compile(r'(,\n|[^\n-])+')
+        ret_str = pattern.match(doc, index).group(0).strip()
+        # New object -> object()
+        ret_str = re.sub(r'[nN]ew (.*)', r'\1()', ret_str)
+
+        ret = docstr_defaults.get(ret_str, ret_str)
+
+    return param_str, ret
+
+
+def create_from_name(inference_state, compiled_value, name):
+    access_paths = compiled_value.access_handle.getattr_paths(name, default=None)
+
+    value = None
+    for access_path in access_paths:
+        value = create_cached_compiled_value(
+            inference_state,
+            access_path,
+            parent_context=None if value is None else value.as_context(),
+        )
+    return value
+
+
+def _normalize_create_args(func):
+    """The cache doesn't care about keyword vs. normal args."""
+    def wrapper(inference_state, obj, parent_context=None):
+        return func(inference_state, obj, parent_context)
+    return wrapper
+
+
+def create_from_access_path(inference_state, access_path):
+    value = None
+    for name, access in access_path.accesses:
+        value = create_cached_compiled_value(
+            inference_state,
+            access,
+            parent_context=None if value is None else value.as_context()
+        )
+    return value
+
+
+@_normalize_create_args
+@inference_state_function_cache()
+def create_cached_compiled_value(inference_state, access_handle, parent_context):
+    assert not isinstance(parent_context, CompiledValue)
+    if parent_context is None:
+        cls = CompiledModule
+    else:
+        cls = CompiledValue
+    return cls(inference_state, access_handle, parent_context)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_backports.py

@@ -0,0 +1,35 @@
+import mmap
+
+from joblib.backports import make_memmap, concurrency_safe_rename
+from joblib.test.common import with_numpy
+from joblib.testing import parametrize
+from joblib import Parallel, delayed
+
+
+@with_numpy
+def test_memmap(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    memmap_obj = make_memmap(fname, shape=size, mode='w+', offset=offset)
+    assert memmap_obj.offset == offset
+
+
+@parametrize('dst_content', [None, 'dst content'])
+@parametrize('backend', [None, 'threading'])
+def test_concurrency_safe_rename(tmpdir, dst_content, backend):
+    src_paths = [tmpdir.join('src_%d' % i) for i in range(4)]
+    for src_path in src_paths:
+        src_path.write('src content')
+    dst_path = tmpdir.join('dst')
+    if dst_content is not None:
+        dst_path.write(dst_content)
+
+    Parallel(n_jobs=4, backend=backend)(
+        delayed(concurrency_safe_rename)(src_path.strpath, dst_path.strpath)
+        for src_path in src_paths
+    )
+    assert dst_path.exists()
+    assert dst_path.read() == 'src content'
+    for src_path in src_paths:
+        assert not src_path.exists()

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_config.py

@@ -0,0 +1,151 @@
+import os
+
+from joblib.parallel import parallel_config
+from joblib.parallel import parallel_backend
+from joblib.parallel import Parallel, delayed
+
+from joblib.parallel import BACKENDS
+from joblib.parallel import DEFAULT_BACKEND
+from joblib.parallel import EXTERNAL_BACKENDS
+
+from joblib._parallel_backends import LokyBackend
+from joblib._parallel_backends import ThreadingBackend
+from joblib._parallel_backends import MultiprocessingBackend
+
+from joblib.testing import parametrize, raises
+from joblib.test.common import np, with_numpy
+from joblib.test.common import with_multiprocessing
+from joblib.test.test_parallel import check_memmap
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_global_parallel_backend(context):
+    default = Parallel()._backend
+
+    pb = context('threading')
+    try:
+        assert isinstance(Parallel()._backend, ThreadingBackend)
+    finally:
+        pb.unregister()
+    assert type(Parallel()._backend) is type(default)
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_external_backends(context):
+    def register_foo():
+        BACKENDS['foo'] = ThreadingBackend
+
+    EXTERNAL_BACKENDS['foo'] = register_foo
+    try:
+        with context('foo'):
+            assert isinstance(Parallel()._backend, ThreadingBackend)
+    finally:
+        del EXTERNAL_BACKENDS['foo']
+
+
+@with_numpy
+@with_multiprocessing
+def test_parallel_config_no_backend(tmpdir):
+    # Check that parallel_config allows to change the config
+    # even if no backend is set.
+    with parallel_config(n_jobs=2, max_nbytes=1, temp_folder=tmpdir):
+        with Parallel(prefer="processes") as p:
+            assert isinstance(p._backend, LokyBackend)
+            assert p.n_jobs == 2
+
+            # Checks that memmapping is enabled
+            p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+            assert len(os.listdir(tmpdir)) > 0
+
+
+@with_numpy
+@with_multiprocessing
+def test_parallel_config_params_explicit_set(tmpdir):
+    with parallel_config(n_jobs=3, max_nbytes=1, temp_folder=tmpdir):
+        with Parallel(n_jobs=2, prefer="processes", max_nbytes='1M') as p:
+            assert isinstance(p._backend, LokyBackend)
+            assert p.n_jobs == 2
+
+            # Checks that memmapping is disabled
+            with raises(TypeError, match="Expected np.memmap instance"):
+                p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+
+
+@parametrize("param", ["prefer", "require"])
+def test_parallel_config_bad_params(param):
+    # Check that an error is raised when setting a wrong backend
+    # hint or constraint
+    with raises(ValueError, match=f"{param}=wrong is not a valid"):
+        with parallel_config(**{param: "wrong"}):
+            Parallel()
+
+
+def test_parallel_config_constructor_params():
+    # Check that an error is raised when backend is None
+    # but backend constructor params are given
+    with raises(ValueError, match="only supported when backend is not None"):
+        with parallel_config(inner_max_num_threads=1):
+            pass
+
+    with raises(ValueError, match="only supported when backend is not None"):
+        with parallel_config(backend_param=1):
+            pass
+
+
+def test_parallel_config_nested():
+    # Check that nested configuration retrieves the info from the
+    # parent config and do not reset them.
+
+    with parallel_config(n_jobs=2):
+        p = Parallel()
+        assert isinstance(p._backend, BACKENDS[DEFAULT_BACKEND])
+        assert p.n_jobs == 2
+
+    with parallel_config(backend='threading'):
+        with parallel_config(n_jobs=2):
+            p = Parallel()
+            assert isinstance(p._backend, ThreadingBackend)
+            assert p.n_jobs == 2
+
+    with parallel_config(verbose=100):
+        with parallel_config(n_jobs=2):
+            p = Parallel()
+            assert p.verbose == 100
+            assert p.n_jobs == 2
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', ['multiprocessing', 'threading',
+                         MultiprocessingBackend(), ThreadingBackend()])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_context_error(context, backend):
+
+    with raises(AssertionError, match=r"does not acc.*inner_max_num_threads"):
+        context(backend, inner_max_num_threads=1)
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parallel_n_jobs_none(context):
+    # Check that n_jobs=None is interpreted as "unset" in Parallel
+    # non regression test for #1473
+    with context(backend="threading", n_jobs=2):
+        with Parallel(n_jobs=None) as p:
+            assert p.n_jobs == 2
+
+    with context(backend="threading"):
+        default_n_jobs = Parallel().n_jobs
+        with Parallel(n_jobs=None) as p:
+            assert p.n_jobs == default_n_jobs
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parallel_config_n_jobs_none(context):
+    # Check that n_jobs=None is interpreted as "explicitly set" in
+    # parallel_(config/backend)
+    # non regression test for #1473
+    with context(backend="threading", n_jobs=2):
+        with context(backend="threading", n_jobs=None):
+            # n_jobs=None resets n_jobs to backend's default
+            with Parallel() as p:
+                assert p.n_jobs == 1

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_dask.py

@@ -0,0 +1,499 @@
+from __future__ import print_function, division, absolute_import
+import os
+import warnings
+
+import pytest
+from random import random
+from uuid import uuid4
+from time import sleep
+
+from .. import Parallel, delayed, parallel_config
+from ..parallel import ThreadingBackend, AutoBatchingMixin
+from .._dask import DaskDistributedBackend
+
+distributed = pytest.importorskip('distributed')
+dask = pytest.importorskip('dask')
+
+# These imports need to be after the pytest.importorskip hence the noqa: E402
+from distributed import Client, LocalCluster, get_client  # noqa: E402
+from distributed.metrics import time  # noqa: E402
+# Note: pytest requires to manually import all fixtures used in the test
+# and their dependencies.
+from distributed.utils_test import cluster, inc, cleanup  # noqa: E402, F401
+
+
+def noop(*args, **kwargs):
+    pass
+
+
+def slow_raise_value_error(condition, duration=0.05):
+    sleep(duration)
+    if condition:
+        raise ValueError("condition evaluated to True")
+
+
+def count_events(event_name, client):
+    worker_events = client.run(lambda dask_worker: dask_worker.log)
+    event_counts = {}
+    for w, events in worker_events.items():
+        event_counts[w] = len([event for event in list(events)
+                               if event[1] == event_name])
+    return event_counts
+
+
+def test_simple(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                seq = Parallel()(delayed(inc)(i) for i in range(10))
+                assert seq == [inc(i) for i in range(10)]
+
+                with pytest.raises(ValueError):
+                    Parallel()(delayed(slow_raise_value_error)(i == 3)
+                               for i in range(10))
+
+                seq = Parallel()(delayed(inc)(i) for i in range(10))
+                assert seq == [inc(i) for i in range(10)]
+
+
+def test_dask_backend_uses_autobatching(loop):
+    assert (DaskDistributedBackend.compute_batch_size
+            is AutoBatchingMixin.compute_batch_size)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                with Parallel() as parallel:
+                    # The backend should be initialized with a default
+                    # batch size of 1:
+                    backend = parallel._backend
+                    assert isinstance(backend, DaskDistributedBackend)
+                    assert backend.parallel is parallel
+                    assert backend._effective_batch_size == 1
+
+                    # Launch many short tasks that should trigger
+                    # auto-batching:
+                    parallel(
+                        delayed(lambda: None)()
+                        for _ in range(int(1e4))
+                    )
+                    assert backend._effective_batch_size > 10
+
+
+def random2():
+    return random()
+
+
+def test_dont_assume_function_purity(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                x, y = Parallel()(delayed(random2)() for i in range(2))
+                assert x != y
+
+
+@pytest.mark.parametrize("mixed", [True, False])
+def test_dask_funcname(loop, mixed):
+    from joblib._dask import Batch
+    if not mixed:
+        tasks = [delayed(inc)(i) for i in range(4)]
+        batch_repr = 'batch_of_inc_4_calls'
+    else:
+        tasks = [
+            delayed(abs)(i) if i % 2 else delayed(inc)(i) for i in range(4)
+        ]
+        batch_repr = 'mixed_batch_of_inc_4_calls'
+
+    assert repr(Batch(tasks)) == batch_repr
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:
+            with parallel_config(backend='dask'):
+                _ = Parallel(batch_size=2, pre_dispatch='all')(tasks)
+
+            def f(dask_scheduler):
+                return list(dask_scheduler.transition_log)
+            batch_repr = batch_repr.replace('4', '2')
+            log = client.run_on_scheduler(f)
+            assert all('batch_of_inc' in tup[0] for tup in log)
+
+
+def test_no_undesired_distributed_cache_hit():
+    # Dask has a pickle cache for callables that are called many times. Because
+    # the dask backends used to wrap both the functions and the arguments
+    # under instances of the Batch callable class this caching mechanism could
+    # lead to bugs as described in: https://github.com/joblib/joblib/pull/1055
+    # The joblib-dask backend has been refactored to avoid bundling the
+    # arguments as an attribute of the Batch instance to avoid this problem.
+    # This test serves as non-regression problem.
+
+    # Use a large number of input arguments to give the AutoBatchingMixin
+    # enough tasks to kick-in.
+    lists = [[] for _ in range(100)]
+    np = pytest.importorskip('numpy')
+    X = np.arange(int(1e6))
+
+    def isolated_operation(list_, data=None):
+        if data is not None:
+            np.testing.assert_array_equal(data, X)
+        list_.append(uuid4().hex)
+        return list_
+
+    cluster = LocalCluster(n_workers=1, threads_per_worker=2)
+    client = Client(cluster)
+    try:
+        with parallel_config(backend='dask'):
+            # dispatches joblib.parallel.BatchedCalls
+            res = Parallel()(
+                delayed(isolated_operation)(list_) for list_ in lists
+            )
+
+        # The original arguments should not have been mutated as the mutation
+        # happens in the dask worker process.
+        assert lists == [[] for _ in range(100)]
+
+        # Here we did not pass any large numpy array as argument to
+        # isolated_operation so no scattering event should happen under the
+        # hood.
+        counts = count_events('receive-from-scatter', client)
+        assert sum(counts.values()) == 0
+        assert all([len(r) == 1 for r in res])
+
+        with parallel_config(backend='dask'):
+            # Append a large array which will be scattered by dask, and
+            # dispatch joblib._dask.Batch
+            res = Parallel()(
+                delayed(isolated_operation)(list_, data=X) for list_ in lists
+            )
+
+        # This time, auto-scattering should have kicked it.
+        counts = count_events('receive-from-scatter', client)
+        assert sum(counts.values()) > 0
+        assert all([len(r) == 1 for r in res])
+    finally:
+        client.close(timeout=30)
+        cluster.close(timeout=30)
+
+
+class CountSerialized(object):
+    def __init__(self, x):
+        self.x = x
+        self.count = 0
+
+    def __add__(self, other):
+        return self.x + getattr(other, 'x', other)
+
+    __radd__ = __add__
+
+    def __reduce__(self):
+        self.count += 1
+        return (CountSerialized, (self.x,))
+
+
+def add5(a, b, c, d=0, e=0):
+    return a + b + c + d + e
+
+
+def test_manual_scatter(loop):
+    x = CountSerialized(1)
+    y = CountSerialized(2)
+    z = CountSerialized(3)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask', scatter=[x, y]):
+                f = delayed(add5)
+                tasks = [f(x, y, z, d=4, e=5),
+                         f(x, z, y, d=5, e=4),
+                         f(y, x, z, d=x, e=5),
+                         f(z, z, x, d=z, e=y)]
+                expected = [func(*args, **kwargs)
+                            for func, args, kwargs in tasks]
+                results = Parallel()(tasks)
+
+            # Scatter must take a list/tuple
+            with pytest.raises(TypeError):
+                with parallel_config(backend='dask', loop=loop, scatter=1):
+                    pass
+
+    assert results == expected
+
+    # Scattered variables only serialized once
+    assert x.count == 1
+    assert y.count == 1
+    # Depending on the version of distributed, the unscattered z variable
+    # is either pickled 4 or 6 times, possibly because of the memoization
+    # of objects that appear several times in the arguments of a delayed
+    # task.
+    assert z.count in (4, 6)
+
+
+# When the same IOLoop is used for multiple clients in a row, use
+# loop_in_thread instead of loop to prevent the Client from closing it.  See
+# dask/distributed #4112
+def test_auto_scatter(loop_in_thread):
+    np = pytest.importorskip('numpy')
+    data1 = np.ones(int(1e4), dtype=np.uint8)
+    data2 = np.ones(int(1e4), dtype=np.uint8)
+    data_to_process = ([data1] * 3) + ([data2] * 3)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                # Passing the same data as arg and kwarg triggers a single
+                # scatter operation whose result is reused.
+                Parallel()(delayed(noop)(data, data, i, opt=data)
+                           for i, data in enumerate(data_to_process))
+            # By default large array are automatically scattered with
+            # broadcast=1 which means that one worker must directly receive
+            # the data from the scatter operation once.
+            counts = count_events('receive-from-scatter', client)
+            assert counts[a['address']] + counts[b['address']] == 2
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                Parallel()(delayed(noop)(data1[:3], i) for i in range(5))
+            # Small arrays are passed within the task definition without going
+            # through a scatter operation.
+            counts = count_events('receive-from-scatter', client)
+            assert counts[a['address']] == 0
+            assert counts[b['address']] == 0
+
+
+@pytest.mark.parametrize("retry_no", list(range(2)))
+def test_nested_scatter(loop, retry_no):
+
+    np = pytest.importorskip('numpy')
+
+    NUM_INNER_TASKS = 10
+    NUM_OUTER_TASKS = 10
+
+    def my_sum(x, i, j):
+        return np.sum(x)
+
+    def outer_function_joblib(array, i):
+        client = get_client()  # noqa
+        with parallel_config(backend="dask"):
+            results = Parallel()(
+                delayed(my_sum)(array[j:], i, j) for j in range(
+                    NUM_INNER_TASKS)
+            )
+        return sum(results)
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as _:
+            with parallel_config(backend="dask"):
+                my_array = np.ones(10000)
+                _ = Parallel()(
+                    delayed(outer_function_joblib)(
+                        my_array[i:], i) for i in range(NUM_OUTER_TASKS)
+                )
+
+
+def test_nested_backend_context_manager(loop_in_thread):
+    def get_nested_pids():
+        pids = set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
+        pids |= set(Parallel(n_jobs=2)(delayed(os.getpid)() for _ in range(2)))
+        return pids
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop_in_thread) as client:
+            with parallel_config(backend='dask'):
+                pid_groups = Parallel(n_jobs=2)(
+                    delayed(get_nested_pids)()
+                    for _ in range(10)
+                )
+                for pid_group in pid_groups:
+                    assert len(set(pid_group)) <= 2
+
+        # No deadlocks
+        with Client(s['address'], loop=loop_in_thread) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                pid_groups = Parallel(n_jobs=2)(
+                    delayed(get_nested_pids)()
+                    for _ in range(10)
+                )
+                for pid_group in pid_groups:
+                    assert len(set(pid_group)) <= 2
+
+
+def test_nested_backend_context_manager_implicit_n_jobs(loop):
+    # Check that Parallel with no explicit n_jobs value automatically selects
+    # all the dask workers, including in nested calls.
+
+    def _backend_type(p):
+        return p._backend.__class__.__name__
+
+    def get_nested_implicit_n_jobs():
+        with Parallel() as p:
+            return _backend_type(p), p.n_jobs
+
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask'):
+                with Parallel() as p:
+                    assert _backend_type(p) == "DaskDistributedBackend"
+                    assert p.n_jobs == -1
+                    all_nested_n_jobs = p(
+                        delayed(get_nested_implicit_n_jobs)()
+                        for _ in range(2)
+                    )
+                for backend_type, nested_n_jobs in all_nested_n_jobs:
+                    assert backend_type == "DaskDistributedBackend"
+                    assert nested_n_jobs == -1
+
+
+def test_errors(loop):
+    with pytest.raises(ValueError) as info:
+        with parallel_config(backend='dask'):
+            pass
+
+    assert "create a dask client" in str(info.value).lower()
+
+
+def test_correct_nested_backend(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            # No requirement, should be us
+            with parallel_config(backend='dask'):
+                result = Parallel(n_jobs=2)(
+                    delayed(outer)(nested_require=None) for _ in range(1))
+                assert isinstance(result[0][0][0], DaskDistributedBackend)
+
+            # Require threads, should be threading
+            with parallel_config(backend='dask'):
+                result = Parallel(n_jobs=2)(
+                    delayed(outer)(nested_require='sharedmem')
+                    for _ in range(1))
+                assert isinstance(result[0][0][0], ThreadingBackend)
+
+
+def outer(nested_require):
+    return Parallel(n_jobs=2, prefer='threads')(
+        delayed(middle)(nested_require) for _ in range(1)
+    )
+
+
+def middle(require):
+    return Parallel(n_jobs=2, require=require)(
+        delayed(inner)() for _ in range(1)
+    )
+
+
+def inner():
+    return Parallel()._backend
+
+
+def test_secede_with_no_processes(loop):
+    # https://github.com/dask/distributed/issues/1775
+    with Client(loop=loop, processes=False, set_as_default=True):
+        with parallel_config(backend='dask'):
+            Parallel(n_jobs=4)(delayed(id)(i) for i in range(2))
+
+
+def _worker_address(_):
+    from distributed import get_worker
+    return get_worker().address
+
+
+def test_dask_backend_keywords(loop):
+    with cluster() as (s, [a, b]):
+        with Client(s['address'], loop=loop) as client:  # noqa: F841
+            with parallel_config(backend='dask', workers=a['address']):
+                seq = Parallel()(
+                    delayed(_worker_address)(i) for i in range(10))
+                assert seq == [a['address']] * 10
+
+            with parallel_config(backend='dask', workers=b['address']):
+                seq = Parallel()(
+                    delayed(_worker_address)(i) for i in range(10))
+                assert seq == [b['address']] * 10
+
+
+def test_scheduler_tasks_cleanup(loop):
+    with Client(processes=False, loop=loop) as client:
+        with parallel_config(backend='dask'):
+            Parallel()(delayed(inc)(i) for i in range(10))
+
+        start = time()
+        while client.cluster.scheduler.tasks:
+            sleep(0.01)
+            assert time() < start + 5
+
+        assert not client.futures
+
+
+@pytest.mark.parametrize("cluster_strategy", ["adaptive", "late_scaling"])
+@pytest.mark.skipif(
+    distributed.__version__ <= '2.1.1' and distributed.__version__ >= '1.28.0',
+    reason="distributed bug - https://github.com/dask/distributed/pull/2841")
+def test_wait_for_workers(cluster_strategy):
+    cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
+    client = Client(cluster)
+    if cluster_strategy == "adaptive":
+        cluster.adapt(minimum=0, maximum=2)
+    elif cluster_strategy == "late_scaling":
+        # Tell the cluster to start workers but this is a non-blocking call
+        # and new workers might take time to connect. In this case the Parallel
+        # call should wait for at least one worker to come up before starting
+        # to schedule work.
+        cluster.scale(2)
+    try:
+        with parallel_config(backend='dask'):
+            # The following should wait a bit for at least one worker to
+            # become available.
+            Parallel()(delayed(inc)(i) for i in range(10))
+    finally:
+        client.close()
+        cluster.close()
+
+
+def test_wait_for_workers_timeout():
+    # Start a cluster with 0 worker:
+    cluster = LocalCluster(n_workers=0, processes=False, threads_per_worker=2)
+    client = Client(cluster)
+    try:
+        with parallel_config(backend='dask', wait_for_workers_timeout=0.1):
+            # Short timeout: DaskDistributedBackend
+            msg = "DaskDistributedBackend has no worker after 0.1 seconds."
+            with pytest.raises(TimeoutError, match=msg):
+                Parallel()(delayed(inc)(i) for i in range(10))
+
+        with parallel_config(backend='dask', wait_for_workers_timeout=0):
+            # No timeout: fallback to generic joblib failure:
+            msg = "DaskDistributedBackend has no active worker"
+            with pytest.raises(RuntimeError, match=msg):
+                Parallel()(delayed(inc)(i) for i in range(10))
+    finally:
+        client.close()
+        cluster.close()
+
+
+@pytest.mark.parametrize("backend", ["loky", "multiprocessing"])
+def test_joblib_warning_inside_dask_daemonic_worker(backend):
+    cluster = LocalCluster(n_workers=2)
+    client = Client(cluster)
+    try:
+
+        def func_using_joblib_parallel():
+            # Somehow trying to check the warning type here (e.g. with
+            # pytest.warns(UserWarning)) make the test hang. Work-around:
+            # return the warning record to the client and the warning check is
+            # done client-side.
+            with warnings.catch_warnings(record=True) as record:
+                Parallel(n_jobs=2, backend=backend)(
+                    delayed(inc)(i) for i in range(10))
+
+            return record
+
+        fut = client.submit(func_using_joblib_parallel)
+        record = fut.result()
+
+        assert len(record) == 1
+        warning = record[0].message
+        assert isinstance(warning, UserWarning)
+        assert "distributed.worker.daemon" in str(warning)
+    finally:
+        client.close(timeout=30)
+        cluster.close(timeout=30)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_hashing.py

@@ -0,0 +1,495 @@
+"""
+Test the hashing module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import time
+import hashlib
+import sys
+import gc
+import io
+import collections
+import itertools
+import pickle
+import random
+from concurrent.futures import ProcessPoolExecutor
+from decimal import Decimal
+
+from joblib.hashing import hash
+from joblib.func_inspect import filter_args
+from joblib.memory import Memory
+from joblib.testing import raises, skipif, fixture, parametrize
+from joblib.test.common import np, with_numpy
+
+
+def unicode(s):
+    return s
+
+
+###############################################################################
+# Helper functions for the tests
+def time_func(func, *args):
+    """ Time function func on *args.
+    """
+    times = list()
+    for _ in range(3):
+        t1 = time.time()
+        func(*args)
+        times.append(time.time() - t1)
+    return min(times)
+
+
+def relative_time(func1, func2, *args):
+    """ Return the relative time between func1 and func2 applied on
+        *args.
+    """
+    time_func1 = time_func(func1, *args)
+    time_func2 = time_func(func2, *args)
+    relative_diff = 0.5 * (abs(time_func1 - time_func2)
+                           / (time_func1 + time_func2))
+    return relative_diff
+
+
+class Klass(object):
+
+    def f(self, x):
+        return x
+
+
+class KlassWithCachedMethod(object):
+
+    def __init__(self, cachedir):
+        mem = Memory(location=cachedir)
+        self.f = mem.cache(self.f)
+
+    def f(self, x):
+        return x
+
+
+###############################################################################
+# Tests
+
+input_list = [1, 2, 1., 2., 1 + 1j, 2. + 1j,
+              'a', 'b',
+              (1,), (1, 1,), [1, ], [1, 1, ],
+              {1: 1}, {1: 2}, {2: 1},
+              None,
+              gc.collect,
+              [1, ].append,
+              # Next 2 sets have unorderable elements in python 3.
+              set(('a', 1)),
+              set(('a', 1, ('a', 1))),
+              # Next 2 dicts have unorderable type of keys in python 3.
+              {'a': 1, 1: 2},
+              {'a': 1, 1: 2, 'd': {'a': 1}}]
+
+
+@parametrize('obj1', input_list)
+@parametrize('obj2', input_list)
+def test_trivial_hash(obj1, obj2):
+    """Smoke test hash on various types."""
+    # Check that 2 objects have the same hash only if they are the same.
+    are_hashes_equal = hash(obj1) == hash(obj2)
+    are_objs_identical = obj1 is obj2
+    assert are_hashes_equal == are_objs_identical
+
+
+def test_hash_methods():
+    # Check that hashing instance methods works
+    a = io.StringIO(unicode('a'))
+    assert hash(a.flush) == hash(a.flush)
+    a1 = collections.deque(range(10))
+    a2 = collections.deque(range(9))
+    assert hash(a1.extend) != hash(a2.extend)
+
+
+@fixture(scope='function')
+@with_numpy
+def three_np_arrays():
+    rnd = np.random.RandomState(0)
+    arr1 = rnd.random_sample((10, 10))
+    arr2 = arr1.copy()
+    arr3 = arr2.copy()
+    arr3[0] += 1
+    return arr1, arr2, arr3
+
+
+def test_hash_numpy_arrays(three_np_arrays):
+    arr1, arr2, arr3 = three_np_arrays
+
+    for obj1, obj2 in itertools.product(three_np_arrays, repeat=2):
+        are_hashes_equal = hash(obj1) == hash(obj2)
+        are_arrays_equal = np.all(obj1 == obj2)
+        assert are_hashes_equal == are_arrays_equal
+
+    assert hash(arr1) != hash(arr1.T)
+
+
+def test_hash_numpy_dict_of_arrays(three_np_arrays):
+    arr1, arr2, arr3 = three_np_arrays
+
+    d1 = {1: arr1, 2: arr2}
+    d2 = {1: arr2, 2: arr1}
+    d3 = {1: arr2, 2: arr3}
+
+    assert hash(d1) == hash(d2)
+    assert hash(d1) != hash(d3)
+
+
+@with_numpy
+@parametrize('dtype', ['datetime64[s]', 'timedelta64[D]'])
+def test_numpy_datetime_array(dtype):
+    # memoryview is not supported for some dtypes e.g. datetime64
+    # see https://github.com/joblib/joblib/issues/188 for more details
+    a_hash = hash(np.arange(10))
+    array = np.arange(0, 10, dtype=dtype)
+    assert hash(array) != a_hash
+
+
+@with_numpy
+def test_hash_numpy_noncontiguous():
+    a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
+                   order='F')[:, :1, :]
+    b = np.ascontiguousarray(a)
+    assert hash(a) != hash(b)
+
+    c = np.asfortranarray(a)
+    assert hash(a) != hash(c)
+
+
+@with_numpy
+@parametrize('coerce_mmap', [True, False])
+def test_hash_memmap(tmpdir, coerce_mmap):
+    """Check that memmap and arrays hash identically if coerce_mmap is True."""
+    filename = tmpdir.join('memmap_temp').strpath
+    try:
+        m = np.memmap(filename, shape=(10, 10), mode='w+')
+        a = np.asarray(m)
+        are_hashes_equal = (hash(a, coerce_mmap=coerce_mmap) ==
+                            hash(m, coerce_mmap=coerce_mmap))
+        assert are_hashes_equal == coerce_mmap
+    finally:
+        if 'm' in locals():
+            del m
+            # Force a garbage-collection cycle, to be certain that the
+            # object is delete, and we don't run in a problem under
+            # Windows with a file handle still open.
+            gc.collect()
+
+
+@with_numpy
+@skipif(sys.platform == 'win32', reason='This test is not stable under windows'
+                                        ' for some reason')
+def test_hash_numpy_performance():
+    """ Check the performance of hashing numpy arrays:
+
+        In [22]: a = np.random.random(1000000)
+
+        In [23]: %timeit hashlib.md5(a).hexdigest()
+        100 loops, best of 3: 20.7 ms per loop
+
+        In [24]: %timeit hashlib.md5(pickle.dumps(a, protocol=2)).hexdigest()
+        1 loops, best of 3: 73.1 ms per loop
+
+        In [25]: %timeit hashlib.md5(cPickle.dumps(a, protocol=2)).hexdigest()
+        10 loops, best of 3: 53.9 ms per loop
+
+        In [26]: %timeit hash(a)
+        100 loops, best of 3: 20.8 ms per loop
+    """
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(1000000)
+
+    def md5_hash(x):
+        return hashlib.md5(memoryview(x)).hexdigest()
+
+    relative_diff = relative_time(md5_hash, hash, a)
+    assert relative_diff < 0.3
+
+    # Check that hashing an tuple of 3 arrays takes approximately
+    # 3 times as much as hashing one array
+    time_hashlib = 3 * time_func(md5_hash, a)
+    time_hash = time_func(hash, (a, a, a))
+    relative_diff = 0.5 * (abs(time_hash - time_hashlib)
+                           / (time_hash + time_hashlib))
+    assert relative_diff < 0.3
+
+
+def test_bound_methods_hash():
+    """ Make sure that calling the same method on two different instances
+    of the same class does resolve to the same hashes.
+    """
+    a = Klass()
+    b = Klass()
+    assert (hash(filter_args(a.f, [], (1, ))) ==
+            hash(filter_args(b.f, [], (1, ))))
+
+
+def test_bound_cached_methods_hash(tmpdir):
+    """ Make sure that calling the same _cached_ method on two different
+    instances of the same class does resolve to the same hashes.
+    """
+    a = KlassWithCachedMethod(tmpdir.strpath)
+    b = KlassWithCachedMethod(tmpdir.strpath)
+    assert (hash(filter_args(a.f.func, [], (1, ))) ==
+            hash(filter_args(b.f.func, [], (1, ))))
+
+
+@with_numpy
+def test_hash_object_dtype():
+    """ Make sure that ndarrays with dtype `object' hash correctly."""
+
+    a = np.array([np.arange(i) for i in range(6)], dtype=object)
+    b = np.array([np.arange(i) for i in range(6)], dtype=object)
+
+    assert hash(a) == hash(b)
+
+
+@with_numpy
+def test_numpy_scalar():
+    # Numpy scalars are built from compiled functions, and lead to
+    # strange pickling paths explored, that can give hash collisions
+    a = np.float64(2.0)
+    b = np.float64(3.0)
+    assert hash(a) != hash(b)
+
+
+def test_dict_hash(tmpdir):
+    # Check that dictionaries hash consistently, even though the ordering
+    # of the keys is not guaranteed
+    k = KlassWithCachedMethod(tmpdir.strpath)
+
+    d = {'#s12069__c_maps.nii.gz': [33],
+         '#s12158__c_maps.nii.gz': [33],
+         '#s12258__c_maps.nii.gz': [33],
+         '#s12277__c_maps.nii.gz': [33],
+         '#s12300__c_maps.nii.gz': [33],
+         '#s12401__c_maps.nii.gz': [33],
+         '#s12430__c_maps.nii.gz': [33],
+         '#s13817__c_maps.nii.gz': [33],
+         '#s13903__c_maps.nii.gz': [33],
+         '#s13916__c_maps.nii.gz': [33],
+         '#s13981__c_maps.nii.gz': [33],
+         '#s13982__c_maps.nii.gz': [33],
+         '#s13983__c_maps.nii.gz': [33]}
+
+    a = k.f(d)
+    b = k.f(a)
+
+    assert hash(a) == hash(b)
+
+
+def test_set_hash(tmpdir):
+    # Check that sets hash consistently, even though their ordering
+    # is not guaranteed
+    k = KlassWithCachedMethod(tmpdir.strpath)
+
+    s = set(['#s12069__c_maps.nii.gz',
+             '#s12158__c_maps.nii.gz',
+             '#s12258__c_maps.nii.gz',
+             '#s12277__c_maps.nii.gz',
+             '#s12300__c_maps.nii.gz',
+             '#s12401__c_maps.nii.gz',
+             '#s12430__c_maps.nii.gz',
+             '#s13817__c_maps.nii.gz',
+             '#s13903__c_maps.nii.gz',
+             '#s13916__c_maps.nii.gz',
+             '#s13981__c_maps.nii.gz',
+             '#s13982__c_maps.nii.gz',
+             '#s13983__c_maps.nii.gz'])
+
+    a = k.f(s)
+    b = k.f(a)
+
+    assert hash(a) == hash(b)
+
+
+def test_set_decimal_hash():
+    # Check that sets containing decimals hash consistently, even though
+    # ordering is not guaranteed
+    assert (hash(set([Decimal(0), Decimal('NaN')])) ==
+            hash(set([Decimal('NaN'), Decimal(0)])))
+
+
+def test_string():
+    # Test that we obtain the same hash for object owning several strings,
+    # whatever the past of these strings (which are immutable in Python)
+    string = 'foo'
+    a = {string: 'bar'}
+    b = {string: 'bar'}
+    c = pickle.loads(pickle.dumps(b))
+    assert hash([a, b]) == hash([a, c])
+
+
+@with_numpy
+def test_numpy_dtype_pickling():
+    # numpy dtype hashing is tricky to get right: see #231, #239, #251 #1080,
+    # #1082, and explanatory comments inside
+    # ``joblib.hashing.NumpyHasher.save``.
+
+    # In this test, we make sure that the pickling of numpy dtypes is robust to
+    # object identity and object copy.
+
+    dt1 = np.dtype('f4')
+    dt2 = np.dtype('f4')
+
+    # simple dtypes objects are interned
+    assert dt1 is dt2
+    assert hash(dt1) == hash(dt2)
+
+    dt1_roundtripped = pickle.loads(pickle.dumps(dt1))
+    assert dt1 is not dt1_roundtripped
+    assert hash(dt1) == hash(dt1_roundtripped)
+
+    assert hash([dt1, dt1]) == hash([dt1_roundtripped, dt1_roundtripped])
+    assert hash([dt1, dt1]) == hash([dt1, dt1_roundtripped])
+
+    complex_dt1 = np.dtype(
+        [('name', np.str_, 16), ('grades', np.float64, (2,))]
+    )
+    complex_dt2 = np.dtype(
+        [('name', np.str_, 16), ('grades', np.float64, (2,))]
+    )
+
+    # complex dtypes objects are not interned
+    assert hash(complex_dt1) == hash(complex_dt2)
+
+    complex_dt1_roundtripped = pickle.loads(pickle.dumps(complex_dt1))
+    assert complex_dt1_roundtripped is not complex_dt1
+    assert hash(complex_dt1) == hash(complex_dt1_roundtripped)
+
+    assert hash([complex_dt1, complex_dt1]) == hash(
+        [complex_dt1_roundtripped, complex_dt1_roundtripped]
+    )
+    assert hash([complex_dt1, complex_dt1]) == hash(
+        [complex_dt1_roundtripped, complex_dt1]
+    )
+
+
+@parametrize('to_hash,expected',
+             [('This is a string to hash',
+               '71b3f47df22cb19431d85d92d0b230b2'),
+              (u"C'est l\xe9t\xe9",
+               '2d8d189e9b2b0b2e384d93c868c0e576'),
+              ((123456, 54321, -98765),
+               'e205227dd82250871fa25aa0ec690aa3'),
+              ([random.Random(42).random() for _ in range(5)],
+               'a11ffad81f9682a7d901e6edc3d16c84'),
+              ({'abcde': 123, 'sadfas': [-9999, 2, 3]},
+                  'aeda150553d4bb5c69f0e69d51b0e2ef')])
+def test_hashes_stay_the_same(to_hash, expected):
+    # We want to make sure that hashes don't change with joblib
+    # version. For end users, that would mean that they have to
+    # regenerate their cache from scratch, which potentially means
+    # lengthy recomputations.
+    # Expected results have been generated with joblib 0.9.2
+    assert hash(to_hash) == expected
+
+
+@with_numpy
+def test_hashes_are_different_between_c_and_fortran_contiguous_arrays():
+    # We want to be sure that the c-contiguous and f-contiguous versions of the
+    # same array produce 2 different hashes.
+    rng = np.random.RandomState(0)
+    arr_c = rng.random_sample((10, 10))
+    arr_f = np.asfortranarray(arr_c)
+    assert hash(arr_c) != hash(arr_f)
+
+
+@with_numpy
+def test_0d_array():
+    hash(np.array(0))
+
+
+@with_numpy
+def test_0d_and_1d_array_hashing_is_different():
+    assert hash(np.array(0)) != hash(np.array([0]))
+
+
+@with_numpy
+def test_hashes_stay_the_same_with_numpy_objects():
+    # Note: joblib used to test numpy objects hashing by comparing the produced
+    # hash of an object with some hard-coded target value to guarantee that
+    # hashing remains the same across joblib versions. However, since numpy
+    # 1.20 and joblib 1.0, joblib relies on potentially unstable implementation
+    # details of numpy to hash np.dtype objects, which makes the stability of
+    # hash values across different environments hard to guarantee and to test.
+    # As a result, hashing stability across joblib versions becomes best-effort
+    # only, and we only test the consistency within a single environment by
+    # making sure:
+    # - the hash of two copies of the same objects is the same
+    # - hashing some object in two different python processes produces the same
+    #   value. This should be viewed as a proxy for testing hash consistency
+    #   through time between Python sessions (provided no change in the
+    #   environment was done between sessions).
+
+    def create_objects_to_hash():
+        rng = np.random.RandomState(42)
+        # Being explicit about dtypes in order to avoid
+        # architecture-related differences. Also using 'f4' rather than
+        # 'f8' for float arrays because 'f8' arrays generated by
+        # rng.random.randn don't seem to be bit-identical on 32bit and
+        # 64bit machines.
+        to_hash_list = [
+            rng.randint(-1000, high=1000, size=50).astype('<i8'),
+            tuple(rng.randn(3).astype('<f4') for _ in range(5)),
+            [rng.randn(3).astype('<f4') for _ in range(5)],
+            {
+                -3333: rng.randn(3, 5).astype('<f4'),
+                0: [
+                    rng.randint(10, size=20).astype('<i8'),
+                    rng.randn(10).astype('<f4')
+                ]
+            },
+            # Non regression cases for
+            # https://github.com/joblib/joblib/issues/308
+            np.arange(100, dtype='<i8').reshape((10, 10)),
+            # Fortran contiguous array
+            np.asfortranarray(np.arange(100, dtype='<i8').reshape((10, 10))),
+            # Non contiguous array
+            np.arange(100, dtype='<i8').reshape((10, 10))[:, :2],
+        ]
+        return to_hash_list
+
+    # Create two lists containing copies of the same objects.  joblib.hash
+    # should return the same hash for to_hash_list_one[i] and
+    # to_hash_list_two[i]
+    to_hash_list_one = create_objects_to_hash()
+    to_hash_list_two = create_objects_to_hash()
+
+    e1 = ProcessPoolExecutor(max_workers=1)
+    e2 = ProcessPoolExecutor(max_workers=1)
+
+    try:
+        for obj_1, obj_2 in zip(to_hash_list_one, to_hash_list_two):
+            # testing consistency of hashes across python processes
+            hash_1 = e1.submit(hash, obj_1).result()
+            hash_2 = e2.submit(hash, obj_1).result()
+            assert hash_1 == hash_2
+
+            # testing consistency when hashing two copies of the same objects.
+            hash_3 = e1.submit(hash, obj_2).result()
+            assert hash_1 == hash_3
+
+    finally:
+        e1.shutdown()
+        e2.shutdown()
+
+
+def test_hashing_pickling_error():
+    def non_picklable():
+        return 42
+
+    with raises(pickle.PicklingError) as excinfo:
+        hash(non_picklable)
+    excinfo.match('PicklingError while hashing')
+
+
+def test_wrong_hash_name():
+    msg = "Valid options for 'hash_name' are"
+    with raises(ValueError, match=msg):
+        data = {'foo': 'bar'}
+        hash(data, hash_name='invalid')

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_logger.py

@@ -0,0 +1,31 @@
+"""
+Test the logger module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+import re
+
+from joblib.logger import PrintTime
+
+
+def test_print_time(tmpdir, capsys):
+    # A simple smoke test for PrintTime.
+    logfile = tmpdir.join('test.log').strpath
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+    # Create a second time, to smoke test log rotation.
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+    # And a third time
+    print_time = PrintTime(logfile=logfile)
+    print_time('Foo')
+
+    out_printed_text, err_printed_text = capsys.readouterr()
+    # Use regexps to be robust to time variations
+    match = r"Foo: 0\..s, 0\..min\nFoo: 0\..s, 0..min\nFoo: " + \
+            r".\..s, 0..min\n"
+    if not re.match(match, err_printed_text):
+        raise AssertionError('Excepted %s, got %s' %
+                             (match, err_printed_text))

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_memmapping.py

@@ -0,0 +1,1191 @@
+import os
+import mmap
+import sys
+import platform
+import gc
+import pickle
+import itertools
+from time import sleep
+import subprocess
+import threading
+import faulthandler
+
+import pytest
+
+from joblib.test.common import with_numpy, np
+from joblib.test.common import with_multiprocessing
+from joblib.test.common import with_dev_shm
+from joblib.testing import raises, parametrize, skipif
+from joblib.backports import make_memmap
+from joblib.parallel import Parallel, delayed
+
+from joblib.pool import MemmappingPool
+from joblib.executor import _TestingMemmappingExecutor as TestExecutor
+from joblib._memmapping_reducer import has_shareable_memory
+from joblib._memmapping_reducer import ArrayMemmapForwardReducer
+from joblib._memmapping_reducer import _strided_from_memmap
+from joblib._memmapping_reducer import _get_temp_dir
+from joblib._memmapping_reducer import _WeakArrayKeyMap
+from joblib._memmapping_reducer import _get_backing_memmap
+import joblib._memmapping_reducer as jmr
+
+
+def setup_module():
+    faulthandler.dump_traceback_later(timeout=300, exit=True)
+
+
+def teardown_module():
+    faulthandler.cancel_dump_traceback_later()
+
+
+def check_memmap_and_send_back(array):
+    assert _get_backing_memmap(array) is not None
+    return array
+
+
+def check_array(args):
+    """Dummy helper function to be executed in subprocesses
+
+    Check that the provided array has the expected values in the provided
+    range.
+
+    """
+    data, position, expected = args
+    np.testing.assert_array_equal(data[position], expected)
+
+
+def inplace_double(args):
+    """Dummy helper function to be executed in subprocesses
+
+
+    Check that the input array has the right values in the provided range
+    and perform an inplace modification to double the values in the range by
+    two.
+
+    """
+    data, position, expected = args
+    assert data[position] == expected
+    data[position] *= 2
+    np.testing.assert_array_equal(data[position], 2 * expected)
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmap_based_array_reducing(tmpdir):
+    """Check that it is possible to reduce a memmap backed array"""
+    assert_array_equal = np.testing.assert_array_equal
+    filename = tmpdir.join('test.mmap').strpath
+
+    # Create a file larger than what will be used by a
+    buffer = np.memmap(filename, dtype=np.float64, shape=500, mode='w+')
+
+    # Fill the original buffer with negative markers to detect over of
+    # underflow in case of test failures
+    buffer[:] = - 1.0 * np.arange(buffer.shape[0], dtype=buffer.dtype)
+    buffer.flush()
+
+    # Memmap a 2D fortran array on a offsetted subsection of the previous
+    # buffer
+    a = np.memmap(filename, dtype=np.float64, shape=(3, 5, 4),
+                  mode='r+', order='F', offset=4)
+    a[:] = np.arange(60).reshape(a.shape)
+
+    # Build various views that share the buffer with the original memmap
+
+    # b is an memmap sliced view on an memmap instance
+    b = a[1:-1, 2:-1, 2:4]
+
+    # c and d are array views
+    c = np.asarray(b)
+    d = c.T
+
+    # Array reducer with auto dumping disabled
+    reducer = ArrayMemmapForwardReducer(None, tmpdir.strpath, 'c', True)
+
+    def reconstruct_array_or_memmap(x):
+        cons, args = reducer(x)
+        return cons(*args)
+
+    # Reconstruct original memmap
+    a_reconstructed = reconstruct_array_or_memmap(a)
+    assert has_shareable_memory(a_reconstructed)
+    assert isinstance(a_reconstructed, np.memmap)
+    assert_array_equal(a_reconstructed, a)
+
+    # Reconstruct strided memmap view
+    b_reconstructed = reconstruct_array_or_memmap(b)
+    assert has_shareable_memory(b_reconstructed)
+    assert_array_equal(b_reconstructed, b)
+
+    # Reconstruct arrays views on memmap base
+    c_reconstructed = reconstruct_array_or_memmap(c)
+    assert not isinstance(c_reconstructed, np.memmap)
+    assert has_shareable_memory(c_reconstructed)
+    assert_array_equal(c_reconstructed, c)
+
+    d_reconstructed = reconstruct_array_or_memmap(d)
+    assert not isinstance(d_reconstructed, np.memmap)
+    assert has_shareable_memory(d_reconstructed)
+    assert_array_equal(d_reconstructed, d)
+
+    # Test graceful degradation on fake memmap instances with in-memory
+    # buffers
+    a3 = a * 3
+    assert not has_shareable_memory(a3)
+    a3_reconstructed = reconstruct_array_or_memmap(a3)
+    assert not has_shareable_memory(a3_reconstructed)
+    assert not isinstance(a3_reconstructed, np.memmap)
+    assert_array_equal(a3_reconstructed, a * 3)
+
+    # Test graceful degradation on arrays derived from fake memmap instances
+    b3 = np.asarray(a3)
+    assert not has_shareable_memory(b3)
+
+    b3_reconstructed = reconstruct_array_or_memmap(b3)
+    assert isinstance(b3_reconstructed, np.ndarray)
+    assert not has_shareable_memory(b3_reconstructed)
+    assert_array_equal(b3_reconstructed, b3)
+
+
+@with_multiprocessing
+@skipif((sys.platform != "win32") or (),
+        reason="PermissionError only easily triggerable on Windows")
+def test_resource_tracker_retries_when_permissionerror(tmpdir):
+    # Test resource_tracker retry mechanism when unlinking memmaps.  See more
+    # thorough information in the ``unlink_file`` documentation of joblib.
+    filename = tmpdir.join('test.mmap').strpath
+    cmd = """if 1:
+    import os
+    import numpy as np
+    import time
+    from joblib.externals.loky.backend import resource_tracker
+    resource_tracker.VERBOSE = 1
+
+    # Start the resource tracker
+    resource_tracker.ensure_running()
+    time.sleep(1)
+
+    # Create a file containing numpy data
+    memmap = np.memmap(r"{filename}", dtype=np.float64, shape=10, mode='w+')
+    memmap[:] = np.arange(10).astype(np.int8).data
+    memmap.flush()
+    assert os.path.exists(r"{filename}")
+    del memmap
+
+    # Create a np.memmap backed by this file
+    memmap = np.memmap(r"{filename}", dtype=np.float64, shape=10, mode='w+')
+    resource_tracker.register(r"{filename}", "file")
+
+    # Ask the resource_tracker to delete the file backing the np.memmap , this
+    # should raise PermissionError that the resource_tracker will log.
+    resource_tracker.maybe_unlink(r"{filename}", "file")
+
+    # Wait for the resource_tracker to process the maybe_unlink before cleaning
+    # up the memmap
+    time.sleep(2)
+    """.format(filename=filename)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0
+    assert out == b''
+    msg = 'tried to unlink {}, got PermissionError'.format(filename)
+    assert msg in err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+def test_high_dimension_memmap_array_reducing(tmpdir):
+    assert_array_equal = np.testing.assert_array_equal
+
+    filename = tmpdir.join('test.mmap').strpath
+
+    # Create a high dimensional memmap
+    a = np.memmap(filename, dtype=np.float64, shape=(100, 15, 15, 3),
+                  mode='w+')
+    a[:] = np.arange(100 * 15 * 15 * 3).reshape(a.shape)
+
+    # Create some slices/indices at various dimensions
+    b = a[0:10]
+    c = a[:, 5:10]
+    d = a[:, :, :, 0]
+    e = a[1:3:4]
+
+    # Array reducer with auto dumping disabled
+    reducer = ArrayMemmapForwardReducer(None, tmpdir.strpath, 'c', True)
+
+    def reconstruct_array_or_memmap(x):
+        cons, args = reducer(x)
+        return cons(*args)
+
+    a_reconstructed = reconstruct_array_or_memmap(a)
+    assert has_shareable_memory(a_reconstructed)
+    assert isinstance(a_reconstructed, np.memmap)
+    assert_array_equal(a_reconstructed, a)
+
+    b_reconstructed = reconstruct_array_or_memmap(b)
+    assert has_shareable_memory(b_reconstructed)
+    assert_array_equal(b_reconstructed, b)
+
+    c_reconstructed = reconstruct_array_or_memmap(c)
+    assert has_shareable_memory(c_reconstructed)
+    assert_array_equal(c_reconstructed, c)
+
+    d_reconstructed = reconstruct_array_or_memmap(d)
+    assert has_shareable_memory(d_reconstructed)
+    assert_array_equal(d_reconstructed, d)
+
+    e_reconstructed = reconstruct_array_or_memmap(e)
+    assert has_shareable_memory(e_reconstructed)
+    assert_array_equal(e_reconstructed, e)
+
+
+@with_numpy
+def test__strided_from_memmap(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    # This line creates the mmap file that is reused later
+    memmap_obj = np.memmap(fname, mode='w+', shape=size + offset)
+    # filename, dtype, mode, offset, order, shape, strides, total_buffer_len
+    memmap_obj = _strided_from_memmap(fname, dtype='uint8', mode='r',
+                                      offset=offset, order='C', shape=size,
+                                      strides=None, total_buffer_len=None,
+                                      unlink_on_gc_collect=False)
+    assert isinstance(memmap_obj, np.memmap)
+    assert memmap_obj.offset == offset
+    memmap_backed_obj = _strided_from_memmap(
+        fname, dtype='uint8', mode='r', offset=offset, order='C',
+        shape=(size // 2,), strides=(2,), total_buffer_len=size,
+        unlink_on_gc_collect=False
+    )
+    assert _get_backing_memmap(memmap_backed_obj).offset == offset
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_pool_with_memmap(factory, tmpdir):
+    """Check that subprocess can access and update shared memory memmap"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Fork the subprocess before allocating the objects to be passed
+    pool_temp_folder = tmpdir.mkdir('pool').strpath
+    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
+    try:
+        filename = tmpdir.join('test.mmap').strpath
+        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
+        a.fill(1.0)
+
+        p.map(inplace_double, [(a, (i, j), 1.0)
+                               for i in range(a.shape[0])
+                               for j in range(a.shape[1])])
+
+        assert_array_equal(a, 2 * np.ones(a.shape))
+
+        # Open a copy-on-write view on the previous data
+        b = np.memmap(filename, dtype=np.float32, shape=(5, 3), mode='c')
+
+        p.map(inplace_double, [(b, (i, j), 2.0)
+                               for i in range(b.shape[0])
+                               for j in range(b.shape[1])])
+
+        # Passing memmap instances to the pool should not trigger the creation
+        # of new files on the FS
+        assert os.listdir(pool_temp_folder) == []
+
+        # the original data is untouched
+        assert_array_equal(a, 2 * np.ones(a.shape))
+        assert_array_equal(b, 2 * np.ones(b.shape))
+
+        # readonly maps can be read but not updated
+        c = np.memmap(filename, dtype=np.float32, shape=(10,), mode='r',
+                      offset=5 * 4)
+
+        with raises(AssertionError):
+            p.map(check_array, [(c, i, 3.0) for i in range(c.shape[0])])
+
+        # depending on the version of numpy one can either get a RuntimeError
+        # or a ValueError
+        with raises((RuntimeError, ValueError)):
+            p.map(inplace_double, [(c, i, 2.0) for i in range(c.shape[0])])
+    finally:
+        # Clean all filehandlers held by the pool
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_pool_with_memmap_array_view(factory, tmpdir):
+    """Check that subprocess can access and update shared memory array"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Fork the subprocess before allocating the objects to be passed
+    pool_temp_folder = tmpdir.mkdir('pool').strpath
+    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
+    try:
+
+        filename = tmpdir.join('test.mmap').strpath
+        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
+        a.fill(1.0)
+
+        # Create an ndarray view on the memmap instance
+        a_view = np.asarray(a)
+        assert not isinstance(a_view, np.memmap)
+        assert has_shareable_memory(a_view)
+
+        p.map(inplace_double, [(a_view, (i, j), 1.0)
+                               for i in range(a.shape[0])
+                               for j in range(a.shape[1])])
+
+        # Both a and the a_view have been updated
+        assert_array_equal(a, 2 * np.ones(a.shape))
+        assert_array_equal(a_view, 2 * np.ones(a.shape))
+
+        # Passing memmap array view to the pool should not trigger the
+        # creation of new files on the FS
+        assert os.listdir(pool_temp_folder) == []
+
+    finally:
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_permission_error_windows_reference_cycle(backend):
+    # Non regression test for:
+    # https://github.com/joblib/joblib/issues/806
+    #
+    # The issue happens when trying to delete a memory mapped file that has
+    # not yet been closed by one of the worker processes.
+    cmd = """if 1:
+        import numpy as np
+        from joblib import Parallel, delayed
+
+
+        data = np.random.rand(int(2e6)).reshape((int(1e6), 2))
+
+        # Build a complex cyclic reference that is likely to delay garbage
+        # collection of the memmapped array in the worker processes.
+        first_list = current_list = [data]
+        for i in range(10):
+            current_list = [current_list]
+        first_list.append(current_list)
+
+        if __name__ == "__main__":
+            results = Parallel(n_jobs=2, backend="{b}")(
+                delayed(len)(current_list) for i in range(10))
+            assert results == [1] * 10
+    """.format(b=backend)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0, out.decode() + "\n\n" + err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_permission_error_windows_memmap_sent_to_parent(backend):
+    # Second non-regression test for:
+    # https://github.com/joblib/joblib/issues/806
+    # previously, child process would not convert temporary memmaps to numpy
+    # arrays when sending the data back to the parent process. This would lead
+    # to permission errors on windows when deleting joblib's temporary folder,
+    # as the memmaped files handles would still opened in the parent process.
+    cmd = '''if 1:
+        import os
+        import time
+
+        import numpy as np
+
+        from joblib import Parallel, delayed
+        from testutils import return_slice_of_data
+
+        data = np.ones(int(2e6))
+
+        if __name__ == '__main__':
+            # warm-up call to launch the workers and start the resource_tracker
+            _ = Parallel(n_jobs=2, verbose=5, backend='{b}')(
+                delayed(id)(i) for i in range(20))
+
+            time.sleep(0.5)
+
+            slice_of_data = Parallel(n_jobs=2, verbose=5, backend='{b}')(
+                delayed(return_slice_of_data)(data, 0, 20) for _ in range(10))
+    '''.format(b=backend)
+
+    for _ in range(3):
+        env = os.environ.copy()
+        env['PYTHONPATH'] = os.path.dirname(__file__)
+        p = subprocess.Popen([sys.executable, '-c', cmd],
+                             stderr=subprocess.PIPE,
+                             stdout=subprocess.PIPE, env=env)
+        p.wait()
+        out, err = p.communicate()
+        assert p.returncode == 0, err
+        assert out == b''
+        if sys.version_info[:3] not in [(3, 8, 0), (3, 8, 1)]:
+            # In early versions of Python 3.8, a reference leak
+            # https://github.com/cloudpipe/cloudpickle/issues/327, holds
+            # references to pickled objects, generating race condition during
+            # cleanup finalizers of joblib and noisy resource_tracker outputs.
+            assert b'resource_tracker' not in err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_parallel_isolated_temp_folders(backend):
+    # Test that consecutive Parallel call use isolated subfolders, even
+    # for the loky backend that reuses its executor instance across calls.
+    array = np.arange(int(1e2))
+    [filename_1] = Parallel(n_jobs=2, backend=backend, max_nbytes=10)(
+        delayed(getattr)(array, 'filename') for _ in range(1)
+    )
+    [filename_2] = Parallel(n_jobs=2, backend=backend, max_nbytes=10)(
+        delayed(getattr)(array, 'filename') for _ in range(1)
+    )
+    assert os.path.dirname(filename_2) != os.path.dirname(filename_1)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_managed_backend_reuse_temp_folder(backend):
+    # Test that calls to a managed parallel object reuse the same memmaps.
+    array = np.arange(int(1e2))
+    with Parallel(n_jobs=2, backend=backend, max_nbytes=10) as p:
+        [filename_1] = p(
+            delayed(getattr)(array, 'filename') for _ in range(1)
+        )
+        [filename_2] = p(
+            delayed(getattr)(array, 'filename') for _ in range(1)
+        )
+    assert os.path.dirname(filename_2) == os.path.dirname(filename_1)
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmapping_temp_folder_thread_safety():
+    # Concurrent calls to Parallel with the loky backend will use the same
+    # executor, and thus the same reducers. Make sure that those reducers use
+    # different temporary folders depending on which Parallel objects called
+    # them, which is necessary to limit potential race conditions during the
+    # garbage collection of temporary memmaps.
+    array = np.arange(int(1e2))
+
+    temp_dirs_thread_1 = set()
+    temp_dirs_thread_2 = set()
+
+    def concurrent_get_filename(array, temp_dirs):
+        with Parallel(backend='loky', n_jobs=2, max_nbytes=10) as p:
+            for i in range(10):
+                [filename] = p(
+                    delayed(getattr)(array, 'filename') for _ in range(1)
+                )
+                temp_dirs.add(os.path.dirname(filename))
+
+    t1 = threading.Thread(
+        target=concurrent_get_filename, args=(array, temp_dirs_thread_1)
+    )
+    t2 = threading.Thread(
+        target=concurrent_get_filename, args=(array, temp_dirs_thread_2)
+    )
+
+    t1.start()
+    t2.start()
+
+    t1.join()
+    t2.join()
+
+    assert len(temp_dirs_thread_1) == 1
+    assert len(temp_dirs_thread_2) == 1
+
+    assert temp_dirs_thread_1 != temp_dirs_thread_2
+
+
+@with_numpy
+@with_multiprocessing
+def test_multithreaded_parallel_termination_resource_tracker_silent():
+    # test that concurrent termination attempts of a same executor does not
+    # emit any spurious error from the resource_tracker. We test various
+    # situations making 0, 1 or both parallel call sending a task that will
+    # make the worker (and thus the whole Parallel call) error out.
+    cmd = '''if 1:
+        import os
+        import numpy as np
+        from joblib import Parallel, delayed
+        from joblib.externals.loky.backend import resource_tracker
+        from concurrent.futures import ThreadPoolExecutor, wait
+
+        resource_tracker.VERBOSE = 0
+
+        array = np.arange(int(1e2))
+
+        temp_dirs_thread_1 = set()
+        temp_dirs_thread_2 = set()
+
+
+        def raise_error(array):
+            raise ValueError
+
+
+        def parallel_get_filename(array, temp_dirs):
+            with Parallel(backend="loky", n_jobs=2, max_nbytes=10) as p:
+                for i in range(10):
+                    [filename] = p(
+                        delayed(getattr)(array, "filename") for _ in range(1)
+                    )
+                    temp_dirs.add(os.path.dirname(filename))
+
+
+        def parallel_raise(array, temp_dirs):
+            with Parallel(backend="loky", n_jobs=2, max_nbytes=10) as p:
+                for i in range(10):
+                    [filename] = p(
+                        delayed(raise_error)(array) for _ in range(1)
+                    )
+                    temp_dirs.add(os.path.dirname(filename))
+
+
+        executor = ThreadPoolExecutor(max_workers=2)
+
+        # both function calls will use the same loky executor, but with a
+        # different Parallel object.
+        future_1 = executor.submit({f1}, array, temp_dirs_thread_1)
+        future_2 = executor.submit({f2}, array, temp_dirs_thread_2)
+
+        # Wait for both threads to terminate their backend
+        wait([future_1, future_2])
+
+        future_1.result()
+        future_2.result()
+    '''
+    functions_and_returncodes = [
+        ("parallel_get_filename", "parallel_get_filename", 0),
+        ("parallel_get_filename", "parallel_raise", 1),
+        ("parallel_raise", "parallel_raise", 1)
+    ]
+
+    for f1, f2, returncode in functions_and_returncodes:
+        p = subprocess.Popen([sys.executable, '-c', cmd.format(f1=f1, f2=f2)],
+                             stderr=subprocess.PIPE, stdout=subprocess.PIPE)
+        p.wait()
+        out, err = p.communicate()
+        assert p.returncode == returncode, out.decode()
+        assert b"resource_tracker" not in err, err.decode()
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_many_parallel_calls_on_same_object(backend):
+    # After #966 got merged, consecutive Parallel objects were sharing temp
+    # folder, which would lead to race conditions happening during the
+    # temporary resources management with the resource_tracker. This is a
+    # non-regression test that makes sure that consecutive Parallel operations
+    # on the same object do not error out.
+    cmd = '''if 1:
+        import os
+        import time
+
+        import numpy as np
+
+        from joblib import Parallel, delayed
+        from testutils import return_slice_of_data
+
+        data = np.ones(100)
+
+        if __name__ == '__main__':
+            for i in range(5):
+                slice_of_data = Parallel(
+                    n_jobs=2, max_nbytes=1, backend='{b}')(
+                        delayed(return_slice_of_data)(data, 0, 20)
+                        for _ in range(10)
+                    )
+    '''.format(b=backend)
+    env = os.environ.copy()
+    env['PYTHONPATH'] = os.path.dirname(__file__)
+    p = subprocess.Popen(
+        [sys.executable, '-c', cmd],
+        stderr=subprocess.PIPE,
+        stdout=subprocess.PIPE,
+        env=env,
+    )
+    p.wait()
+    out, err = p.communicate()
+    assert p.returncode == 0, err
+    assert out == b''
+    if sys.version_info[:3] not in [(3, 8, 0), (3, 8, 1)]:
+        # In early versions of Python 3.8, a reference leak
+        # https://github.com/cloudpipe/cloudpickle/issues/327, holds
+        # references to pickled objects, generating race condition during
+        # cleanup finalizers of joblib and noisy resource_tracker outputs.
+        assert b'resource_tracker' not in err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_memmap_returned_as_regular_array(backend):
+    data = np.ones(int(1e3))
+    # Check that child processes send temporary memmaps back as numpy arrays.
+    [result] = Parallel(n_jobs=2, backend=backend, max_nbytes=100)(
+        delayed(check_memmap_and_send_back)(data) for _ in range(1))
+    assert _get_backing_memmap(result) is None
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("backend", ["multiprocessing", "loky"])
+def test_resource_tracker_silent_when_reference_cycles(backend):
+    # There is a variety of reasons that can make joblib with loky backend
+    # output noisy warnings when a reference cycle is preventing a memmap from
+    # being garbage collected. Especially, joblib's main process finalizer
+    # deletes the temporary folder if it was not done before, which can
+    # interact badly with the resource_tracker. We don't risk leaking any
+    # resources, but this will likely make joblib output a lot of low-level
+    # confusing messages.
+    #
+    # This test makes sure that the resource_tracker is silent when a reference
+    # has been collected concurrently on non-Windows platforms.
+    #
+    # Note that the script in ``cmd`` is the exact same script as in
+    # test_permission_error_windows_reference_cycle.
+    if backend == "loky" and sys.platform.startswith('win'):
+        # XXX: on Windows, reference cycles can delay timely garbage collection
+        # and make it impossible to properly delete the temporary folder in the
+        # main process because of permission errors.
+        pytest.xfail(
+            "The temporary folder cannot be deleted on Windows in the "
+            "presence of a reference cycle"
+        )
+
+    cmd = """if 1:
+        import numpy as np
+        from joblib import Parallel, delayed
+
+
+        data = np.random.rand(int(2e6)).reshape((int(1e6), 2))
+
+        # Build a complex cyclic reference that is likely to delay garbage
+        # collection of the memmapped array in the worker processes.
+        first_list = current_list = [data]
+        for i in range(10):
+            current_list = [current_list]
+        first_list.append(current_list)
+
+        if __name__ == "__main__":
+            results = Parallel(n_jobs=2, backend="{b}")(
+                delayed(len)(current_list) for i in range(10))
+            assert results == [1] * 10
+    """.format(b=backend)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE)
+    p.wait()
+    out, err = p.communicate()
+    out = out.decode()
+    err = err.decode()
+    assert p.returncode == 0, out + "\n\n" + err
+    assert "resource_tracker" not in err, err
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays(factory, tmpdir):
+    """Check that large arrays are not copied in memory"""
+
+    # Check that the tempfolder is empty
+    assert os.listdir(tmpdir.strpath) == []
+
+    # Build an array reducers that automatically dump large array content
+    # to filesystem backed memmap instances to avoid memory explosion
+    p = factory(3, max_nbytes=40, temp_folder=tmpdir.strpath, verbose=2)
+    try:
+        # The temporary folder for the pool is not provisioned in advance
+        assert os.listdir(tmpdir.strpath) == []
+        assert not os.path.exists(p._temp_folder)
+
+        small = np.ones(5, dtype=np.float32)
+        assert small.nbytes == 20
+        p.map(check_array, [(small, i, 1.0) for i in range(small.shape[0])])
+
+        # Memory has been copied, the pool filesystem folder is unused
+        assert os.listdir(tmpdir.strpath) == []
+
+        # Try with a file larger than the memmap threshold of 40 bytes
+        large = np.ones(100, dtype=np.float64)
+        assert large.nbytes == 800
+        p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
+
+        # The data has been dumped in a temp folder for subprocess to share it
+        # without per-child memory copies
+        assert os.path.isdir(p._temp_folder)
+        dumped_filenames = os.listdir(p._temp_folder)
+        assert len(dumped_filenames) == 1
+
+        # Check that memory mapping is not triggered for arrays with
+        # dtype='object'
+        objects = np.array(['abc'] * 100, dtype='object')
+        results = p.map(has_shareable_memory, [objects])
+        assert not results[0]
+
+    finally:
+        # check FS garbage upon pool termination
+        p.terminate()
+        for i in range(10):
+            sleep(.1)
+            if not os.path.exists(p._temp_folder):
+                break
+        else:  # pragma: no cover
+            raise AssertionError(
+                'temporary folder {} was not deleted'.format(p._temp_folder)
+            )
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize(
+    "backend",
+    [
+        pytest.param(
+            "multiprocessing",
+            marks=pytest.mark.xfail(
+                reason='https://github.com/joblib/joblib/issues/1086'
+            ),
+        ),
+        "loky",
+    ]
+)
+def test_child_raises_parent_exits_cleanly(backend):
+    # When a task executed by a child process raises an error, the parent
+    # process's backend is notified, and calls abort_everything.
+    # In loky, abort_everything itself calls shutdown(kill_workers=True) which
+    # sends SIGKILL to the worker, preventing it from running the finalizers
+    # supposed to signal the resource_tracker when the worker is done using
+    # objects relying on a shared resource (e.g np.memmaps). Because this
+    # behavior is prone to :
+    # - cause a resource leak
+    # - make the resource tracker emit noisy resource warnings
+    # we explicitly test that, when the said situation occurs:
+    # - no resources are actually leaked
+    # - the temporary resources are deleted as soon as possible (typically, at
+    #   the end of the failing Parallel call)
+    # - the resource_tracker does not emit any warnings.
+    cmd = """if 1:
+        import os
+        from pathlib import Path
+        from time import sleep
+
+        import numpy as np
+        from joblib import Parallel, delayed
+        from testutils import print_filename_and_raise
+
+        data = np.random.rand(1000)
+
+        def get_temp_folder(parallel_obj, backend):
+            if "{b}" == "loky":
+                return Path(parallel_obj._backend._workers._temp_folder)
+            else:
+                return Path(parallel_obj._backend._pool._temp_folder)
+
+
+        if __name__ == "__main__":
+            try:
+                with Parallel(n_jobs=2, backend="{b}", max_nbytes=100) as p:
+                    temp_folder = get_temp_folder(p, "{b}")
+                    p(delayed(print_filename_and_raise)(data)
+                              for i in range(1))
+            except ValueError as e:
+                # the temporary folder should be deleted by the end of this
+                # call but apparently on some file systems, this takes
+                # some time to be visible.
+                #
+                # We attempt to write into the temporary folder to test for
+                # its existence and we wait for a maximum of 10 seconds.
+                for i in range(100):
+                    try:
+                        with open(temp_folder / "some_file.txt", "w") as f:
+                            f.write("some content")
+                    except FileNotFoundError:
+                        # temp_folder has been deleted, all is fine
+                        break
+
+                    # ... else, wait a bit and try again
+                    sleep(.1)
+                else:
+                    raise AssertionError(
+                        str(temp_folder) + " was not deleted"
+                    ) from e
+    """.format(b=backend)
+    env = os.environ.copy()
+    env['PYTHONPATH'] = os.path.dirname(__file__)
+    p = subprocess.Popen([sys.executable, '-c', cmd], stderr=subprocess.PIPE,
+                         stdout=subprocess.PIPE, env=env)
+    p.wait()
+    out, err = p.communicate()
+    out, err = out.decode(), err.decode()
+    filename = out.split('\n')[0]
+    assert p.returncode == 0, err or out
+    assert err == ''  # no resource_tracker warnings.
+    assert not os.path.exists(filename)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays_disabled(factory, tmpdir):
+    """Check that large arrays memmapping can be disabled"""
+    # Set max_nbytes to None to disable the auto memmapping feature
+    p = factory(3, max_nbytes=None, temp_folder=tmpdir.strpath)
+    try:
+
+        # Check that the tempfolder is empty
+        assert os.listdir(tmpdir.strpath) == []
+
+        # Try with a file largish than the memmap threshold of 40 bytes
+        large = np.ones(100, dtype=np.float64)
+        assert large.nbytes == 800
+        p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
+
+        # Check that the tempfolder is still empty
+        assert os.listdir(tmpdir.strpath) == []
+
+    finally:
+        # Cleanup open file descriptors
+        p.terminate()
+        del p
+
+
+@with_numpy
+@with_multiprocessing
+@with_dev_shm
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_on_large_enough_dev_shm(factory):
+    """Check that memmapping uses /dev/shm when possible"""
+    orig_size = jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE
+    try:
+        # Make joblib believe that it can use /dev/shm even when running on a
+        # CI container where the size of the /dev/shm is not very large (that
+        # is at least 32 MB instead of 2 GB by default).
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(32e6)
+        p = factory(3, max_nbytes=10)
+        try:
+            # Check that the pool has correctly detected the presence of the
+            # shared memory filesystem.
+            pool_temp_folder = p._temp_folder
+            folder_prefix = '/dev/shm/joblib_memmapping_folder_'
+            assert pool_temp_folder.startswith(folder_prefix)
+            assert os.path.exists(pool_temp_folder)
+
+            # Try with a file larger than the memmap threshold of 10 bytes
+            a = np.ones(100, dtype=np.float64)
+            assert a.nbytes == 800
+            p.map(id, [a] * 10)
+            # a should have been memmapped to the pool temp folder: the joblib
+            # pickling procedure generate one .pkl file:
+            assert len(os.listdir(pool_temp_folder)) == 1
+
+            # create a new array with content that is different from 'a' so
+            # that it is mapped to a different file in the temporary folder of
+            # the pool.
+            b = np.ones(100, dtype=np.float64) * 2
+            assert b.nbytes == 800
+            p.map(id, [b] * 10)
+            # A copy of both a and b are now stored in the shared memory folder
+            assert len(os.listdir(pool_temp_folder)) == 2
+        finally:
+            # Cleanup open file descriptors
+            p.terminate()
+            del p
+
+        for i in range(100):
+            # The temp folder is cleaned up upon pool termination
+            if not os.path.exists(pool_temp_folder):
+                break
+            sleep(.1)
+        else:  # pragma: no cover
+            raise AssertionError('temporary folder of pool was not deleted')
+    finally:
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = orig_size
+
+
+@with_numpy
+@with_multiprocessing
+@with_dev_shm
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_on_too_small_dev_shm(factory):
+    orig_size = jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE
+    try:
+        # Make joblib believe that it cannot use /dev/shm unless there is
+        # 42 exabytes of available shared memory in /dev/shm
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(42e18)
+
+        p = factory(3, max_nbytes=10)
+        try:
+            # Check that the pool has correctly detected the presence of the
+            # shared memory filesystem.
+            pool_temp_folder = p._temp_folder
+            assert not pool_temp_folder.startswith('/dev/shm')
+        finally:
+            # Cleanup open file descriptors
+            p.terminate()
+            del p
+
+        # The temp folder is cleaned up upon pool termination
+        assert not os.path.exists(pool_temp_folder)
+    finally:
+        jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = orig_size
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_memmapping_pool_for_large_arrays_in_return(factory, tmpdir):
+    """Check that large arrays are not copied in memory in return"""
+    assert_array_equal = np.testing.assert_array_equal
+
+    # Build an array reducers that automatically dump large array content
+    # but check that the returned datastructure are regular arrays to avoid
+    # passing a memmap array pointing to a pool controlled temp folder that
+    # might be confusing to the user
+
+    # The MemmappingPool user can always return numpy.memmap object explicitly
+    # to avoid memory copy
+    p = factory(3, max_nbytes=10, temp_folder=tmpdir.strpath)
+    try:
+        res = p.apply_async(np.ones, args=(1000,))
+        large = res.get()
+        assert not has_shareable_memory(large)
+        assert_array_equal(large, np.ones(1000))
+    finally:
+        p.terminate()
+        del p
+
+
+def _worker_multiply(a, n_times):
+    """Multiplication function to be executed by subprocess"""
+    assert has_shareable_memory(a)
+    return a * n_times
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize("factory", [MemmappingPool, TestExecutor.get_memmapping_executor],
+             ids=["multiprocessing", "loky"])
+def test_workaround_against_bad_memmap_with_copied_buffers(factory, tmpdir):
+    """Check that memmaps with a bad buffer are returned as regular arrays
+
+    Unary operations and ufuncs on memmap instances return a new memmap
+    instance with an in-memory buffer (probably a numpy bug).
+    """
+    assert_array_equal = np.testing.assert_array_equal
+
+    p = factory(3, max_nbytes=10, temp_folder=tmpdir.strpath)
+    try:
+        # Send a complex, large-ish view on a array that will be converted to
+        # a memmap in the worker process
+        a = np.asarray(np.arange(6000).reshape((1000, 2, 3)),
+                       order='F')[:, :1, :]
+
+        # Call a non-inplace multiply operation on the worker and memmap and
+        # send it back to the parent.
+        b = p.apply_async(_worker_multiply, args=(a, 3)).get()
+        assert not has_shareable_memory(b)
+        assert_array_equal(b, 3 * a)
+    finally:
+        p.terminate()
+        del p
+
+
+def identity(arg):
+    return arg
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize(
+    "factory,retry_no",
+    list(itertools.product(
+        [MemmappingPool, TestExecutor.get_memmapping_executor], range(3))),
+    ids=['{}, {}'.format(x, y) for x, y in itertools.product(
+        ["multiprocessing", "loky"], map(str, range(3)))])
+def test_pool_memmap_with_big_offset(factory, retry_no, tmpdir):
+    # Test that numpy memmap offset is set correctly if greater than
+    # mmap.ALLOCATIONGRANULARITY, see
+    # https://github.com/joblib/joblib/issues/451 and
+    # https://github.com/numpy/numpy/pull/8443 for more details.
+    fname = tmpdir.join('test.mmap').strpath
+    size = 5 * mmap.ALLOCATIONGRANULARITY
+    offset = mmap.ALLOCATIONGRANULARITY + 1
+    obj = make_memmap(fname, mode='w+', shape=size, dtype='uint8',
+                      offset=offset)
+
+    p = factory(2, temp_folder=tmpdir.strpath)
+    result = p.apply_async(identity, args=(obj,)).get()
+    assert isinstance(result, np.memmap)
+    assert result.offset == offset
+    np.testing.assert_array_equal(obj, result)
+    p.terminate()
+
+
+def test_pool_get_temp_dir(tmpdir):
+    pool_folder_name = 'test.tmpdir'
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, tmpdir.strpath)
+    assert shared_mem is False
+    assert pool_folder == tmpdir.join('test.tmpdir').strpath
+
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, temp_folder=None)
+    if sys.platform.startswith('win'):
+        assert shared_mem is False
+    assert pool_folder.endswith(pool_folder_name)
+
+
+def test_pool_get_temp_dir_no_statvfs(tmpdir, monkeypatch):
+    """Check that _get_temp_dir works when os.statvfs is not defined
+
+    Regression test for #902
+    """
+    pool_folder_name = 'test.tmpdir'
+    import joblib._memmapping_reducer
+    if hasattr(joblib._memmapping_reducer.os, 'statvfs'):
+        # We are on Unix, since Windows doesn't have this function
+        monkeypatch.delattr(joblib._memmapping_reducer.os, 'statvfs')
+
+    pool_folder, shared_mem = _get_temp_dir(pool_folder_name, temp_folder=None)
+    if sys.platform.startswith('win'):
+        assert shared_mem is False
+    assert pool_folder.endswith(pool_folder_name)
+
+
+@with_numpy
+@skipif(sys.platform == 'win32', reason='This test fails with a '
+        'PermissionError on Windows')
+@parametrize("mmap_mode", ["r+", "w+"])
+def test_numpy_arrays_use_different_memory(mmap_mode):
+    def func(arr, value):
+        arr[:] = value
+        return arr
+
+    arrays = [np.zeros((10, 10), dtype='float64') for i in range(10)]
+
+    results = Parallel(mmap_mode=mmap_mode, max_nbytes=0, n_jobs=2)(
+        delayed(func)(arr, i) for i, arr in enumerate(arrays))
+
+    for i, arr in enumerate(results):
+        np.testing.assert_array_equal(arr, i)
+
+
+@with_numpy
+def test_weak_array_key_map():
+
+    def assert_empty_after_gc_collect(container, retries=100):
+        for i in range(retries):
+            if len(container) == 0:
+                return
+            gc.collect()
+            sleep(.1)
+        assert len(container) == 0
+
+    a = np.ones(42)
+    m = _WeakArrayKeyMap()
+    m.set(a, 'a')
+    assert m.get(a) == 'a'
+
+    b = a
+    assert m.get(b) == 'a'
+    m.set(b, 'b')
+    assert m.get(a) == 'b'
+
+    del a
+    gc.collect()
+    assert len(m._data) == 1
+    assert m.get(b) == 'b'
+
+    del b
+    assert_empty_after_gc_collect(m._data)
+
+    c = np.ones(42)
+    m.set(c, 'c')
+    assert len(m._data) == 1
+    assert m.get(c) == 'c'
+
+    with raises(KeyError):
+        m.get(np.ones(42))
+
+    del c
+    assert_empty_after_gc_collect(m._data)
+
+    # Check that creating and dropping numpy arrays with potentially the same
+    # object id will not cause the map to get confused.
+    def get_set_get_collect(m, i):
+        a = np.ones(42)
+        with raises(KeyError):
+            m.get(a)
+        m.set(a, i)
+        assert m.get(a) == i
+        return id(a)
+
+    unique_ids = set([get_set_get_collect(m, i) for i in range(1000)])
+    if platform.python_implementation() == 'CPython':
+        # On CPython (at least) the same id is often reused many times for the
+        # temporary arrays created under the local scope of the
+        # get_set_get_collect function without causing any spurious lookups /
+        # insertions in the map. Apparently on Python nogil, the id is not
+        # reused as often.
+        max_len_unique_ids = 400 if getattr(sys.flags, 'nogil', False) else 100
+        assert len(unique_ids) < max_len_unique_ids
+
+
+def test_weak_array_key_map_no_pickling():
+    m = _WeakArrayKeyMap()
+    with raises(pickle.PicklingError):
+        pickle.dumps(m)
+
+
+@with_numpy
+@with_multiprocessing
+def test_direct_mmap(tmpdir):
+    testfile = str(tmpdir.join('arr.dat'))
+    a = np.arange(10, dtype='uint8')
+    a.tofile(testfile)
+
+    def _read_array():
+        with open(testfile) as fd:
+            mm = mmap.mmap(fd.fileno(), 0, access=mmap.ACCESS_READ, offset=0)
+        return np.ndarray((10,), dtype=np.uint8, buffer=mm, offset=0)
+
+    def func(x):
+        return x**2
+
+    arr = _read_array()
+
+    # this is expected to work and gives the reference
+    ref = Parallel(n_jobs=2)(delayed(func)(x) for x in [a])
+
+    # now test that it work with the mmap array
+    results = Parallel(n_jobs=2)(delayed(func)(x) for x in [arr])
+    np.testing.assert_array_equal(results, ref)
+
+    # also test with a mmap array read in the subprocess
+    def worker():
+        return _read_array()
+
+    results = Parallel(n_jobs=2)(delayed(worker)() for _ in range(1))
+    np.testing.assert_array_equal(results[0], arr)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_memory.py

@@ -0,0 +1,1526 @@
+"""
+Test the memory module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2009 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import functools
+import gc
+import logging
+import shutil
+import os
+import os.path
+import pathlib
+import pickle
+import sys
+import time
+import datetime
+import textwrap
+
+import pytest
+
+from joblib.memory import Memory
+from joblib.memory import expires_after
+from joblib.memory import MemorizedFunc, NotMemorizedFunc
+from joblib.memory import MemorizedResult, NotMemorizedResult
+from joblib.memory import _FUNCTION_HASHES
+from joblib.memory import register_store_backend, _STORE_BACKENDS
+from joblib.memory import _build_func_identifier, _store_backend_factory
+from joblib.memory import JobLibCollisionWarning
+from joblib.parallel import Parallel, delayed
+from joblib._store_backends import StoreBackendBase, FileSystemStoreBackend
+from joblib.test.common import with_numpy, np
+from joblib.test.common import with_multiprocessing
+from joblib.testing import parametrize, raises, warns
+from joblib.hashing import hash
+
+
+###############################################################################
+# Module-level variables for the tests
+def f(x, y=1):
+    """ A module-level function for testing purposes.
+    """
+    return x ** 2 + y
+
+
+###############################################################################
+# Helper function for the tests
+def check_identity_lazy(func, accumulator, location):
+    """ Given a function and an accumulator (a list that grows every
+        time the function is called), check that the function can be
+        decorated by memory to be a lazy identity.
+    """
+    # Call each function with several arguments, and check that it is
+    # evaluated only once per argument.
+    memory = Memory(location=location, verbose=0)
+    func = memory.cache(func)
+    for i in range(3):
+        for _ in range(2):
+            assert func(i) == i
+            assert len(accumulator) == i + 1
+
+
+def corrupt_single_cache_item(memory):
+    single_cache_item, = memory.store_backend.get_items()
+    output_filename = os.path.join(single_cache_item.path, 'output.pkl')
+    with open(output_filename, 'w') as f:
+        f.write('garbage')
+
+
+def monkeypatch_cached_func_warn(func, monkeypatch_fixture):
+    # Need monkeypatch because pytest does not
+    # capture stdlib logging output (see
+    # https://github.com/pytest-dev/pytest/issues/2079)
+
+    recorded = []
+
+    def append_to_record(item):
+        recorded.append(item)
+    monkeypatch_fixture.setattr(func, 'warn', append_to_record)
+    return recorded
+
+
+###############################################################################
+# Tests
+def test_memory_integration(tmpdir):
+    """ Simple test of memory lazy evaluation.
+    """
+    accumulator = list()
+
+    # Rmk: this function has the same name than a module-level function,
+    # thus it serves as a test to see that both are identified
+    # as different.
+    def f(arg):
+        accumulator.append(1)
+        return arg
+
+    check_identity_lazy(f, accumulator, tmpdir.strpath)
+
+    # Now test clearing
+    for compress in (False, True):
+        for mmap_mode in ('r', None):
+            memory = Memory(location=tmpdir.strpath, verbose=10,
+                            mmap_mode=mmap_mode, compress=compress)
+            # First clear the cache directory, to check that our code can
+            # handle that
+            # NOTE: this line would raise an exception, as the database file is
+            # still open; we ignore the error since we want to test what
+            # happens if the directory disappears
+            shutil.rmtree(tmpdir.strpath, ignore_errors=True)
+            g = memory.cache(f)
+            g(1)
+            g.clear(warn=False)
+            current_accumulator = len(accumulator)
+            out = g(1)
+
+        assert len(accumulator) == current_accumulator + 1
+        # Also, check that Memory.eval works similarly
+        assert memory.eval(f, 1) == out
+        assert len(accumulator) == current_accumulator + 1
+
+    # Now do a smoke test with a function defined in __main__, as the name
+    # mangling rules are more complex
+    f.__module__ = '__main__'
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    memory.cache(f)(1)
+
+
+@parametrize("call_before_reducing", [True, False])
+def test_parallel_call_cached_function_defined_in_jupyter(
+    tmpdir, call_before_reducing
+):
+    # Calling an interactively defined memory.cache()'d function inside a
+    # Parallel call used to clear the existing cache related to the said
+    # function (https://github.com/joblib/joblib/issues/1035)
+
+    # This tests checks that this is no longer the case.
+
+    # TODO: test that the cache related to the function cache persists across
+    # ipython sessions (provided that no code change were made to the
+    # function's source)?
+
+    # The first part of the test makes the necessary low-level calls to emulate
+    # the definition of a function in an jupyter notebook cell. Joblib has
+    # some custom code to treat functions defined specifically in jupyter
+    # notebooks/ipython session -- we want to test this code, which requires
+    # the emulation to be rigorous.
+    for session_no in [0, 1]:
+        ipython_cell_source = '''
+        def f(x):
+            return x
+        '''
+
+        ipython_cell_id = '<ipython-input-{}-000000000000>'.format(session_no)
+
+        exec(
+            compile(
+                textwrap.dedent(ipython_cell_source),
+                filename=ipython_cell_id,
+                mode='exec'
+            )
+        )
+        # f is now accessible in the locals mapping - but for some unknown
+        # reason, f = locals()['f'] throws a KeyError at runtime, we need to
+        # bind locals()['f'] to a different name in the local namespace
+        aliased_f = locals()['f']
+        aliased_f.__module__ = "__main__"
+
+        # Preliminary sanity checks, and tests checking that joblib properly
+        # identified f as an interactive function defined in a jupyter notebook
+        assert aliased_f(1) == 1
+        assert aliased_f.__code__.co_filename == ipython_cell_id
+
+        memory = Memory(location=tmpdir.strpath, verbose=0)
+        cached_f = memory.cache(aliased_f)
+
+        assert len(os.listdir(tmpdir / 'joblib')) == 1
+        f_cache_relative_directory = os.listdir(tmpdir / 'joblib')[0]
+        assert 'ipython-input' in f_cache_relative_directory
+
+        f_cache_directory = tmpdir / 'joblib' / f_cache_relative_directory
+
+        if session_no == 0:
+            # The cache should be empty as cached_f has not been called yet.
+            assert os.listdir(f_cache_directory) == ['f']
+            assert os.listdir(f_cache_directory / 'f') == []
+
+            if call_before_reducing:
+                cached_f(3)
+                # Two files were just created, func_code.py, and a folder
+                # containing the information (inputs hash/ouptput) of
+                # cached_f(3)
+                assert len(os.listdir(f_cache_directory / 'f')) == 2
+
+                # Now, testing  #1035: when calling a cached function, joblib
+                # used to dynamically inspect the underlying function to
+                # extract its source code (to verify it matches the source code
+                # of the function as last inspected by joblib) -- however,
+                # source code introspection fails for dynamic functions sent to
+                # child processes - which would eventually make joblib clear
+                # the cache associated to f
+                res = Parallel(n_jobs=2)(delayed(cached_f)(i) for i in [1, 2])
+            else:
+                # Submit the function to the joblib child processes, although
+                # the function has never been called in the parent yet. This
+                # triggers a specific code branch inside
+                # MemorizedFunc.__reduce__.
+                res = Parallel(n_jobs=2)(delayed(cached_f)(i) for i in [1, 2])
+                assert len(os.listdir(f_cache_directory / 'f')) == 3
+
+                cached_f(3)
+
+            # Making sure f's cache does not get cleared after the parallel
+            # calls, and contains ALL cached functions calls (f(1), f(2), f(3))
+            # and 'func_code.py'
+            assert len(os.listdir(f_cache_directory / 'f')) == 4
+        else:
+            # For the second session, there should be an already existing cache
+            assert len(os.listdir(f_cache_directory / 'f')) == 4
+
+            cached_f(3)
+
+            # The previous cache should not be invalidated after calling the
+            # function in a new session
+            assert len(os.listdir(f_cache_directory / 'f')) == 4
+
+
+def test_no_memory():
+    """ Test memory with location=None: no memoize """
+    accumulator = list()
+
+    def ff(arg):
+        accumulator.append(1)
+        return arg
+
+    memory = Memory(location=None, verbose=0)
+    gg = memory.cache(ff)
+    for _ in range(4):
+        current_accumulator = len(accumulator)
+        gg(1)
+        assert len(accumulator) == current_accumulator + 1
+
+
+def test_memory_kwarg(tmpdir):
+    " Test memory with a function with keyword arguments."
+    accumulator = list()
+
+    def g(arg1=None, arg2=1):
+        accumulator.append(1)
+        return arg1
+
+    check_identity_lazy(g, accumulator, tmpdir.strpath)
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    g = memory.cache(g)
+    # Smoke test with an explicit keyword argument:
+    assert g(arg1=30, arg2=2) == 30
+
+
+def test_memory_lambda(tmpdir):
+    " Test memory with a function with a lambda."
+    accumulator = list()
+
+    def helper(x):
+        """ A helper function to define l as a lambda.
+        """
+        accumulator.append(1)
+        return x
+
+    check_identity_lazy(lambda x: helper(x), accumulator, tmpdir.strpath)
+
+
+def test_memory_name_collision(tmpdir):
+    " Check that name collisions with functions will raise warnings"
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    @memory.cache
+    def name_collision(x):
+        """ A first function called name_collision
+        """
+        return x
+
+    a = name_collision
+
+    @memory.cache
+    def name_collision(x):
+        """ A second function called name_collision
+        """
+        return x
+
+    b = name_collision
+
+    with warns(JobLibCollisionWarning) as warninfo:
+        a(1)
+        b(1)
+
+    assert len(warninfo) == 1
+    assert "collision" in str(warninfo[0].message)
+
+
+def test_memory_warning_lambda_collisions(tmpdir):
+    # Check that multiple use of lambda will raise collisions
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    a = memory.cache(lambda x: x)
+    b = memory.cache(lambda x: x + 1)
+
+    with warns(JobLibCollisionWarning) as warninfo:
+        assert a(0) == 0
+        assert b(1) == 2
+        assert a(1) == 1
+
+    # In recent Python versions, we can retrieve the code of lambdas,
+    # thus nothing is raised
+    assert len(warninfo) == 4
+
+
+def test_memory_warning_collision_detection(tmpdir):
+    # Check that collisions impossible to detect will raise appropriate
+    # warnings.
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    a1 = eval('lambda x: x')
+    a1 = memory.cache(a1)
+    b1 = eval('lambda x: x+1')
+    b1 = memory.cache(b1)
+
+    with warns(JobLibCollisionWarning) as warninfo:
+        a1(1)
+        b1(1)
+        a1(0)
+
+    assert len(warninfo) == 2
+    assert "cannot detect" in str(warninfo[0].message).lower()
+
+
+def test_memory_partial(tmpdir):
+    " Test memory with functools.partial."
+    accumulator = list()
+
+    def func(x, y):
+        """ A helper function to define l as a lambda.
+        """
+        accumulator.append(1)
+        return y
+
+    import functools
+    function = functools.partial(func, 1)
+
+    check_identity_lazy(function, accumulator, tmpdir.strpath)
+
+
+def test_memory_eval(tmpdir):
+    " Smoke test memory with a function with a function defined in an eval."
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    m = eval('lambda x: x')
+    mm = memory.cache(m)
+
+    assert mm(1) == 1
+
+
+def count_and_append(x=[]):
+    """ A function with a side effect in its arguments.
+
+        Return the length of its argument and append one element.
+    """
+    len_x = len(x)
+    x.append(None)
+    return len_x
+
+
+def test_argument_change(tmpdir):
+    """ Check that if a function has a side effect in its arguments, it
+        should use the hash of changing arguments.
+    """
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    func = memory.cache(count_and_append)
+    # call the function for the first time, is should cache it with
+    # argument x=[]
+    assert func() == 0
+    # the second time the argument is x=[None], which is not cached
+    # yet, so the functions should be called a second time
+    assert func() == 1
+
+
+@with_numpy
+@parametrize('mmap_mode', [None, 'r'])
+def test_memory_numpy(tmpdir, mmap_mode):
+    " Test memory with a function with numpy arrays."
+    accumulator = list()
+
+    def n(arg=None):
+        accumulator.append(1)
+        return arg
+
+    memory = Memory(location=tmpdir.strpath, mmap_mode=mmap_mode,
+                    verbose=0)
+    cached_n = memory.cache(n)
+
+    rnd = np.random.RandomState(0)
+    for i in range(3):
+        a = rnd.random_sample((10, 10))
+        for _ in range(3):
+            assert np.all(cached_n(a) == a)
+            assert len(accumulator) == i + 1
+
+
+@with_numpy
+def test_memory_numpy_check_mmap_mode(tmpdir, monkeypatch):
+    """Check that mmap_mode is respected even at the first call"""
+
+    memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
+
+    @memory.cache()
+    def twice(a):
+        return a * 2
+
+    a = np.ones(3)
+
+    b = twice(a)
+    c = twice(a)
+
+    assert isinstance(c, np.memmap)
+    assert c.mode == 'r'
+
+    assert isinstance(b, np.memmap)
+    assert b.mode == 'r'
+
+    # Corrupts the file,  Deleting b and c mmaps
+    # is necessary to be able edit the file
+    del b
+    del c
+    gc.collect()
+    corrupt_single_cache_item(memory)
+
+    # Make sure that corrupting the file causes recomputation and that
+    # a warning is issued.
+    recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
+    d = twice(a)
+    assert len(recorded_warnings) == 1
+    exception_msg = 'Exception while loading results'
+    assert exception_msg in recorded_warnings[0]
+    # Asserts that the recomputation returns a mmap
+    assert isinstance(d, np.memmap)
+    assert d.mode == 'r'
+
+
+def test_memory_exception(tmpdir):
+    """ Smoketest the exception handling of Memory.
+    """
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    class MyException(Exception):
+        pass
+
+    @memory.cache
+    def h(exc=0):
+        if exc:
+            raise MyException
+
+    # Call once, to initialise the cache
+    h()
+
+    for _ in range(3):
+        # Call 3 times, to be sure that the Exception is always raised
+        with raises(MyException):
+            h(1)
+
+
+def test_memory_ignore(tmpdir):
+    " Test the ignore feature of memory "
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    accumulator = list()
+
+    @memory.cache(ignore=['y'])
+    def z(x, y=1):
+        accumulator.append(1)
+
+    assert z.ignore == ['y']
+
+    z(0, y=1)
+    assert len(accumulator) == 1
+    z(0, y=1)
+    assert len(accumulator) == 1
+    z(0, y=2)
+    assert len(accumulator) == 1
+
+
+def test_memory_ignore_decorated(tmpdir):
+    " Test the ignore feature of memory on a decorated function "
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    accumulator = list()
+
+    def decorate(f):
+        @functools.wraps(f)
+        def wrapped(*args, **kwargs):
+            return f(*args, **kwargs)
+        return wrapped
+
+    @memory.cache(ignore=['y'])
+    @decorate
+    def z(x, y=1):
+        accumulator.append(1)
+
+    assert z.ignore == ['y']
+
+    z(0, y=1)
+    assert len(accumulator) == 1
+    z(0, y=1)
+    assert len(accumulator) == 1
+    z(0, y=2)
+    assert len(accumulator) == 1
+
+
+def test_memory_args_as_kwargs(tmpdir):
+    """Non-regression test against 0.12.0 changes.
+
+    https://github.com/joblib/joblib/pull/751
+    """
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    @memory.cache
+    def plus_one(a):
+        return a + 1
+
+    # It's possible to call a positional arg as a kwarg.
+    assert plus_one(1) == 2
+    assert plus_one(a=1) == 2
+
+    # However, a positional argument that joblib hadn't seen
+    # before would cause a failure if it was passed as a kwarg.
+    assert plus_one(a=2) == 3
+
+
+@parametrize('ignore, verbose, mmap_mode', [(['x'], 100, 'r'),
+                                            ([], 10, None)])
+def test_partial_decoration(tmpdir, ignore, verbose, mmap_mode):
+    "Check cache may be called with kwargs before decorating"
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    @memory.cache(ignore=ignore, verbose=verbose, mmap_mode=mmap_mode)
+    def z(x):
+        pass
+
+    assert z.ignore == ignore
+    assert z._verbose == verbose
+    assert z.mmap_mode == mmap_mode
+
+
+def test_func_dir(tmpdir):
+    # Test the creation of the memory cache directory for the function.
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    path = __name__.split('.')
+    path.append('f')
+    path = tmpdir.join('joblib', *path).strpath
+
+    g = memory.cache(f)
+    # Test that the function directory is created on demand
+    func_id = _build_func_identifier(f)
+    location = os.path.join(g.store_backend.location, func_id)
+    assert location == path
+    assert os.path.exists(path)
+    assert memory.location == os.path.dirname(g.store_backend.location)
+
+    # Test that the code is stored.
+    # For the following test to be robust to previous execution, we clear
+    # the in-memory store
+    _FUNCTION_HASHES.clear()
+    assert not g._check_previous_func_code()
+    assert os.path.exists(os.path.join(path, 'func_code.py'))
+    assert g._check_previous_func_code()
+
+    # Test the robustness to failure of loading previous results.
+    args_id = g._get_args_id(1)
+    output_dir = os.path.join(g.store_backend.location, g.func_id, args_id)
+    a = g(1)
+    assert os.path.exists(output_dir)
+    os.remove(os.path.join(output_dir, 'output.pkl'))
+    assert a == g(1)
+
+
+def test_persistence(tmpdir):
+    # Test the memorized functions can be pickled and restored.
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    g = memory.cache(f)
+    output = g(1)
+
+    h = pickle.loads(pickle.dumps(g))
+
+    args_id = h._get_args_id(1)
+    output_dir = os.path.join(h.store_backend.location, h.func_id, args_id)
+    assert os.path.exists(output_dir)
+    assert output == h.store_backend.load_item([h.func_id, args_id])
+    memory2 = pickle.loads(pickle.dumps(memory))
+    assert memory.store_backend.location == memory2.store_backend.location
+
+    # Smoke test that pickling a memory with location=None works
+    memory = Memory(location=None, verbose=0)
+    pickle.loads(pickle.dumps(memory))
+    g = memory.cache(f)
+    gp = pickle.loads(pickle.dumps(g))
+    gp(1)
+
+
+def test_check_call_in_cache(tmpdir):
+    for func in (MemorizedFunc(f, tmpdir.strpath),
+                 Memory(location=tmpdir.strpath, verbose=0).cache(f)):
+        result = func.check_call_in_cache(2)
+        assert not result
+        assert isinstance(result, bool)
+        assert func(2) == 5
+        result = func.check_call_in_cache(2)
+        assert result
+        assert isinstance(result, bool)
+        func.clear()
+
+
+def test_call_and_shelve(tmpdir):
+    # Test MemorizedFunc outputting a reference to cache.
+
+    for func, Result in zip((MemorizedFunc(f, tmpdir.strpath),
+                             NotMemorizedFunc(f),
+                             Memory(location=tmpdir.strpath,
+                                    verbose=0).cache(f),
+                             Memory(location=None).cache(f),
+                             ),
+                            (MemorizedResult, NotMemorizedResult,
+                             MemorizedResult, NotMemorizedResult)):
+        assert func(2) == 5
+        result = func.call_and_shelve(2)
+        assert isinstance(result, Result)
+        assert result.get() == 5
+
+        result.clear()
+        with raises(KeyError):
+            result.get()
+        result.clear()  # Do nothing if there is no cache.
+
+
+def test_call_and_shelve_argument_hash(tmpdir):
+    # Verify that a warning is raised when accessing arguments_hash
+    # attribute from MemorizedResult
+    func = Memory(location=tmpdir.strpath, verbose=0).cache(f)
+    result = func.call_and_shelve(2)
+    assert isinstance(result, MemorizedResult)
+    with warns(DeprecationWarning) as w:
+        assert result.argument_hash == result.args_id
+    assert len(w) == 1
+    assert "The 'argument_hash' attribute has been deprecated" \
+        in str(w[-1].message)
+
+
+def test_call_and_shelve_lazily_load_stored_result(tmpdir):
+    """Check call_and_shelve only load stored data if needed."""
+    test_access_time_file = tmpdir.join('test_access')
+    test_access_time_file.write('test_access')
+    test_access_time = os.stat(test_access_time_file.strpath).st_atime
+    # check file system access time stats resolution is lower than test wait
+    # timings.
+    time.sleep(0.5)
+    assert test_access_time_file.read() == 'test_access'
+
+    if test_access_time == os.stat(test_access_time_file.strpath).st_atime:
+        # Skip this test when access time cannot be retrieved with enough
+        # precision from the file system (e.g. NTFS on windows).
+        pytest.skip("filesystem does not support fine-grained access time "
+                    "attribute")
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    func = memory.cache(f)
+    args_id = func._get_args_id(2)
+    result_path = os.path.join(memory.store_backend.location,
+                               func.func_id, args_id, 'output.pkl')
+    assert func(2) == 5
+    first_access_time = os.stat(result_path).st_atime
+    time.sleep(1)
+
+    # Should not access the stored data
+    result = func.call_and_shelve(2)
+    assert isinstance(result, MemorizedResult)
+    assert os.stat(result_path).st_atime == first_access_time
+    time.sleep(1)
+
+    # Read the stored data => last access time is greater than first_access
+    assert result.get() == 5
+    assert os.stat(result_path).st_atime > first_access_time
+
+
+def test_memorized_pickling(tmpdir):
+    for func in (MemorizedFunc(f, tmpdir.strpath), NotMemorizedFunc(f)):
+        filename = tmpdir.join('pickling_test.dat').strpath
+        result = func.call_and_shelve(2)
+        with open(filename, 'wb') as fp:
+            pickle.dump(result, fp)
+        with open(filename, 'rb') as fp:
+            result2 = pickle.load(fp)
+        assert result2.get() == result.get()
+        os.remove(filename)
+
+
+def test_memorized_repr(tmpdir):
+    func = MemorizedFunc(f, tmpdir.strpath)
+    result = func.call_and_shelve(2)
+
+    func2 = MemorizedFunc(f, tmpdir.strpath)
+    result2 = func2.call_and_shelve(2)
+    assert result.get() == result2.get()
+    assert repr(func) == repr(func2)
+
+    # Smoke test with NotMemorizedFunc
+    func = NotMemorizedFunc(f)
+    repr(func)
+    repr(func.call_and_shelve(2))
+
+    # Smoke test for message output (increase code coverage)
+    func = MemorizedFunc(f, tmpdir.strpath, verbose=11, timestamp=time.time())
+    result = func.call_and_shelve(11)
+    result.get()
+
+    func = MemorizedFunc(f, tmpdir.strpath, verbose=11)
+    result = func.call_and_shelve(11)
+    result.get()
+
+    func = MemorizedFunc(f, tmpdir.strpath, verbose=5, timestamp=time.time())
+    result = func.call_and_shelve(11)
+    result.get()
+
+    func = MemorizedFunc(f, tmpdir.strpath, verbose=5)
+    result = func.call_and_shelve(11)
+    result.get()
+
+
+def test_memory_file_modification(capsys, tmpdir, monkeypatch):
+    # Test that modifying a Python file after loading it does not lead to
+    # Recomputation
+    dir_name = tmpdir.mkdir('tmp_import').strpath
+    filename = os.path.join(dir_name, 'tmp_joblib_.py')
+    content = 'def f(x):\n    print(x)\n    return x\n'
+    with open(filename, 'w') as module_file:
+        module_file.write(content)
+
+    # Load the module:
+    monkeypatch.syspath_prepend(dir_name)
+    import tmp_joblib_ as tmp
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    f = memory.cache(tmp.f)
+    # First call f a few times
+    f(1)
+    f(2)
+    f(1)
+
+    # Now modify the module where f is stored without modifying f
+    with open(filename, 'w') as module_file:
+        module_file.write('\n\n' + content)
+
+    # And call f a couple more times
+    f(1)
+    f(1)
+
+    # Flush the .pyc files
+    shutil.rmtree(dir_name)
+    os.mkdir(dir_name)
+    # Now modify the module where f is stored, modifying f
+    content = 'def f(x):\n    print("x=%s" % x)\n    return x\n'
+    with open(filename, 'w') as module_file:
+        module_file.write(content)
+
+    # And call f more times prior to reloading: the cache should not be
+    # invalidated at this point as the active function definition has not
+    # changed in memory yet.
+    f(1)
+    f(1)
+
+    # Now reload
+    sys.stdout.write('Reloading\n')
+    sys.modules.pop('tmp_joblib_')
+    import tmp_joblib_ as tmp
+    f = memory.cache(tmp.f)
+
+    # And call f more times
+    f(1)
+    f(1)
+
+    out, err = capsys.readouterr()
+    assert out == '1\n2\nReloading\nx=1\n'
+
+
+def _function_to_cache(a, b):
+    # Just a place holder function to be mutated by tests
+    pass
+
+
+def _sum(a, b):
+    return a + b
+
+
+def _product(a, b):
+    return a * b
+
+
+def test_memory_in_memory_function_code_change(tmpdir):
+    _function_to_cache.__code__ = _sum.__code__
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    f = memory.cache(_function_to_cache)
+
+    assert f(1, 2) == 3
+    assert f(1, 2) == 3
+
+    with warns(JobLibCollisionWarning):
+        # Check that inline function modification triggers a cache invalidation
+        _function_to_cache.__code__ = _product.__code__
+        assert f(1, 2) == 2
+        assert f(1, 2) == 2
+
+
+def test_clear_memory_with_none_location():
+    memory = Memory(location=None)
+    memory.clear()
+
+
+def func_with_kwonly_args(a, b, *, kw1='kw1', kw2='kw2'):
+    return a, b, kw1, kw2
+
+
+def func_with_signature(a: int, b: float) -> float:
+    return a + b
+
+
+def test_memory_func_with_kwonly_args(tmpdir):
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    func_cached = memory.cache(func_with_kwonly_args)
+
+    assert func_cached(1, 2, kw1=3) == (1, 2, 3, 'kw2')
+
+    # Making sure that providing a keyword-only argument by
+    # position raises an exception
+    with raises(ValueError) as excinfo:
+        func_cached(1, 2, 3, kw2=4)
+    excinfo.match("Keyword-only parameter 'kw1' was passed as positional "
+                  "parameter")
+
+    # Keyword-only parameter passed by position with cached call
+    # should still raise ValueError
+    func_cached(1, 2, kw1=3, kw2=4)
+
+    with raises(ValueError) as excinfo:
+        func_cached(1, 2, 3, kw2=4)
+    excinfo.match("Keyword-only parameter 'kw1' was passed as positional "
+                  "parameter")
+
+    # Test 'ignore' parameter
+    func_cached = memory.cache(func_with_kwonly_args, ignore=['kw2'])
+    assert func_cached(1, 2, kw1=3, kw2=4) == (1, 2, 3, 4)
+    assert func_cached(1, 2, kw1=3, kw2='ignored') == (1, 2, 3, 4)
+
+
+def test_memory_func_with_signature(tmpdir):
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    func_cached = memory.cache(func_with_signature)
+
+    assert func_cached(1, 2.) == 3.
+
+
+def _setup_toy_cache(tmpdir, num_inputs=10):
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    @memory.cache()
+    def get_1000_bytes(arg):
+        return 'a' * 1000
+
+    inputs = list(range(num_inputs))
+    for arg in inputs:
+        get_1000_bytes(arg)
+
+    func_id = _build_func_identifier(get_1000_bytes)
+    hash_dirnames = [get_1000_bytes._get_args_id(arg)
+                     for arg in inputs]
+
+    full_hashdirs = [os.path.join(get_1000_bytes.store_backend.location,
+                                  func_id, dirname)
+                     for dirname in hash_dirnames]
+    return memory, full_hashdirs, get_1000_bytes
+
+
+def test__get_items(tmpdir):
+    memory, expected_hash_dirs, _ = _setup_toy_cache(tmpdir)
+    items = memory.store_backend.get_items()
+    hash_dirs = [ci.path for ci in items]
+    assert set(hash_dirs) == set(expected_hash_dirs)
+
+    def get_files_size(directory):
+        full_paths = [os.path.join(directory, fn)
+                      for fn in os.listdir(directory)]
+        return sum(os.path.getsize(fp) for fp in full_paths)
+
+    expected_hash_cache_sizes = [get_files_size(hash_dir)
+                                 for hash_dir in hash_dirs]
+    hash_cache_sizes = [ci.size for ci in items]
+    assert hash_cache_sizes == expected_hash_cache_sizes
+
+    output_filenames = [os.path.join(hash_dir, 'output.pkl')
+                        for hash_dir in hash_dirs]
+
+    expected_last_accesses = [
+        datetime.datetime.fromtimestamp(os.path.getatime(fn))
+        for fn in output_filenames]
+    last_accesses = [ci.last_access for ci in items]
+    assert last_accesses == expected_last_accesses
+
+
+def test__get_items_to_delete(tmpdir):
+    # test empty cache
+    memory, _, _ = _setup_toy_cache(tmpdir, num_inputs=0)
+    items_to_delete = memory.store_backend._get_items_to_delete('1K')
+    assert items_to_delete == []
+
+    memory, expected_hash_cachedirs, _ = _setup_toy_cache(tmpdir)
+    items = memory.store_backend.get_items()
+    # bytes_limit set to keep only one cache item (each hash cache
+    # folder is about 1000 bytes + metadata)
+    items_to_delete = memory.store_backend._get_items_to_delete('2K')
+    nb_hashes = len(expected_hash_cachedirs)
+    assert set.issubset(set(items_to_delete), set(items))
+    assert len(items_to_delete) == nb_hashes - 1
+
+    # Sanity check bytes_limit=2048 is the same as bytes_limit='2K'
+    items_to_delete_2048b = memory.store_backend._get_items_to_delete(2048)
+    assert sorted(items_to_delete) == sorted(items_to_delete_2048b)
+
+    # bytes_limit greater than the size of the cache
+    items_to_delete_empty = memory.store_backend._get_items_to_delete('1M')
+    assert items_to_delete_empty == []
+
+    # All the cache items need to be deleted
+    bytes_limit_too_small = 500
+    items_to_delete_500b = memory.store_backend._get_items_to_delete(
+        bytes_limit_too_small
+    )
+    assert set(items_to_delete_500b), set(items)
+
+    # Test LRU property: surviving cache items should all have a more
+    # recent last_access that the ones that have been deleted
+    items_to_delete_6000b = memory.store_backend._get_items_to_delete(6000)
+    surviving_items = set(items).difference(items_to_delete_6000b)
+
+    assert (max(ci.last_access for ci in items_to_delete_6000b) <=
+            min(ci.last_access for ci in surviving_items))
+
+
+def test_memory_reduce_size_bytes_limit(tmpdir):
+    memory, _, _ = _setup_toy_cache(tmpdir)
+    ref_cache_items = memory.store_backend.get_items()
+
+    # By default memory.bytes_limit is None and reduce_size is a noop
+    memory.reduce_size()
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # No cache items deleted if bytes_limit greater than the size of
+    # the cache
+    memory.reduce_size(bytes_limit='1M')
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # bytes_limit is set so that only two cache items are kept
+    memory.reduce_size(bytes_limit='3K')
+    cache_items = memory.store_backend.get_items()
+    assert set.issubset(set(cache_items), set(ref_cache_items))
+    assert len(cache_items) == 2
+
+    # bytes_limit set so that no cache item is kept
+    bytes_limit_too_small = 500
+    memory.reduce_size(bytes_limit=bytes_limit_too_small)
+    cache_items = memory.store_backend.get_items()
+    assert cache_items == []
+
+
+def test_memory_reduce_size_items_limit(tmpdir):
+    memory, _, _ = _setup_toy_cache(tmpdir)
+    ref_cache_items = memory.store_backend.get_items()
+
+    # By default reduce_size is a noop
+    memory.reduce_size()
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # No cache items deleted if items_limit greater than the size of
+    # the cache
+    memory.reduce_size(items_limit=10)
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # items_limit is set so that only two cache items are kept
+    memory.reduce_size(items_limit=2)
+    cache_items = memory.store_backend.get_items()
+    assert set.issubset(set(cache_items), set(ref_cache_items))
+    assert len(cache_items) == 2
+
+    # item_limit set so that no cache item is kept
+    memory.reduce_size(items_limit=0)
+    cache_items = memory.store_backend.get_items()
+    assert cache_items == []
+
+
+def test_memory_reduce_size_age_limit(tmpdir):
+    import time
+    import datetime
+    memory, _, put_cache = _setup_toy_cache(tmpdir)
+    ref_cache_items = memory.store_backend.get_items()
+
+    # By default reduce_size is a noop
+    memory.reduce_size()
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # No cache items deleted if age_limit big.
+    memory.reduce_size(age_limit=datetime.timedelta(days=1))
+    cache_items = memory.store_backend.get_items()
+    assert sorted(ref_cache_items) == sorted(cache_items)
+
+    # age_limit is set so that only two cache items are kept
+    time.sleep(1)
+    put_cache(-1)
+    put_cache(-2)
+    memory.reduce_size(age_limit=datetime.timedelta(seconds=1))
+    cache_items = memory.store_backend.get_items()
+    assert not set.issubset(set(cache_items), set(ref_cache_items))
+    assert len(cache_items) == 2
+
+    # age_limit set so that no cache item is kept
+    memory.reduce_size(age_limit=datetime.timedelta(seconds=0))
+    cache_items = memory.store_backend.get_items()
+    assert cache_items == []
+
+
+def test_memory_clear(tmpdir):
+    memory, _, g = _setup_toy_cache(tmpdir)
+    memory.clear()
+
+    assert os.listdir(memory.store_backend.location) == []
+
+    # Check that the cache for functions hash is also reset.
+    assert not g._check_previous_func_code(stacklevel=4)
+
+
+def fast_func_with_complex_output():
+    complex_obj = ['a' * 1000] * 1000
+    return complex_obj
+
+
+def fast_func_with_conditional_complex_output(complex_output=True):
+    complex_obj = {str(i): i for i in range(int(1e5))}
+    return complex_obj if complex_output else 'simple output'
+
+
+@with_multiprocessing
+def test_cached_function_race_condition_when_persisting_output(tmpdir, capfd):
+    # Test race condition where multiple processes are writing into
+    # the same output.pkl. See
+    # https://github.com/joblib/joblib/issues/490 for more details.
+    memory = Memory(location=tmpdir.strpath)
+    func_cached = memory.cache(fast_func_with_complex_output)
+
+    Parallel(n_jobs=2)(delayed(func_cached)() for i in range(3))
+
+    stdout, stderr = capfd.readouterr()
+
+    # Checking both stdout and stderr (ongoing PR #434 may change
+    # logging destination) to make sure there is no exception while
+    # loading the results
+    exception_msg = 'Exception while loading results'
+    assert exception_msg not in stdout
+    assert exception_msg not in stderr
+
+
+@with_multiprocessing
+def test_cached_function_race_condition_when_persisting_output_2(tmpdir,
+                                                                 capfd):
+    # Test race condition in first attempt at solving
+    # https://github.com/joblib/joblib/issues/490. The race condition
+    # was due to the delay between seeing the cache directory created
+    # (interpreted as the result being cached) and the output.pkl being
+    # pickled.
+    memory = Memory(location=tmpdir.strpath)
+    func_cached = memory.cache(fast_func_with_conditional_complex_output)
+
+    Parallel(n_jobs=2)(delayed(func_cached)(True if i % 2 == 0 else False)
+                       for i in range(3))
+
+    stdout, stderr = capfd.readouterr()
+
+    # Checking both stdout and stderr (ongoing PR #434 may change
+    # logging destination) to make sure there is no exception while
+    # loading the results
+    exception_msg = 'Exception while loading results'
+    assert exception_msg not in stdout
+    assert exception_msg not in stderr
+
+
+def test_memory_recomputes_after_an_error_while_loading_results(
+        tmpdir, monkeypatch):
+    memory = Memory(location=tmpdir.strpath)
+
+    def func(arg):
+        # This makes sure that the timestamp returned by two calls of
+        # func are different. This is needed on Windows where
+        # time.time resolution may not be accurate enough
+        time.sleep(0.01)
+        return arg, time.time()
+
+    cached_func = memory.cache(func)
+    input_arg = 'arg'
+    arg, timestamp = cached_func(input_arg)
+
+    # Make sure the function is correctly cached
+    assert arg == input_arg
+
+    # Corrupting output.pkl to make sure that an error happens when
+    # loading the cached result
+    corrupt_single_cache_item(memory)
+
+    # Make sure that corrupting the file causes recomputation and that
+    # a warning is issued.
+    recorded_warnings = monkeypatch_cached_func_warn(cached_func, monkeypatch)
+    recomputed_arg, recomputed_timestamp = cached_func(arg)
+    assert len(recorded_warnings) == 1
+    exception_msg = 'Exception while loading results'
+    assert exception_msg in recorded_warnings[0]
+    assert recomputed_arg == arg
+    assert recomputed_timestamp > timestamp
+
+    # Corrupting output.pkl to make sure that an error happens when
+    # loading the cached result
+    corrupt_single_cache_item(memory)
+    reference = cached_func.call_and_shelve(arg)
+    try:
+        reference.get()
+        raise AssertionError(
+            "It normally not possible to load a corrupted"
+            " MemorizedResult"
+        )
+    except KeyError as e:
+        message = "is corrupted"
+        assert message in str(e.args)
+
+
+class IncompleteStoreBackend(StoreBackendBase):
+    """This backend cannot be instantiated and should raise a TypeError."""
+    pass
+
+
+class DummyStoreBackend(StoreBackendBase):
+    """A dummy store backend that does nothing."""
+
+    def _open_item(self, *args, **kwargs):
+        """Open an item on store."""
+        "Does nothing"
+
+    def _item_exists(self, location):
+        """Check if an item location exists."""
+        "Does nothing"
+
+    def _move_item(self, src, dst):
+        """Move an item from src to dst in store."""
+        "Does nothing"
+
+    def create_location(self, location):
+        """Create location on store."""
+        "Does nothing"
+
+    def exists(self, obj):
+        """Check if an object exists in the store"""
+        return False
+
+    def clear_location(self, obj):
+        """Clear object on store"""
+        "Does nothing"
+
+    def get_items(self):
+        """Returns the whole list of items available in cache."""
+        return []
+
+    def configure(self, location, *args, **kwargs):
+        """Configure the store"""
+        "Does nothing"
+
+
+@parametrize("invalid_prefix", [None, dict(), list()])
+def test_register_invalid_store_backends_key(invalid_prefix):
+    # verify the right exceptions are raised when passing a wrong backend key.
+    with raises(ValueError) as excinfo:
+        register_store_backend(invalid_prefix, None)
+    excinfo.match(r'Store backend name should be a string*')
+
+
+def test_register_invalid_store_backends_object():
+    # verify the right exceptions are raised when passing a wrong backend
+    # object.
+    with raises(ValueError) as excinfo:
+        register_store_backend("fs", None)
+    excinfo.match(r'Store backend should inherit StoreBackendBase*')
+
+
+def test_memory_default_store_backend():
+    # test an unknown backend falls back into a FileSystemStoreBackend
+    with raises(TypeError) as excinfo:
+        Memory(location='/tmp/joblib', backend='unknown')
+    excinfo.match(r"Unknown location*")
+
+
+def test_warning_on_unknown_location_type():
+    class NonSupportedLocationClass:
+        pass
+    unsupported_location = NonSupportedLocationClass()
+
+    with warns(UserWarning) as warninfo:
+        _store_backend_factory("local", location=unsupported_location)
+
+    expected_mesage = ("Instantiating a backend using a "
+                       "NonSupportedLocationClass as a location is not "
+                       "supported by joblib")
+    assert expected_mesage in str(warninfo[0].message)
+
+
+def test_instanciate_incomplete_store_backend():
+    # Verify that registering an external incomplete store backend raises an
+    # exception when one tries to instantiate it.
+    backend_name = "isb"
+    register_store_backend(backend_name, IncompleteStoreBackend)
+    assert (backend_name, IncompleteStoreBackend) in _STORE_BACKENDS.items()
+    with raises(TypeError) as excinfo:
+        _store_backend_factory(backend_name, "fake_location")
+    excinfo.match(r"Can't instantiate abstract class IncompleteStoreBackend "
+                  "(without an implementation for|with) abstract methods*")
+
+
+def test_dummy_store_backend():
+    # Verify that registering an external store backend works.
+
+    backend_name = "dsb"
+    register_store_backend(backend_name, DummyStoreBackend)
+    assert (backend_name, DummyStoreBackend) in _STORE_BACKENDS.items()
+
+    backend_obj = _store_backend_factory(backend_name, "dummy_location")
+    assert isinstance(backend_obj, DummyStoreBackend)
+
+
+def test_instanciate_store_backend_with_pathlib_path():
+    # Instantiate a FileSystemStoreBackend using a pathlib.Path object
+    path = pathlib.Path("some_folder")
+    backend_obj = _store_backend_factory("local", path)
+    assert backend_obj.location == "some_folder"
+
+
+def test_filesystem_store_backend_repr(tmpdir):
+    # Verify string representation of a filesystem store backend.
+
+    repr_pattern = 'FileSystemStoreBackend(location="{location}")'
+    backend = FileSystemStoreBackend()
+    assert backend.location is None
+
+    repr(backend)  # Should not raise an exception
+
+    assert str(backend) == repr_pattern.format(location=None)
+
+    # backend location is passed explicitly via the configure method (called
+    # by the internal _store_backend_factory function)
+    backend.configure(tmpdir.strpath)
+
+    assert str(backend) == repr_pattern.format(location=tmpdir.strpath)
+
+    repr(backend)  # Should not raise an exception
+
+
+def test_memory_objects_repr(tmpdir):
+    # Verify printable reprs of MemorizedResult, MemorizedFunc and Memory.
+
+    def my_func(a, b):
+        return a + b
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    memorized_func = memory.cache(my_func)
+
+    memorized_func_repr = 'MemorizedFunc(func={func}, location={location})'
+
+    assert str(memorized_func) == memorized_func_repr.format(
+        func=my_func,
+        location=memory.store_backend.location)
+
+    memorized_result = memorized_func.call_and_shelve(42, 42)
+
+    memorized_result_repr = ('MemorizedResult(location="{location}", '
+                             'func="{func}", args_id="{args_id}")')
+
+    assert str(memorized_result) == memorized_result_repr.format(
+        location=memory.store_backend.location,
+        func=memorized_result.func_id,
+        args_id=memorized_result.args_id)
+
+    assert str(memory) == 'Memory(location={location})'.format(
+        location=memory.store_backend.location)
+
+
+def test_memorized_result_pickle(tmpdir):
+    # Verify a MemoryResult object can be pickled/depickled. Non regression
+    # test introduced following issue
+    # https://github.com/joblib/joblib/issues/747
+
+    memory = Memory(location=tmpdir.strpath)
+
+    @memory.cache
+    def g(x):
+        return x**2
+
+    memorized_result = g.call_and_shelve(4)
+    memorized_result_pickle = pickle.dumps(memorized_result)
+    memorized_result_loads = pickle.loads(memorized_result_pickle)
+
+    assert memorized_result.store_backend.location == \
+        memorized_result_loads.store_backend.location
+    assert memorized_result.func == memorized_result_loads.func
+    assert memorized_result.args_id == memorized_result_loads.args_id
+    assert str(memorized_result) == str(memorized_result_loads)
+
+
+def compare(left, right, ignored_attrs=None):
+    if ignored_attrs is None:
+        ignored_attrs = []
+
+    left_vars = vars(left)
+    right_vars = vars(right)
+    assert set(left_vars.keys()) == set(right_vars.keys())
+    for attr in left_vars.keys():
+        if attr in ignored_attrs:
+            continue
+        assert left_vars[attr] == right_vars[attr]
+
+
+@pytest.mark.parametrize('memory_kwargs',
+                         [{'compress': 3, 'verbose': 2},
+                          {'mmap_mode': 'r', 'verbose': 5,
+                           'backend_options': {'parameter': 'unused'}}])
+def test_memory_pickle_dump_load(tmpdir, memory_kwargs):
+    memory = Memory(location=tmpdir.strpath, **memory_kwargs)
+
+    memory_reloaded = pickle.loads(pickle.dumps(memory))
+
+    # Compare Memory instance before and after pickle roundtrip
+    compare(memory.store_backend, memory_reloaded.store_backend)
+    compare(memory, memory_reloaded,
+            ignored_attrs=set(['store_backend', 'timestamp', '_func_code_id']))
+    assert hash(memory) == hash(memory_reloaded)
+
+    func_cached = memory.cache(f)
+
+    func_cached_reloaded = pickle.loads(pickle.dumps(func_cached))
+
+    # Compare MemorizedFunc instance before/after pickle roundtrip
+    compare(func_cached.store_backend, func_cached_reloaded.store_backend)
+    compare(func_cached, func_cached_reloaded,
+            ignored_attrs=set(['store_backend', 'timestamp', '_func_code_id']))
+    assert hash(func_cached) == hash(func_cached_reloaded)
+
+    # Compare MemorizedResult instance before/after pickle roundtrip
+    memorized_result = func_cached.call_and_shelve(1)
+    memorized_result_reloaded = pickle.loads(pickle.dumps(memorized_result))
+
+    compare(memorized_result.store_backend,
+            memorized_result_reloaded.store_backend)
+    compare(memorized_result, memorized_result_reloaded,
+            ignored_attrs=set(['store_backend', 'timestamp', '_func_code_id']))
+    assert hash(memorized_result) == hash(memorized_result_reloaded)
+
+
+def test_info_log(tmpdir, caplog):
+    caplog.set_level(logging.INFO)
+    x = 3
+
+    memory = Memory(location=tmpdir.strpath, verbose=20)
+
+    @memory.cache
+    def f(x):
+        return x ** 2
+
+    _ = f(x)
+    assert "Querying" in caplog.text
+    caplog.clear()
+
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+
+    @memory.cache
+    def f(x):
+        return x ** 2
+
+    _ = f(x)
+    assert "Querying" not in caplog.text
+    caplog.clear()
+
+
+def test_deprecated_bytes_limit(tmpdir):
+    from joblib import __version__
+    if __version__ >= "1.5":
+        raise DeprecationWarning(
+            "Bytes limit is deprecated and should be removed by 1.4"
+        )
+    with pytest.warns(DeprecationWarning, match="bytes_limit"):
+        _ = Memory(location=tmpdir.strpath, bytes_limit='1K')
+
+
+class TestCacheValidationCallback:
+    "Tests on parameter `cache_validation_callback`"
+
+    def foo(self, x, d, delay=None):
+        d["run"] = True
+        if delay is not None:
+            time.sleep(delay)
+        return x * 2
+
+    def test_invalid_cache_validation_callback(self, memory):
+        "Test invalid values for `cache_validation_callback"
+        match = "cache_validation_callback needs to be callable. Got True."
+        with pytest.raises(ValueError, match=match):
+            memory.cache(cache_validation_callback=True)
+
+    @pytest.mark.parametrize("consider_cache_valid", [True, False])
+    def test_constant_cache_validation_callback(
+            self, memory, consider_cache_valid
+    ):
+        "Test expiry of old results"
+        f = memory.cache(
+            self.foo, cache_validation_callback=lambda _: consider_cache_valid,
+            ignore=["d"]
+        )
+
+        d1, d2 = {"run": False}, {"run": False}
+        assert f(2, d1) == 4
+        assert f(2, d2) == 4
+
+        assert d1["run"]
+        assert d2["run"] != consider_cache_valid
+
+    def test_memory_only_cache_long_run(self, memory):
+        "Test cache validity based on run duration."
+
+        def cache_validation_callback(metadata):
+            duration = metadata['duration']
+            if duration > 0.1:
+                return True
+
+        f = memory.cache(
+            self.foo, cache_validation_callback=cache_validation_callback,
+            ignore=["d"]
+        )
+
+        # Short run are not cached
+        d1, d2 = {"run": False}, {"run": False}
+        assert f(2, d1, delay=0) == 4
+        assert f(2, d2, delay=0) == 4
+        assert d1["run"]
+        assert d2["run"]
+
+        # Longer run are cached
+        d1, d2 = {"run": False}, {"run": False}
+        assert f(2, d1, delay=0.2) == 4
+        assert f(2, d2, delay=0.2) == 4
+        assert d1["run"]
+        assert not d2["run"]
+
+    def test_memory_expires_after(self, memory):
+        "Test expiry of old cached results"
+
+        f = memory.cache(
+            self.foo, cache_validation_callback=expires_after(seconds=.3),
+            ignore=["d"]
+        )
+
+        d1, d2, d3 = {"run": False}, {"run": False}, {"run": False}
+        assert f(2, d1) == 4
+        assert f(2, d2) == 4
+        time.sleep(.5)
+        assert f(2, d3) == 4
+
+        assert d1["run"]
+        assert not d2["run"]
+        assert d3["run"]
+
+
+class TestMemorizedFunc:
+    "Tests for the MemorizedFunc and NotMemorizedFunc classes"
+
+    @staticmethod
+    def f(x, counter):
+        counter[x] = counter.get(x, 0) + 1
+        return counter[x]
+
+    def test_call_method_memorized(self, memory):
+        "Test calling the function"
+
+        f = memory.cache(self.f, ignore=['counter'])
+
+        counter = {}
+        assert f(2, counter) == 1
+        assert f(2, counter) == 1
+
+        x, meta = f.call(2, counter)
+        assert x == 2, "f has not been called properly"
+        assert isinstance(meta, dict), (
+            "Metadata are not returned by MemorizedFunc.call."
+        )
+
+    def test_call_method_not_memorized(self, memory):
+        "Test calling the function"
+
+        f = NotMemorizedFunc(self.f)
+
+        counter = {}
+        assert f(2, counter) == 1
+        assert f(2, counter) == 2
+
+        x, meta = f.call(2, counter)
+        assert x == 3, "f has not been called properly"
+        assert isinstance(meta, dict), (
+            "Metadata are not returned by MemorizedFunc.call."
+        )

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_memory_async.py

@@ -0,0 +1,170 @@
+import asyncio
+import gc
+import shutil
+
+import pytest
+
+from joblib.memory import (AsyncMemorizedFunc, AsyncNotMemorizedFunc,
+                           MemorizedResult, Memory, NotMemorizedResult)
+from joblib.test.common import np, with_numpy
+from joblib.testing import raises
+
+from .test_memory import (corrupt_single_cache_item,
+                          monkeypatch_cached_func_warn)
+
+
+async def check_identity_lazy_async(func, accumulator, location):
+    """ Similar to check_identity_lazy_async for coroutine functions"""
+    memory = Memory(location=location, verbose=0)
+    func = memory.cache(func)
+    for i in range(3):
+        for _ in range(2):
+            value = await func(i)
+            assert value == i
+            assert len(accumulator) == i + 1
+
+
+@pytest.mark.asyncio
+async def test_memory_integration_async(tmpdir):
+    accumulator = list()
+
+    async def f(n):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return n
+
+    await check_identity_lazy_async(f, accumulator, tmpdir.strpath)
+
+    # Now test clearing
+    for compress in (False, True):
+        for mmap_mode in ('r', None):
+            memory = Memory(location=tmpdir.strpath, verbose=10,
+                            mmap_mode=mmap_mode, compress=compress)
+            # First clear the cache directory, to check that our code can
+            # handle that
+            # NOTE: this line would raise an exception, as the database
+            # file is still open; we ignore the error since we want to
+            # test what happens if the directory disappears
+            shutil.rmtree(tmpdir.strpath, ignore_errors=True)
+            g = memory.cache(f)
+            await g(1)
+            g.clear(warn=False)
+            current_accumulator = len(accumulator)
+            out = await g(1)
+
+        assert len(accumulator) == current_accumulator + 1
+        # Also, check that Memory.eval works similarly
+        evaled = await memory.eval(f, 1)
+        assert evaled == out
+        assert len(accumulator) == current_accumulator + 1
+
+    # Now do a smoke test with a function defined in __main__, as the name
+    # mangling rules are more complex
+    f.__module__ = '__main__'
+    memory = Memory(location=tmpdir.strpath, verbose=0)
+    await memory.cache(f)(1)
+
+
+@pytest.mark.asyncio
+async def test_no_memory_async():
+    accumulator = list()
+
+    async def ff(x):
+        await asyncio.sleep(0.1)
+        accumulator.append(1)
+        return x
+
+    memory = Memory(location=None, verbose=0)
+    gg = memory.cache(ff)
+    for _ in range(4):
+        current_accumulator = len(accumulator)
+        await gg(1)
+        assert len(accumulator) == current_accumulator + 1
+
+
+@with_numpy
+@pytest.mark.asyncio
+async def test_memory_numpy_check_mmap_mode_async(tmpdir, monkeypatch):
+    """Check that mmap_mode is respected even at the first call"""
+
+    memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
+
+    @memory.cache()
+    async def twice(a):
+        return a * 2
+
+    a = np.ones(3)
+    b = await twice(a)
+    c = await twice(a)
+
+    assert isinstance(c, np.memmap)
+    assert c.mode == 'r'
+
+    assert isinstance(b, np.memmap)
+    assert b.mode == 'r'
+
+    # Corrupts the file,  Deleting b and c mmaps
+    # is necessary to be able edit the file
+    del b
+    del c
+    gc.collect()
+    corrupt_single_cache_item(memory)
+
+    # Make sure that corrupting the file causes recomputation and that
+    # a warning is issued.
+    recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
+    d = await twice(a)
+    assert len(recorded_warnings) == 1
+    exception_msg = 'Exception while loading results'
+    assert exception_msg in recorded_warnings[0]
+    # Asserts that the recomputation returns a mmap
+    assert isinstance(d, np.memmap)
+    assert d.mode == 'r'
+
+
+@pytest.mark.asyncio
+async def test_call_and_shelve_async(tmpdir):
+    async def f(x, y=1):
+        await asyncio.sleep(0.1)
+        return x ** 2 + y
+
+    # Test MemorizedFunc outputting a reference to cache.
+    for func, Result in zip((AsyncMemorizedFunc(f, tmpdir.strpath),
+                             AsyncNotMemorizedFunc(f),
+                             Memory(location=tmpdir.strpath,
+                                    verbose=0).cache(f),
+                             Memory(location=None).cache(f),
+                             ),
+                            (MemorizedResult, NotMemorizedResult,
+                             MemorizedResult, NotMemorizedResult,
+                             )):
+        for _ in range(2):
+            result = await func.call_and_shelve(2)
+            assert isinstance(result, Result)
+            assert result.get() == 5
+
+        result.clear()
+        with raises(KeyError):
+            result.get()
+        result.clear()  # Do nothing if there is no cache.
+
+
+@pytest.mark.asyncio
+async def test_memorized_func_call_async(memory):
+
+    async def ff(x, counter):
+        await asyncio.sleep(0.1)
+        counter[x] = counter.get(x, 0) + 1
+        return counter[x]
+
+    gg = memory.cache(ff, ignore=['counter'])
+
+    counter = {}
+    assert await gg(2, counter) == 1
+    assert await gg(2, counter) == 1
+
+    x, meta = await gg.call(2, counter)
+    assert x == 2, "f has not been called properly"
+    assert isinstance(meta, dict), (
+        "Metadata are not returned by MemorizedFunc.call."
+    )

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_missing_multiprocessing.py

@@ -0,0 +1,32 @@
+"""
+Pyodide and other single-threaded Python builds will be missing the
+_multiprocessing module. Test that joblib still works in this environment.
+"""
+
+import os
+import subprocess
+import sys
+
+
+def test_missing_multiprocessing(tmp_path):
+    """
+    Test that import joblib works even if _multiprocessing is missing.
+
+    pytest has already imported everything from joblib. The most reasonable way
+    to test importing joblib with modified environment is to invoke a separate
+    Python process. This also ensures that we don't break other tests by
+    importing a bad `_multiprocessing` module.
+    """
+    (tmp_path / "_multiprocessing.py").write_text(
+        'raise ImportError("No _multiprocessing module!")'
+    )
+    env = dict(os.environ)
+    # For subprocess, use current sys.path with our custom version of
+    # multiprocessing inserted.
+    env["PYTHONPATH"] = ":".join([str(tmp_path)] + sys.path)
+    subprocess.check_call(
+        [sys.executable, "-c",
+         "import joblib, math; "
+         "joblib.Parallel(n_jobs=1)("
+         "joblib.delayed(math.sqrt)(i**2) for i in range(10))"
+         ], env=env)

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_module.py

@@ -0,0 +1,53 @@
+import sys
+import joblib
+from joblib.testing import check_subprocess_call
+from joblib.test.common import with_multiprocessing
+
+
+def test_version():
+    assert hasattr(joblib, '__version__'), (
+        "There are no __version__ argument on the joblib module")
+
+
+@with_multiprocessing
+def test_no_start_method_side_effect_on_import():
+    # check that importing joblib does not implicitly set the global
+    # start_method for multiprocessing.
+    code = """if True:
+        import joblib
+        import multiprocessing as mp
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        mp.set_start_method("loky")
+    """
+    check_subprocess_call([sys.executable, '-c', code])
+
+
+@with_multiprocessing
+def test_no_semaphore_tracker_on_import():
+    # check that importing joblib does not implicitly spawn a resource tracker
+    # or a semaphore tracker
+    code = """if True:
+        import joblib
+        from multiprocessing import semaphore_tracker
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        msg = "multiprocessing.semaphore_tracker has been spawned on import"
+        assert semaphore_tracker._semaphore_tracker._fd is None, msg"""
+    if sys.version_info >= (3, 8):
+        # semaphore_tracker was renamed in Python 3.8:
+        code = code.replace("semaphore_tracker", "resource_tracker")
+    check_subprocess_call([sys.executable, '-c', code])
+
+
+@with_multiprocessing
+def test_no_resource_tracker_on_import():
+    code = """if True:
+        import joblib
+        from joblib.externals.loky.backend import resource_tracker
+        # The following line would raise RuntimeError if the
+        # start_method is already set.
+        msg = "loky.resource_tracker has been spawned on import"
+        assert resource_tracker._resource_tracker._fd is None, msg
+    """
+    check_subprocess_call([sys.executable, '-c', code])

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_numpy_pickle.py

@@ -0,0 +1,1159 @@
+"""Test the numpy pickler as a replacement of the standard pickler."""
+
+import copy
+import os
+import random
+import re
+import io
+import sys
+import warnings
+import gzip
+import zlib
+import bz2
+import pickle
+import socket
+from contextlib import closing
+import mmap
+from pathlib import Path
+
+try:
+    import lzma
+except ImportError:
+    lzma = None
+
+import pytest
+
+from joblib.test.common import np, with_numpy, with_lz4, without_lz4
+from joblib.test.common import with_memory_profiler, memory_used
+from joblib.testing import parametrize, raises, warns
+
+# numpy_pickle is not a drop-in replacement of pickle, as it takes
+# filenames instead of open files as arguments.
+from joblib import numpy_pickle, register_compressor
+from joblib.test import data
+
+from joblib.numpy_pickle_utils import _IO_BUFFER_SIZE
+from joblib.numpy_pickle_utils import _detect_compressor
+from joblib.numpy_pickle_utils import _is_numpy_array_byte_order_mismatch
+from joblib.numpy_pickle_utils import _ensure_native_byte_order
+from joblib.compressor import (_COMPRESSORS, _LZ4_PREFIX, CompressorWrapper,
+                               LZ4_NOT_INSTALLED_ERROR, BinaryZlibFile)
+
+
+###############################################################################
+# Define a list of standard types.
+# Borrowed from dill, initial author: Micheal McKerns:
+# http://dev.danse.us/trac/pathos/browser/dill/dill_test2.py
+
+typelist = []
+
+# testing types
+_none = None
+typelist.append(_none)
+_type = type
+typelist.append(_type)
+_bool = bool(1)
+typelist.append(_bool)
+_int = int(1)
+typelist.append(_int)
+_float = float(1)
+typelist.append(_float)
+_complex = complex(1)
+typelist.append(_complex)
+_string = str(1)
+typelist.append(_string)
+_tuple = ()
+typelist.append(_tuple)
+_list = []
+typelist.append(_list)
+_dict = {}
+typelist.append(_dict)
+_builtin = len
+typelist.append(_builtin)
+
+
+def _function(x):
+    yield x
+
+
+class _class:
+    def _method(self):
+        pass
+
+
+class _newclass(object):
+    def _method(self):
+        pass
+
+
+typelist.append(_function)
+typelist.append(_class)
+typelist.append(_newclass)  # <type 'type'>
+_instance = _class()
+typelist.append(_instance)
+_object = _newclass()
+typelist.append(_object)  # <type 'class'>
+
+
+###############################################################################
+# Tests
+
+@parametrize('compress', [0, 1])
+@parametrize('member', typelist)
+def test_standard_types(tmpdir, compress, member):
+    # Test pickling and saving with standard types.
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump(member, filename, compress=compress)
+    _member = numpy_pickle.load(filename)
+    # We compare the pickled instance to the reloaded one only if it
+    # can be compared to a copied one
+    if member == copy.deepcopy(member):
+        assert member == _member
+
+
+def test_value_error():
+    # Test inverting the input arguments to dump
+    with raises(ValueError):
+        numpy_pickle.dump('foo', dict())
+
+
+@parametrize('wrong_compress', [-1, 10, dict()])
+def test_compress_level_error(wrong_compress):
+    # Verify that passing an invalid compress argument raises an error.
+    exception_msg = ('Non valid compress level given: '
+                     '"{0}"'.format(wrong_compress))
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump('dummy', 'foo', compress=wrong_compress)
+    excinfo.match(exception_msg)
+
+
+@with_numpy
+@parametrize('compress', [False, True, 0, 3, 'zlib'])
+def test_numpy_persistence(tmpdir, compress):
+    filename = tmpdir.join('test.pkl').strpath
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample((10, 2))
+    # We use 'a.T' to have a non C-contiguous array.
+    for index, obj in enumerate(((a,), (a.T,), (a, a), [a, a, a])):
+        filenames = numpy_pickle.dump(obj, filename, compress=compress)
+
+        # All is cached in one file
+        assert len(filenames) == 1
+        # Check that only one file was created
+        assert filenames[0] == filename
+        # Check that this file does exist
+        assert os.path.exists(filenames[0])
+
+        # Unpickle the object
+        obj_ = numpy_pickle.load(filename)
+        # Check that the items are indeed arrays
+        for item in obj_:
+            assert isinstance(item, np.ndarray)
+        # And finally, check that all the values are equal.
+        np.testing.assert_array_equal(np.array(obj), np.array(obj_))
+
+    # Now test with an array subclass
+    obj = np.memmap(filename + 'mmap', mode='w+', shape=4, dtype=np.float64)
+    filenames = numpy_pickle.dump(obj, filename, compress=compress)
+    # All is cached in one file
+    assert len(filenames) == 1
+
+    obj_ = numpy_pickle.load(filename)
+    if (type(obj) is not np.memmap and
+            hasattr(obj, '__array_prepare__')):
+        # We don't reconstruct memmaps
+        assert isinstance(obj_, type(obj))
+
+    np.testing.assert_array_equal(obj_, obj)
+
+    # Test with an object containing multiple numpy arrays
+    obj = ComplexTestObject()
+    filenames = numpy_pickle.dump(obj, filename, compress=compress)
+    # All is cached in one file
+    assert len(filenames) == 1
+
+    obj_loaded = numpy_pickle.load(filename)
+    assert isinstance(obj_loaded, type(obj))
+    np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
+    np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
+    np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)
+
+
+@with_numpy
+def test_numpy_persistence_bufferred_array_compression(tmpdir):
+    big_array = np.ones((_IO_BUFFER_SIZE + 100), dtype=np.uint8)
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump(big_array, filename, compress=True)
+    arr_reloaded = numpy_pickle.load(filename)
+
+    np.testing.assert_array_equal(big_array, arr_reloaded)
+
+
+@with_numpy
+def test_memmap_persistence(tmpdir):
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    filename = tmpdir.join('test1.pkl').strpath
+    numpy_pickle.dump(a, filename)
+    b = numpy_pickle.load(filename, mmap_mode='r')
+
+    assert isinstance(b, np.memmap)
+
+    # Test with an object containing multiple numpy arrays
+    filename = tmpdir.join('test2.pkl').strpath
+    obj = ComplexTestObject()
+    numpy_pickle.dump(obj, filename)
+    obj_loaded = numpy_pickle.load(filename, mmap_mode='r')
+    assert isinstance(obj_loaded, type(obj))
+    assert isinstance(obj_loaded.array_float, np.memmap)
+    assert not obj_loaded.array_float.flags.writeable
+    assert isinstance(obj_loaded.array_int, np.memmap)
+    assert not obj_loaded.array_int.flags.writeable
+    # Memory map not allowed for numpy object arrays
+    assert not isinstance(obj_loaded.array_obj, np.memmap)
+    np.testing.assert_array_equal(obj_loaded.array_float,
+                                  obj.array_float)
+    np.testing.assert_array_equal(obj_loaded.array_int,
+                                  obj.array_int)
+    np.testing.assert_array_equal(obj_loaded.array_obj,
+                                  obj.array_obj)
+
+    # Test we can write in memmapped arrays
+    obj_loaded = numpy_pickle.load(filename, mmap_mode='r+')
+    assert obj_loaded.array_float.flags.writeable
+    obj_loaded.array_float[0:10] = 10.0
+    assert obj_loaded.array_int.flags.writeable
+    obj_loaded.array_int[0:10] = 10
+
+    obj_reloaded = numpy_pickle.load(filename, mmap_mode='r')
+    np.testing.assert_array_equal(obj_reloaded.array_float,
+                                  obj_loaded.array_float)
+    np.testing.assert_array_equal(obj_reloaded.array_int,
+                                  obj_loaded.array_int)
+
+    # Test w+ mode is caught and the mode has switched to r+
+    numpy_pickle.load(filename, mmap_mode='w+')
+    assert obj_loaded.array_int.flags.writeable
+    assert obj_loaded.array_int.mode == 'r+'
+    assert obj_loaded.array_float.flags.writeable
+    assert obj_loaded.array_float.mode == 'r+'
+
+
+@with_numpy
+def test_memmap_persistence_mixed_dtypes(tmpdir):
+    # loading datastructures that have sub-arrays with dtype=object
+    # should not prevent memmapping on fixed size dtype sub-arrays.
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    b = np.array([1, 'b'], dtype=object)
+    construct = (a, b)
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump(construct, filename)
+    a_clone, b_clone = numpy_pickle.load(filename, mmap_mode='r')
+
+    # the floating point array has been memory mapped
+    assert isinstance(a_clone, np.memmap)
+
+    # the object-dtype array has been loaded in memory
+    assert not isinstance(b_clone, np.memmap)
+
+
+@with_numpy
+def test_masked_array_persistence(tmpdir):
+    # The special-case picker fails, because saving masked_array
+    # not implemented, but it just delegates to the standard pickler.
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    a = np.ma.masked_greater(a, 0.5)
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump(a, filename)
+    b = numpy_pickle.load(filename, mmap_mode='r')
+    assert isinstance(b, np.ma.masked_array)
+
+
+@with_numpy
+def test_compress_mmap_mode_warning(tmpdir):
+    # Test the warning in case of compress + mmap_mode
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample(10)
+    this_filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump(a, this_filename, compress=1)
+    with warns(UserWarning) as warninfo:
+        numpy_pickle.load(this_filename, mmap_mode='r+')
+    debug_msg = "\n".join([str(w) for w in warninfo])
+    warninfo = [w.message for w in warninfo]
+    assert len(warninfo) == 1, debug_msg
+    assert (
+        str(warninfo[0]) ==
+        'mmap_mode "r+" is not compatible with compressed '
+        f'file {this_filename}. "r+" flag will be ignored.'
+    )
+
+
+@with_numpy
+@parametrize('cache_size', [None, 0, 10])
+def test_cache_size_warning(tmpdir, cache_size):
+    # Check deprecation warning raised when cache size is not None
+    filename = tmpdir.join('test.pkl').strpath
+    rnd = np.random.RandomState(0)
+    a = rnd.random_sample((10, 2))
+
+    warnings.simplefilter("always")
+    with warnings.catch_warnings(record=True) as warninfo:
+        numpy_pickle.dump(a, filename, cache_size=cache_size)
+    expected_nb_warnings = 1 if cache_size is not None else 0
+    assert len(warninfo) == expected_nb_warnings
+    for w in warninfo:
+        assert w.category == DeprecationWarning
+        assert (str(w.message) ==
+                "Please do not set 'cache_size' in joblib.dump, this "
+                "parameter has no effect and will be removed. You "
+                "used 'cache_size={0}'".format(cache_size))
+
+
+@with_numpy
+@with_memory_profiler
+@parametrize('compress', [True, False])
+def test_memory_usage(tmpdir, compress):
+    # Verify memory stays within expected bounds.
+    filename = tmpdir.join('test.pkl').strpath
+    small_array = np.ones((10, 10))
+    big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
+
+    for obj in (small_array, big_array):
+        size = obj.nbytes / 1e6
+        obj_filename = filename + str(np.random.randint(0, 1000))
+        mem_used = memory_used(numpy_pickle.dump,
+                               obj, obj_filename, compress=compress)
+
+        # The memory used to dump the object shouldn't exceed the buffer
+        # size used to write array chunks (16MB).
+        write_buf_size = _IO_BUFFER_SIZE + 16 * 1024 ** 2 / 1e6
+        assert mem_used <= write_buf_size
+
+        mem_used = memory_used(numpy_pickle.load, obj_filename)
+        # memory used should be less than array size + buffer size used to
+        # read the array chunk by chunk.
+        read_buf_size = 32 + _IO_BUFFER_SIZE  # MiB
+        assert mem_used < size + read_buf_size
+
+
+@with_numpy
+def test_compressed_pickle_dump_and_load(tmpdir):
+    expected_list = [np.arange(5, dtype=np.dtype('<i8')),
+                     np.arange(5, dtype=np.dtype('>i8')),
+                     np.arange(5, dtype=np.dtype('<f8')),
+                     np.arange(5, dtype=np.dtype('>f8')),
+                     np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
+                     np.arange(256, dtype=np.uint8).tobytes(),
+                     u"C'est l'\xe9t\xe9 !"]
+
+    fname = tmpdir.join('temp.pkl.gz').strpath
+
+    dumped_filenames = numpy_pickle.dump(expected_list, fname, compress=1)
+    assert len(dumped_filenames) == 1
+    result_list = numpy_pickle.load(fname)
+    for result, expected in zip(result_list, expected_list):
+        if isinstance(expected, np.ndarray):
+            expected = _ensure_native_byte_order(expected)
+            assert result.dtype == expected.dtype
+            np.testing.assert_equal(result, expected)
+        else:
+            assert result == expected
+
+
+def _check_pickle(filename, expected_list, mmap_mode=None):
+    """Helper function to test joblib pickle content.
+
+    Note: currently only pickles containing an iterable are supported
+    by this function.
+    """
+    version_match = re.match(r'.+py(\d)(\d).+', filename)
+    py_version_used_for_writing = int(version_match.group(1))
+
+    py_version_to_default_pickle_protocol = {2: 2, 3: 3}
+    pickle_reading_protocol = py_version_to_default_pickle_protocol.get(3, 4)
+    pickle_writing_protocol = py_version_to_default_pickle_protocol.get(
+        py_version_used_for_writing, 4)
+    if pickle_reading_protocol >= pickle_writing_protocol:
+        try:
+            with warnings.catch_warnings(record=True) as warninfo:
+                warnings.simplefilter('always')
+                warnings.filterwarnings(
+                    'ignore', module='numpy',
+                    message='The compiler package is deprecated')
+                result_list = numpy_pickle.load(filename, mmap_mode=mmap_mode)
+            filename_base = os.path.basename(filename)
+            expected_nb_deprecation_warnings = 1 if (
+                "_0.9" in filename_base or "_0.8.4" in filename_base) else 0
+
+            expected_nb_user_warnings = 3 if (
+                re.search("_0.1.+.pkl$", filename_base) and
+                mmap_mode is not None) else 0
+            expected_nb_warnings = \
+                expected_nb_deprecation_warnings + expected_nb_user_warnings
+            assert len(warninfo) == expected_nb_warnings
+
+            deprecation_warnings = [
+                w for w in warninfo if issubclass(
+                    w.category, DeprecationWarning)]
+            user_warnings = [
+                w for w in warninfo if issubclass(
+                    w.category, UserWarning)]
+            for w in deprecation_warnings:
+                assert (str(w.message) ==
+                        "The file '{0}' has been generated with a joblib "
+                        "version less than 0.10. Please regenerate this "
+                        "pickle file.".format(filename))
+
+            for w in user_warnings:
+                escaped_filename = re.escape(filename)
+                assert re.search(
+                    f"memmapped.+{escaped_filename}.+segmentation fault",
+                    str(w.message))
+
+            for result, expected in zip(result_list, expected_list):
+                if isinstance(expected, np.ndarray):
+                    expected = _ensure_native_byte_order(expected)
+                    assert result.dtype == expected.dtype
+                    np.testing.assert_equal(result, expected)
+                else:
+                    assert result == expected
+        except Exception as exc:
+            # When trying to read with python 3 a pickle generated
+            # with python 2 we expect a user-friendly error
+            if py_version_used_for_writing == 2:
+                assert isinstance(exc, ValueError)
+                message = ('You may be trying to read with '
+                           'python 3 a joblib pickle generated with python 2.')
+                assert message in str(exc)
+            elif filename.endswith('.lz4') and with_lz4.args[0]:
+                assert isinstance(exc, ValueError)
+                assert LZ4_NOT_INSTALLED_ERROR in str(exc)
+            else:
+                raise
+    else:
+        # Pickle protocol used for writing is too high. We expect a
+        # "unsupported pickle protocol" error message
+        try:
+            numpy_pickle.load(filename)
+            raise AssertionError('Numpy pickle loading should '
+                                 'have raised a ValueError exception')
+        except ValueError as e:
+            message = 'unsupported pickle protocol: {0}'.format(
+                pickle_writing_protocol)
+            assert message in str(e.args)
+
+
+@with_numpy
+def test_joblib_pickle_across_python_versions():
+    # We need to be specific about dtypes in particular endianness
+    # because the pickles can be generated on one architecture and
+    # the tests run on another one. See
+    # https://github.com/joblib/joblib/issues/279.
+    expected_list = [np.arange(5, dtype=np.dtype('<i8')),
+                     np.arange(5, dtype=np.dtype('<f8')),
+                     np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
+                     np.arange(256, dtype=np.uint8).tobytes(),
+                     # np.matrix is a subclass of np.ndarray, here we want
+                     # to verify this type of object is correctly unpickled
+                     # among versions.
+                     np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
+                     u"C'est l'\xe9t\xe9 !"]
+
+    # Testing all the compressed and non compressed
+    # pickles in joblib/test/data. These pickles were generated by
+    # the joblib/test/data/create_numpy_pickle.py script for the
+    # relevant python, joblib and numpy versions.
+    test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
+
+    pickle_extensions = ('.pkl', '.gz', '.gzip', '.bz2', 'lz4')
+    if lzma is not None:
+        pickle_extensions += ('.xz', '.lzma')
+    pickle_filenames = [os.path.join(test_data_dir, fn)
+                        for fn in os.listdir(test_data_dir)
+                        if any(fn.endswith(ext) for ext in pickle_extensions)]
+
+    for fname in pickle_filenames:
+        _check_pickle(fname, expected_list)
+
+
+@with_numpy
+def test_joblib_pickle_across_python_versions_with_mmap():
+    expected_list = [np.arange(5, dtype=np.dtype('<i8')),
+                     np.arange(5, dtype=np.dtype('<f8')),
+                     np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
+                     np.arange(256, dtype=np.uint8).tobytes(),
+                     # np.matrix is a subclass of np.ndarray, here we want
+                     # to verify this type of object is correctly unpickled
+                     # among versions.
+                     np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
+                     u"C'est l'\xe9t\xe9 !"]
+
+    test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
+
+    pickle_filenames = [
+        os.path.join(test_data_dir, fn)
+        for fn in os.listdir(test_data_dir) if fn.endswith('.pkl')]
+    for fname in pickle_filenames:
+        _check_pickle(fname, expected_list, mmap_mode='r')
+
+
+@with_numpy
+def test_numpy_array_byte_order_mismatch_detection():
+    # List of numpy arrays with big endian byteorder.
+    be_arrays = [np.array([(1, 2.0), (3, 4.0)],
+                          dtype=[('', '>i8'), ('', '>f8')]),
+                 np.arange(3, dtype=np.dtype('>i8')),
+                 np.arange(3, dtype=np.dtype('>f8'))]
+
+    # Verify the byteorder mismatch is correctly detected.
+    for array in be_arrays:
+        if sys.byteorder == 'big':
+            assert not _is_numpy_array_byte_order_mismatch(array)
+        else:
+            assert _is_numpy_array_byte_order_mismatch(array)
+        converted = _ensure_native_byte_order(array)
+        if converted.dtype.fields:
+            for f in converted.dtype.fields.values():
+                f[0].byteorder == '='
+        else:
+            assert converted.dtype.byteorder == "="
+
+    # List of numpy arrays with little endian byteorder.
+    le_arrays = [np.array([(1, 2.0), (3, 4.0)],
+                          dtype=[('', '<i8'), ('', '<f8')]),
+                 np.arange(3, dtype=np.dtype('<i8')),
+                 np.arange(3, dtype=np.dtype('<f8'))]
+
+    # Verify the byteorder mismatch is correctly detected.
+    for array in le_arrays:
+        if sys.byteorder == 'little':
+            assert not _is_numpy_array_byte_order_mismatch(array)
+        else:
+            assert _is_numpy_array_byte_order_mismatch(array)
+        converted = _ensure_native_byte_order(array)
+        if converted.dtype.fields:
+            for f in converted.dtype.fields.values():
+                f[0].byteorder == '='
+        else:
+            assert converted.dtype.byteorder == "="
+
+
+@parametrize('compress_tuple', [('zlib', 3), ('gzip', 3)])
+def test_compress_tuple_argument(tmpdir, compress_tuple):
+    # Verify the tuple is correctly taken into account.
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump("dummy", filename,
+                      compress=compress_tuple)
+    # Verify the file contains the right magic number
+    with open(filename, 'rb') as f:
+        assert _detect_compressor(f) == compress_tuple[0]
+
+
+@parametrize('compress_tuple,message',
+             [(('zlib', 3, 'extra'),        # wrong compress tuple
+               'Compress argument tuple should contain exactly 2 elements'),
+              (('wrong', 3),                # wrong compress method
+               'Non valid compression method given: "{}"'.format('wrong')),
+              (('zlib', 'wrong'),           # wrong compress level
+               'Non valid compress level given: "{}"'.format('wrong'))])
+def test_compress_tuple_argument_exception(tmpdir, compress_tuple, message):
+    filename = tmpdir.join('test.pkl').strpath
+    # Verify setting a wrong compress tuple raises a ValueError.
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump('dummy', filename, compress=compress_tuple)
+    excinfo.match(message)
+
+
+@parametrize('compress_string', ['zlib', 'gzip'])
+def test_compress_string_argument(tmpdir, compress_string):
+    # Verify the string is correctly taken into account.
+    filename = tmpdir.join('test.pkl').strpath
+    numpy_pickle.dump("dummy", filename,
+                      compress=compress_string)
+    # Verify the file contains the right magic number
+    with open(filename, 'rb') as f:
+        assert _detect_compressor(f) == compress_string
+
+
+@with_numpy
+@parametrize('compress', [1, 3, 6])
+@parametrize('cmethod', _COMPRESSORS)
+def test_joblib_compression_formats(tmpdir, compress, cmethod):
+    filename = tmpdir.join('test.pkl').strpath
+    objects = (np.ones(shape=(100, 100), dtype='f8'),
+               range(10),
+               {'a': 1, 2: 'b'}, [], (), {}, 0, 1.0)
+
+    if cmethod in ("lzma", "xz") and lzma is None:
+        pytest.skip("lzma is support not available")
+
+    elif cmethod == 'lz4' and with_lz4.args[0]:
+        # Skip the test if lz4 is not installed. We here use the with_lz4
+        # skipif fixture whose argument is True when lz4 is not installed
+        pytest.skip("lz4 is not installed.")
+
+    dump_filename = filename + "." + cmethod
+    for obj in objects:
+        numpy_pickle.dump(obj, dump_filename, compress=(cmethod, compress))
+        # Verify the file contains the right magic number
+        with open(dump_filename, 'rb') as f:
+            assert _detect_compressor(f) == cmethod
+        # Verify the reloaded object is correct
+        obj_reloaded = numpy_pickle.load(dump_filename)
+        assert isinstance(obj_reloaded, type(obj))
+        if isinstance(obj, np.ndarray):
+            np.testing.assert_array_equal(obj_reloaded, obj)
+        else:
+            assert obj_reloaded == obj
+
+
+def _gzip_file_decompress(source_filename, target_filename):
+    """Decompress a gzip file."""
+    with closing(gzip.GzipFile(source_filename, "rb")) as fo:
+        buf = fo.read()
+
+    with open(target_filename, "wb") as fo:
+        fo.write(buf)
+
+
+def _zlib_file_decompress(source_filename, target_filename):
+    """Decompress a zlib file."""
+    with open(source_filename, 'rb') as fo:
+        buf = zlib.decompress(fo.read())
+
+    with open(target_filename, 'wb') as fo:
+        fo.write(buf)
+
+
+@parametrize('extension,decompress',
+             [('.z', _zlib_file_decompress),
+              ('.gz', _gzip_file_decompress)])
+def test_load_externally_decompressed_files(tmpdir, extension, decompress):
+    # Test that BinaryZlibFile generates valid gzip and zlib compressed files.
+    obj = "a string to persist"
+    filename_raw = tmpdir.join('test.pkl').strpath
+
+    filename_compressed = filename_raw + extension
+    # Use automatic extension detection to compress with the right method.
+    numpy_pickle.dump(obj, filename_compressed)
+
+    # Decompress with the corresponding method
+    decompress(filename_compressed, filename_raw)
+
+    # Test that the uncompressed pickle can be loaded and
+    # that the result is correct.
+    obj_reloaded = numpy_pickle.load(filename_raw)
+    assert obj == obj_reloaded
+
+
+@parametrize('extension,cmethod',
+             # valid compressor extensions
+             [('.z', 'zlib'),
+              ('.gz', 'gzip'),
+              ('.bz2', 'bz2'),
+              ('.lzma', 'lzma'),
+              ('.xz', 'xz'),
+              # invalid compressor extensions
+              ('.pkl', 'not-compressed'),
+              ('', 'not-compressed')])
+def test_compression_using_file_extension(tmpdir, extension, cmethod):
+    if cmethod in ("lzma", "xz") and lzma is None:
+        pytest.skip("lzma is missing")
+    # test that compression method corresponds to the given filename extension.
+    filename = tmpdir.join('test.pkl').strpath
+    obj = "object to dump"
+
+    dump_fname = filename + extension
+    numpy_pickle.dump(obj, dump_fname)
+    # Verify the file contains the right magic number
+    with open(dump_fname, 'rb') as f:
+        assert _detect_compressor(f) == cmethod
+    # Verify the reloaded object is correct
+    obj_reloaded = numpy_pickle.load(dump_fname)
+    assert isinstance(obj_reloaded, type(obj))
+    assert obj_reloaded == obj
+
+
+@with_numpy
+def test_file_handle_persistence(tmpdir):
+    objs = [np.random.random((10, 10)), "some data"]
+    fobjs = [bz2.BZ2File, gzip.GzipFile]
+    if lzma is not None:
+        fobjs += [lzma.LZMAFile]
+    filename = tmpdir.join('test.pkl').strpath
+
+    for obj in objs:
+        for fobj in fobjs:
+            with fobj(filename, 'wb') as f:
+                numpy_pickle.dump(obj, f)
+
+            # using the same decompressor prevents from internally
+            # decompress again.
+            with fobj(filename, 'rb') as f:
+                obj_reloaded = numpy_pickle.load(f)
+
+            # when needed, the correct decompressor should be used when
+            # passing a raw file handle.
+            with open(filename, 'rb') as f:
+                obj_reloaded_2 = numpy_pickle.load(f)
+
+            if isinstance(obj, np.ndarray):
+                np.testing.assert_array_equal(obj_reloaded, obj)
+                np.testing.assert_array_equal(obj_reloaded_2, obj)
+            else:
+                assert obj_reloaded == obj
+                assert obj_reloaded_2 == obj
+
+
+@with_numpy
+def test_in_memory_persistence():
+    objs = [np.random.random((10, 10)), "some data"]
+    for obj in objs:
+        f = io.BytesIO()
+        numpy_pickle.dump(obj, f)
+        obj_reloaded = numpy_pickle.load(f)
+        if isinstance(obj, np.ndarray):
+            np.testing.assert_array_equal(obj_reloaded, obj)
+        else:
+            assert obj_reloaded == obj
+
+
+@with_numpy
+def test_file_handle_persistence_mmap(tmpdir):
+    obj = np.random.random((10, 10))
+    filename = tmpdir.join('test.pkl').strpath
+
+    with open(filename, 'wb') as f:
+        numpy_pickle.dump(obj, f)
+
+    with open(filename, 'rb') as f:
+        obj_reloaded = numpy_pickle.load(f, mmap_mode='r+')
+
+    np.testing.assert_array_equal(obj_reloaded, obj)
+
+
+@with_numpy
+def test_file_handle_persistence_compressed_mmap(tmpdir):
+    obj = np.random.random((10, 10))
+    filename = tmpdir.join('test.pkl').strpath
+
+    with open(filename, 'wb') as f:
+        numpy_pickle.dump(obj, f, compress=('gzip', 3))
+
+    with closing(gzip.GzipFile(filename, 'rb')) as f:
+        with warns(UserWarning) as warninfo:
+            numpy_pickle.load(f, mmap_mode='r+')
+        assert len(warninfo) == 1
+        assert (str(warninfo[0].message) ==
+                '"%(fileobj)r" is not a raw file, mmap_mode "%(mmap_mode)s" '
+                'flag will be ignored.' % {'fileobj': f, 'mmap_mode': 'r+'})
+
+
+@with_numpy
+def test_file_handle_persistence_in_memory_mmap():
+    obj = np.random.random((10, 10))
+    buf = io.BytesIO()
+
+    numpy_pickle.dump(obj, buf)
+
+    with warns(UserWarning) as warninfo:
+        numpy_pickle.load(buf, mmap_mode='r+')
+    assert len(warninfo) == 1
+    assert (str(warninfo[0].message) ==
+            'In memory persistence is not compatible with mmap_mode '
+            '"%(mmap_mode)s" flag passed. mmap_mode option will be '
+            'ignored.' % {'mmap_mode': 'r+'})
+
+
+@parametrize('data', [b'a little data as bytes.',
+                      # More bytes
+                      10000 * "{}".format(
+                          random.randint(0, 1000) * 1000).encode('latin-1')],
+             ids=["a little data as bytes.", "a large data as bytes."])
+@parametrize('compress_level', [1, 3, 9])
+def test_binary_zlibfile(tmpdir, data, compress_level):
+    filename = tmpdir.join('test.pkl').strpath
+    # Regular cases
+    with open(filename, 'wb') as f:
+        with BinaryZlibFile(f, 'wb',
+                            compresslevel=compress_level) as fz:
+            assert fz.writable()
+            fz.write(data)
+            assert fz.fileno() == f.fileno()
+            with raises(io.UnsupportedOperation):
+                fz._check_can_read()
+
+            with raises(io.UnsupportedOperation):
+                fz._check_can_seek()
+        assert fz.closed
+        with raises(ValueError):
+            fz._check_not_closed()
+
+    with open(filename, 'rb') as f:
+        with BinaryZlibFile(f) as fz:
+            assert fz.readable()
+            assert fz.seekable()
+            assert fz.fileno() == f.fileno()
+            assert fz.read() == data
+            with raises(io.UnsupportedOperation):
+                fz._check_can_write()
+            assert fz.seekable()
+            fz.seek(0)
+            assert fz.tell() == 0
+        assert fz.closed
+
+    # Test with a filename as input
+    with BinaryZlibFile(filename, 'wb',
+                        compresslevel=compress_level) as fz:
+        assert fz.writable()
+        fz.write(data)
+
+    with BinaryZlibFile(filename, 'rb') as fz:
+        assert fz.read() == data
+        assert fz.seekable()
+
+    # Test without context manager
+    fz = BinaryZlibFile(filename, 'wb', compresslevel=compress_level)
+    assert fz.writable()
+    fz.write(data)
+    fz.close()
+
+    fz = BinaryZlibFile(filename, 'rb')
+    assert fz.read() == data
+    fz.close()
+
+
+@parametrize('bad_value', [-1, 10, 15, 'a', (), {}])
+def test_binary_zlibfile_bad_compression_levels(tmpdir, bad_value):
+    filename = tmpdir.join('test.pkl').strpath
+    with raises(ValueError) as excinfo:
+        BinaryZlibFile(filename, 'wb', compresslevel=bad_value)
+    pattern = re.escape("'compresslevel' must be an integer between 1 and 9. "
+                        "You provided 'compresslevel={}'".format(bad_value))
+    excinfo.match(pattern)
+
+
+@parametrize('bad_mode', ['a', 'x', 'r', 'w', 1, 2])
+def test_binary_zlibfile_invalid_modes(tmpdir, bad_mode):
+    filename = tmpdir.join('test.pkl').strpath
+    with raises(ValueError) as excinfo:
+        BinaryZlibFile(filename, bad_mode)
+    excinfo.match("Invalid mode")
+
+
+@parametrize('bad_file', [1, (), {}])
+def test_binary_zlibfile_invalid_filename_type(bad_file):
+    with raises(TypeError) as excinfo:
+        BinaryZlibFile(bad_file, 'rb')
+    excinfo.match("filename must be a str or bytes object, or a file")
+
+
+###############################################################################
+# Test dumping array subclasses
+if np is not None:
+
+    class SubArray(np.ndarray):
+
+        def __reduce__(self):
+            return _load_sub_array, (np.asarray(self), )
+
+    def _load_sub_array(arr):
+        d = SubArray(arr.shape)
+        d[:] = arr
+        return d
+
+    class ComplexTestObject:
+        """A complex object containing numpy arrays as attributes."""
+
+        def __init__(self):
+            self.array_float = np.arange(100, dtype='float64')
+            self.array_int = np.ones(100, dtype='int32')
+            self.array_obj = np.array(['a', 10, 20.0], dtype='object')
+
+
+@with_numpy
+def test_numpy_subclass(tmpdir):
+    filename = tmpdir.join('test.pkl').strpath
+    a = SubArray((10,))
+    numpy_pickle.dump(a, filename)
+    c = numpy_pickle.load(filename)
+    assert isinstance(c, SubArray)
+    np.testing.assert_array_equal(c, a)
+
+
+def test_pathlib(tmpdir):
+    filename = tmpdir.join('test.pkl').strpath
+    value = 123
+    numpy_pickle.dump(value, Path(filename))
+    assert numpy_pickle.load(filename) == value
+    numpy_pickle.dump(value, filename)
+    assert numpy_pickle.load(Path(filename)) == value
+
+
+@with_numpy
+def test_non_contiguous_array_pickling(tmpdir):
+    filename = tmpdir.join('test.pkl').strpath
+
+    for array in [  # Array that triggers a contiguousness issue with nditer,
+                    # see https://github.com/joblib/joblib/pull/352 and see
+                    # https://github.com/joblib/joblib/pull/353
+                    np.asfortranarray([[1, 2], [3, 4]])[1:],
+                    # Non contiguous array with works fine with nditer
+                    np.ones((10, 50, 20), order='F')[:, :1, :]]:
+        assert not array.flags.c_contiguous
+        assert not array.flags.f_contiguous
+        numpy_pickle.dump(array, filename)
+        array_reloaded = numpy_pickle.load(filename)
+        np.testing.assert_array_equal(array_reloaded, array)
+
+
+@with_numpy
+def test_pickle_highest_protocol(tmpdir):
+    # ensure persistence of a numpy array is valid even when using
+    # the pickle HIGHEST_PROTOCOL.
+    # see https://github.com/joblib/joblib/issues/362
+
+    filename = tmpdir.join('test.pkl').strpath
+    test_array = np.zeros(10)
+
+    numpy_pickle.dump(test_array, filename, protocol=pickle.HIGHEST_PROTOCOL)
+    array_reloaded = numpy_pickle.load(filename)
+
+    np.testing.assert_array_equal(array_reloaded, test_array)
+
+
+@with_numpy
+def test_pickle_in_socket():
+    # test that joblib can pickle in sockets
+    test_array = np.arange(10)
+    _ADDR = ("localhost", 12345)
+    listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    listener.bind(_ADDR)
+    listener.listen(1)
+
+    with socket.create_connection(_ADDR) as client:
+        server, client_addr = listener.accept()
+
+        with server.makefile("wb") as sf:
+            numpy_pickle.dump(test_array, sf)
+
+        with client.makefile("rb") as cf:
+            array_reloaded = numpy_pickle.load(cf)
+
+        np.testing.assert_array_equal(array_reloaded, test_array)
+
+        # Check that a byte-aligned numpy array written in a file can be send
+        # over a socket and then read on the other side
+        bytes_to_send = io.BytesIO()
+        numpy_pickle.dump(test_array, bytes_to_send)
+        server.send(bytes_to_send.getvalue())
+
+        with client.makefile("rb") as cf:
+            array_reloaded = numpy_pickle.load(cf)
+
+        np.testing.assert_array_equal(array_reloaded, test_array)
+
+
+@with_numpy
+def test_load_memmap_with_big_offset(tmpdir):
+    # Test that numpy memmap offset is set correctly if greater than
+    # mmap.ALLOCATIONGRANULARITY, see
+    # https://github.com/joblib/joblib/issues/451 and
+    # https://github.com/numpy/numpy/pull/8443 for more details.
+    fname = tmpdir.join('test.mmap').strpath
+    size = mmap.ALLOCATIONGRANULARITY
+    obj = [np.zeros(size, dtype='uint8'), np.ones(size, dtype='uint8')]
+    numpy_pickle.dump(obj, fname)
+    memmaps = numpy_pickle.load(fname, mmap_mode='r')
+    assert isinstance(memmaps[1], np.memmap)
+    assert memmaps[1].offset > size
+    np.testing.assert_array_equal(obj, memmaps)
+
+
+def test_register_compressor(tmpdir):
+    # Check that registering compressor file works.
+    compressor_name = 'test-name'
+    compressor_prefix = 'test-prefix'
+
+    class BinaryCompressorTestFile(io.BufferedIOBase):
+        pass
+
+    class BinaryCompressorTestWrapper(CompressorWrapper):
+
+        def __init__(self):
+            CompressorWrapper.__init__(self, obj=BinaryCompressorTestFile,
+                                       prefix=compressor_prefix)
+
+    register_compressor(compressor_name, BinaryCompressorTestWrapper())
+
+    assert (_COMPRESSORS[compressor_name].fileobj_factory ==
+            BinaryCompressorTestFile)
+    assert _COMPRESSORS[compressor_name].prefix == compressor_prefix
+
+    # Remove this dummy compressor file from extra compressors because other
+    # tests might fail because of this
+    _COMPRESSORS.pop(compressor_name)
+
+
+@parametrize('invalid_name', [1, (), {}])
+def test_register_compressor_invalid_name(invalid_name):
+    # Test that registering an invalid compressor name is not allowed.
+    with raises(ValueError) as excinfo:
+        register_compressor(invalid_name, None)
+    excinfo.match("Compressor name should be a string")
+
+
+def test_register_compressor_invalid_fileobj():
+    # Test that registering an invalid file object is not allowed.
+
+    class InvalidFileObject():
+        pass
+
+    class InvalidFileObjectWrapper(CompressorWrapper):
+        def __init__(self):
+            CompressorWrapper.__init__(self, obj=InvalidFileObject,
+                                       prefix=b'prefix')
+
+    with raises(ValueError) as excinfo:
+        register_compressor('invalid', InvalidFileObjectWrapper())
+
+    excinfo.match("Compressor 'fileobj_factory' attribute should implement "
+                  "the file object interface")
+
+
+class AnotherZlibCompressorWrapper(CompressorWrapper):
+
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=BinaryZlibFile, prefix=b'prefix')
+
+
+class StandardLibGzipCompressorWrapper(CompressorWrapper):
+
+    def __init__(self):
+        CompressorWrapper.__init__(self, obj=gzip.GzipFile, prefix=b'prefix')
+
+
+def test_register_compressor_already_registered():
+    # Test registration of existing compressor files.
+    compressor_name = 'test-name'
+
+    # register a test compressor
+    register_compressor(compressor_name, AnotherZlibCompressorWrapper())
+
+    with raises(ValueError) as excinfo:
+        register_compressor(compressor_name,
+                            StandardLibGzipCompressorWrapper())
+    excinfo.match("Compressor '{}' already registered."
+                  .format(compressor_name))
+
+    register_compressor(compressor_name, StandardLibGzipCompressorWrapper(),
+                        force=True)
+
+    assert compressor_name in _COMPRESSORS
+    assert _COMPRESSORS[compressor_name].fileobj_factory == gzip.GzipFile
+
+    # Remove this dummy compressor file from extra compressors because other
+    # tests might fail because of this
+    _COMPRESSORS.pop(compressor_name)
+
+
+@with_lz4
+def test_lz4_compression(tmpdir):
+    # Check that lz4 can be used when dependency is available.
+    import lz4.frame
+    compressor = 'lz4'
+    assert compressor in _COMPRESSORS
+    assert _COMPRESSORS[compressor].fileobj_factory == lz4.frame.LZ4FrameFile
+
+    fname = tmpdir.join('test.pkl').strpath
+    data = 'test data'
+    numpy_pickle.dump(data, fname, compress=compressor)
+
+    with open(fname, 'rb') as f:
+        assert f.read(len(_LZ4_PREFIX)) == _LZ4_PREFIX
+    assert numpy_pickle.load(fname) == data
+
+    # Test that LZ4 is applied based on file extension
+    numpy_pickle.dump(data, fname + '.lz4')
+    with open(fname, 'rb') as f:
+        assert f.read(len(_LZ4_PREFIX)) == _LZ4_PREFIX
+    assert numpy_pickle.load(fname) == data
+
+
+@without_lz4
+def test_lz4_compression_without_lz4(tmpdir):
+    # Check that lz4 cannot be used when dependency is not available.
+    fname = tmpdir.join('test.nolz4').strpath
+    data = 'test data'
+    msg = LZ4_NOT_INSTALLED_ERROR
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump(data, fname, compress='lz4')
+    excinfo.match(msg)
+
+    with raises(ValueError) as excinfo:
+        numpy_pickle.dump(data, fname + '.lz4')
+    excinfo.match(msg)
+
+
+protocols = [pickle.DEFAULT_PROTOCOL]
+if pickle.HIGHEST_PROTOCOL != pickle.DEFAULT_PROTOCOL:
+    protocols.append(pickle.HIGHEST_PROTOCOL)
+
+
+@with_numpy
+@parametrize('protocol', protocols)
+def test_memmap_alignment_padding(tmpdir, protocol):
+    # Test that memmaped arrays returned by numpy.load are correctly aligned
+    fname = tmpdir.join('test.mmap').strpath
+
+    a = np.random.randn(2)
+    numpy_pickle.dump(a, fname, protocol=protocol)
+    memmap = numpy_pickle.load(fname, mmap_mode='r')
+    assert isinstance(memmap, np.memmap)
+    np.testing.assert_array_equal(a, memmap)
+    assert (
+        memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0)
+    assert memmap.flags.aligned
+
+    array_list = [
+        np.random.randn(2), np.random.randn(2),
+        np.random.randn(2), np.random.randn(2)
+    ]
+
+    # On Windows OSError 22 if reusing the same path for memmap ...
+    fname = tmpdir.join('test1.mmap').strpath
+    numpy_pickle.dump(array_list, fname, protocol=protocol)
+    l_reloaded = numpy_pickle.load(fname, mmap_mode='r')
+
+    for idx, memmap in enumerate(l_reloaded):
+        assert isinstance(memmap, np.memmap)
+        np.testing.assert_array_equal(array_list[idx], memmap)
+        assert (
+            memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0)
+        assert memmap.flags.aligned
+
+    array_dict = {
+        'a0': np.arange(2, dtype=np.uint8),
+        'a1': np.arange(3, dtype=np.uint8),
+        'a2': np.arange(5, dtype=np.uint8),
+        'a3': np.arange(7, dtype=np.uint8),
+        'a4': np.arange(11, dtype=np.uint8),
+        'a5': np.arange(13, dtype=np.uint8),
+        'a6': np.arange(17, dtype=np.uint8),
+        'a7': np.arange(19, dtype=np.uint8),
+        'a8': np.arange(23, dtype=np.uint8),
+    }
+
+    # On Windows OSError 22 if reusing the same path for memmap ...
+    fname = tmpdir.join('test2.mmap').strpath
+    numpy_pickle.dump(array_dict, fname, protocol=protocol)
+    d_reloaded = numpy_pickle.load(fname, mmap_mode='r')
+
+    for key, memmap in d_reloaded.items():
+        assert isinstance(memmap, np.memmap)
+        np.testing.assert_array_equal(array_dict[key], memmap)
+        assert (
+            memmap.ctypes.data % numpy_pickle.NUMPY_ARRAY_ALIGNMENT_BYTES == 0)
+        assert memmap.flags.aligned

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_numpy_pickle_compat.py

@@ -0,0 +1,16 @@
+"""Test the old numpy pickler, compatibility version."""
+
+# numpy_pickle is not a drop-in replacement of pickle, as it takes
+# filenames instead of open files as arguments.
+from joblib import numpy_pickle_compat
+
+
+def test_z_file(tmpdir):
+    # Test saving and loading data with Zfiles.
+    filename = tmpdir.join('test.pkl').strpath
+    data = numpy_pickle_compat.asbytes('Foo, \n Bar, baz, \n\nfoobar')
+    with open(filename, 'wb') as f:
+        numpy_pickle_compat.write_zfile(f, data)
+    with open(filename, 'rb') as f:
+        data_read = numpy_pickle_compat.read_zfile(f)
+    assert data == data_read

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_parallel.py

@@ -0,0 +1,2056 @@
+"""
+Test the parallel module.
+"""
+
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# Copyright (c) 2010-2011 Gael Varoquaux
+# License: BSD Style, 3 clauses.
+
+import os
+import sys
+import time
+import mmap
+import weakref
+import warnings
+import threading
+from traceback import format_exception
+from math import sqrt
+from time import sleep
+from pickle import PicklingError
+from contextlib import nullcontext
+from multiprocessing import TimeoutError
+import pytest
+
+import joblib
+from joblib import parallel
+from joblib import dump, load
+
+from joblib._multiprocessing_helpers import mp
+
+from joblib.test.common import np, with_numpy
+from joblib.test.common import with_multiprocessing
+from joblib.test.common import IS_PYPY, force_gc_pypy
+from joblib.testing import (parametrize, raises, check_subprocess_call,
+                            skipif, warns)
+
+if mp is not None:
+    # Loky is not available if multiprocessing is not
+    from joblib.externals.loky import get_reusable_executor
+
+from queue import Queue
+
+try:
+    import posix
+except ImportError:
+    posix = None
+
+try:
+    from ._openmp_test_helper.parallel_sum import parallel_sum
+except ImportError:
+    parallel_sum = None
+
+try:
+    import distributed
+except ImportError:
+    distributed = None
+
+from joblib._parallel_backends import SequentialBackend
+from joblib._parallel_backends import ThreadingBackend
+from joblib._parallel_backends import MultiprocessingBackend
+from joblib._parallel_backends import ParallelBackendBase
+from joblib._parallel_backends import LokyBackend
+
+from joblib.parallel import Parallel, delayed
+from joblib.parallel import parallel_config
+from joblib.parallel import parallel_backend
+from joblib.parallel import register_parallel_backend
+from joblib.parallel import effective_n_jobs, cpu_count
+
+from joblib.parallel import mp, BACKENDS, DEFAULT_BACKEND
+
+
+RETURN_GENERATOR_BACKENDS = BACKENDS.copy()
+RETURN_GENERATOR_BACKENDS.pop("multiprocessing", None)
+
+ALL_VALID_BACKENDS = [None] + sorted(BACKENDS.keys())
+# Add instances of backend classes deriving from ParallelBackendBase
+ALL_VALID_BACKENDS += [BACKENDS[backend_str]() for backend_str in BACKENDS]
+if mp is None:
+    PROCESS_BACKENDS = []
+else:
+    PROCESS_BACKENDS = ['multiprocessing', 'loky']
+PARALLEL_BACKENDS = PROCESS_BACKENDS + ['threading']
+
+if hasattr(mp, 'get_context'):
+    # Custom multiprocessing context in Python 3.4+
+    ALL_VALID_BACKENDS.append(mp.get_context('spawn'))
+
+DefaultBackend = BACKENDS[DEFAULT_BACKEND]
+
+
+def get_workers(backend):
+    return getattr(backend, '_pool', getattr(backend, '_workers', None))
+
+
+def division(x, y):
+    return x / y
+
+
+def square(x):
+    return x ** 2
+
+
+class MyExceptionWithFinickyInit(Exception):
+    """An exception class with non trivial __init__
+    """
+    def __init__(self, a, b, c, d):
+        pass
+
+
+def exception_raiser(x, custom_exception=False):
+    if x == 7:
+        raise (MyExceptionWithFinickyInit('a', 'b', 'c', 'd')
+               if custom_exception else ValueError)
+    return x
+
+
+def interrupt_raiser(x):
+    time.sleep(.05)
+    raise KeyboardInterrupt
+
+
+def f(x, y=0, z=0):
+    """ A module-level function so that it can be spawn with
+    multiprocessing.
+    """
+    return x ** 2 + y + z
+
+
+def _active_backend_type():
+    return type(parallel.get_active_backend()[0])
+
+
+def parallel_func(inner_n_jobs, backend):
+    return Parallel(n_jobs=inner_n_jobs, backend=backend)(
+        delayed(square)(i) for i in range(3))
+
+
+###############################################################################
+def test_cpu_count():
+    assert cpu_count() > 0
+
+
+def test_effective_n_jobs():
+    assert effective_n_jobs() > 0
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+@pytest.mark.parametrize(
+    "backend_n_jobs, expected_n_jobs",
+    [(3, 3), (-1, effective_n_jobs(n_jobs=-1)), (None, 1)],
+    ids=["positive-int", "negative-int", "None"]
+)
+@with_multiprocessing
+def test_effective_n_jobs_None(context, backend_n_jobs, expected_n_jobs):
+    # check the number of effective jobs when `n_jobs=None`
+    # non-regression test for https://github.com/joblib/joblib/issues/984
+    with context("threading", n_jobs=backend_n_jobs):
+        # when using a backend, the default of number jobs will be the one set
+        # in the backend
+        assert effective_n_jobs(n_jobs=None) == expected_n_jobs
+    # without any backend, None will default to a single job
+    assert effective_n_jobs(n_jobs=None) == 1
+
+
+###############################################################################
+# Test parallel
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+@parametrize('n_jobs', [1, 2, -1, -2])
+@parametrize('verbose', [2, 11, 100])
+def test_simple_parallel(backend, n_jobs, verbose):
+    assert ([square(x) for x in range(5)] ==
+            Parallel(n_jobs=n_jobs, backend=backend,
+                     verbose=verbose)(
+                delayed(square)(x) for x in range(5)))
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+def test_main_thread_renamed_no_warning(backend, monkeypatch):
+    # Check that no default backend relies on the name of the main thread:
+    # https://github.com/joblib/joblib/issues/180#issuecomment-253266247
+    # Some programs use a different name for the main thread. This is the case
+    # for uWSGI apps for instance.
+    monkeypatch.setattr(target=threading.current_thread(), name='name',
+                        value='some_new_name_for_the_main_thread')
+
+    with warnings.catch_warnings(record=True) as warninfo:
+        results = Parallel(n_jobs=2, backend=backend)(
+            delayed(square)(x) for x in range(3))
+        assert results == [0, 1, 4]
+
+    # Due to the default parameters of LokyBackend, there is a chance that
+    # warninfo catches Warnings from worker timeouts. We remove it if it exists
+    warninfo = [w for w in warninfo if "worker timeout" not in str(w.message)]
+
+    # The multiprocessing backend will raise a warning when detecting that is
+    # started from the non-main thread. Let's check that there is no false
+    # positive because of the name change.
+    assert len(warninfo) == 0
+
+
+def _assert_warning_nested(backend, inner_n_jobs, expected):
+    with warnings.catch_warnings(record=True) as warninfo:
+        warnings.simplefilter("always")
+        parallel_func(backend=backend, inner_n_jobs=inner_n_jobs)
+
+    warninfo = [w.message for w in warninfo]
+    if expected:
+        if warninfo:
+            warnings_are_correct = all(
+                'backed parallel loops cannot' in each.args[0]
+                for each in warninfo
+            )
+            # With Python nogil, when the outer backend is threading, we might
+            # see more that one warning
+            warnings_have_the_right_length = (
+                len(warninfo) >= 1 if getattr(sys.flags, 'nogil', False)
+                else len(warninfo) == 1)
+            return warnings_are_correct and warnings_have_the_right_length
+
+        return False
+    else:
+        assert not warninfo
+        return True
+
+
+@with_multiprocessing
+@parametrize('parent_backend,child_backend,expected', [
+    ('loky', 'multiprocessing', True),
+    ('loky', 'loky', False),
+    ('multiprocessing', 'multiprocessing', True),
+    ('multiprocessing', 'loky', True),
+    ('threading', 'multiprocessing', True),
+    ('threading', 'loky', True),
+])
+def test_nested_parallel_warnings(parent_backend, child_backend, expected):
+
+    # no warnings if inner_n_jobs=1
+    Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(_assert_warning_nested)(
+            backend=child_backend, inner_n_jobs=1,
+            expected=False)
+        for _ in range(5))
+
+    #  warnings if inner_n_jobs != 1 and expected
+    res = Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(_assert_warning_nested)(
+            backend=child_backend, inner_n_jobs=2,
+            expected=expected)
+        for _ in range(5))
+
+    # warning handling is not thread safe. One thread might see multiple
+    # warning or no warning at all.
+    if parent_backend == "threading":
+        if IS_PYPY and not any(res):
+            # Related to joblib#1426, should be removed once it is solved.
+            pytest.xfail(reason="This test often fails in PyPy.")
+        assert any(res)
+    else:
+        assert all(res)
+
+
+@with_multiprocessing
+@parametrize('backend', ['loky', 'multiprocessing', 'threading'])
+def test_background_thread_parallelism(backend):
+    is_run_parallel = [False]
+
+    def background_thread(is_run_parallel):
+        with warnings.catch_warnings(record=True) as warninfo:
+            Parallel(n_jobs=2)(
+                delayed(sleep)(.1) for _ in range(4))
+        print(len(warninfo))
+        is_run_parallel[0] = len(warninfo) == 0
+
+    t = threading.Thread(target=background_thread, args=(is_run_parallel,))
+    t.start()
+    t.join()
+    assert is_run_parallel[0]
+
+
+def nested_loop(backend):
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(square)(.01) for _ in range(2))
+
+
+@parametrize('child_backend', BACKENDS)
+@parametrize('parent_backend', BACKENDS)
+def test_nested_loop(parent_backend, child_backend):
+    Parallel(n_jobs=2, backend=parent_backend)(
+        delayed(nested_loop)(child_backend) for _ in range(2))
+
+
+def raise_exception(backend):
+    raise ValueError
+
+
+@with_multiprocessing
+def test_nested_loop_with_exception_with_loky():
+    with raises(ValueError):
+        with Parallel(n_jobs=2, backend="loky") as parallel:
+            parallel([delayed(nested_loop)("loky"),
+                      delayed(raise_exception)("loky")])
+
+
+def test_mutate_input_with_threads():
+    """Input is mutable when using the threading backend"""
+    q = Queue(maxsize=5)
+    Parallel(n_jobs=2, backend="threading")(
+        delayed(q.put)(1) for _ in range(5))
+    assert q.full()
+
+
+@parametrize('n_jobs', [1, 2, 3])
+def test_parallel_kwargs(n_jobs):
+    """Check the keyword argument processing of pmap."""
+    lst = range(10)
+    assert ([f(x, y=1) for x in lst] ==
+            Parallel(n_jobs=n_jobs)(delayed(f)(x, y=1) for x in lst))
+
+
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_as_context_manager(backend):
+    lst = range(10)
+    expected = [f(x, y=1) for x in lst]
+
+    with Parallel(n_jobs=4, backend=backend) as p:
+        # Internally a pool instance has been eagerly created and is managed
+        # via the context manager protocol
+        managed_backend = p._backend
+
+        # We make call with the managed parallel object several times inside
+        # the managed block:
+        assert expected == p(delayed(f)(x, y=1) for x in lst)
+        assert expected == p(delayed(f)(x, y=1) for x in lst)
+
+        # Those calls have all used the same pool instance:
+        if mp is not None:
+            assert get_workers(managed_backend) is get_workers(p._backend)
+
+    # As soon as we exit the context manager block, the pool is terminated and
+    # no longer referenced from the parallel object:
+    if mp is not None:
+        assert get_workers(p._backend) is None
+
+    # It's still possible to use the parallel instance in non-managed mode:
+    assert expected == p(delayed(f)(x, y=1) for x in lst)
+    if mp is not None:
+        assert get_workers(p._backend) is None
+
+
+@with_multiprocessing
+def test_parallel_pickling():
+    """ Check that pmap captures the errors when it is passed an object
+        that cannot be pickled.
+    """
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise RuntimeError('123')
+
+    with raises(PicklingError, match=r"the task to send"):
+        Parallel(n_jobs=2, backend='loky')(delayed(id)(
+            UnpicklableObject()) for _ in range(10))
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('byteorder', ['<', '>', '='])
+def test_parallel_byteorder_corruption(byteorder):
+
+    def inspect_byteorder(x):
+        return x, x.dtype.byteorder
+
+    x = np.arange(6).reshape((2, 3)).view(f'{byteorder}i4')
+
+    initial_np_byteorder = x.dtype.byteorder
+
+    result = Parallel(n_jobs=2, backend='loky')(
+        delayed(inspect_byteorder)(x) for _ in range(3)
+    )
+
+    for x_returned, byteorder_in_worker in result:
+        assert byteorder_in_worker == initial_np_byteorder
+        assert byteorder_in_worker == x_returned.dtype.byteorder
+        np.testing.assert_array_equal(x, x_returned)
+
+
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_timeout_success(backend):
+    # Check that timeout isn't thrown when function is fast enough
+    assert len(Parallel(n_jobs=2, backend=backend, timeout=30)(
+        delayed(sleep)(0.001) for x in range(10))) == 10
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_parallel_timeout_fail(backend):
+    # Check that timeout properly fails when function is too slow
+    with raises(TimeoutError):
+        Parallel(n_jobs=2, backend=backend, timeout=0.01)(
+            delayed(sleep)(10) for x in range(10))
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_error_capture(backend):
+    # Check that error are captured, and that correct exceptions
+    # are raised.
+    if mp is not None:
+        with raises(ZeroDivisionError):
+            Parallel(n_jobs=2, backend=backend)(
+                [delayed(division)(x, y)
+                    for x, y in zip((0, 1), (1, 0))])
+
+        with raises(KeyboardInterrupt):
+            Parallel(n_jobs=2, backend=backend)(
+                [delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+        # Try again with the context manager API
+        with Parallel(n_jobs=2, backend=backend) as parallel:
+            assert get_workers(parallel._backend) is not None
+            original_workers = get_workers(parallel._backend)
+
+            with raises(ZeroDivisionError):
+                parallel([delayed(division)(x, y)
+                          for x, y in zip((0, 1), (1, 0))])
+
+            # The managed pool should still be available and be in a working
+            # state despite the previously raised (and caught) exception
+            assert get_workers(parallel._backend) is not None
+
+            # The pool should have been interrupted and restarted:
+            assert get_workers(parallel._backend) is not original_workers
+
+            assert ([f(x, y=1) for x in range(10)] ==
+                    parallel(delayed(f)(x, y=1) for x in range(10)))
+
+            original_workers = get_workers(parallel._backend)
+            with raises(KeyboardInterrupt):
+                parallel([delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+            # The pool should still be available despite the exception
+            assert get_workers(parallel._backend) is not None
+
+            # The pool should have been interrupted and restarted:
+            assert get_workers(parallel._backend) is not original_workers
+
+            assert ([f(x, y=1) for x in range(10)] ==
+                    parallel(delayed(f)(x, y=1) for x in range(10))), (
+                parallel._iterating, parallel.n_completed_tasks,
+                parallel.n_dispatched_tasks, parallel._aborting
+            )
+
+        # Check that the inner pool has been terminated when exiting the
+        # context manager
+        assert get_workers(parallel._backend) is None
+    else:
+        with raises(KeyboardInterrupt):
+            Parallel(n_jobs=2)(
+                [delayed(interrupt_raiser)(x) for x in (1, 0)])
+
+    # wrapped exceptions should inherit from the class of the original
+    # exception to make it easy to catch them
+    with raises(ZeroDivisionError):
+        Parallel(n_jobs=2)(
+            [delayed(division)(x, y) for x, y in zip((0, 1), (1, 0))])
+
+    with raises(MyExceptionWithFinickyInit):
+        Parallel(n_jobs=2, verbose=0)(
+            (delayed(exception_raiser)(i, custom_exception=True)
+             for i in range(30)))
+
+
+@with_multiprocessing
+@parametrize('backend', BACKENDS)
+def test_error_in_task_iterator(backend):
+
+    def my_generator(raise_at=0):
+        for i in range(20):
+            if i == raise_at:
+                raise ValueError("Iterator Raising Error")
+            yield i
+
+    with Parallel(n_jobs=2, backend=backend) as p:
+        # The error is raised in the pre-dispatch phase
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=0))
+
+        # The error is raised when dispatching a new task after the
+        # pre-dispatch (likely to happen in a different thread)
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=5))
+
+        # Same, but raises long after the pre-dispatch phase
+        with raises(ValueError, match="Iterator Raising Error"):
+            p(delayed(square)(i) for i in my_generator(raise_at=19))
+
+
+def consumer(queue, item):
+    queue.append('Consumed %s' % item)
+
+
+@parametrize('backend', BACKENDS)
+@parametrize('batch_size, expected_queue',
+             [(1, ['Produced 0', 'Consumed 0',
+                   'Produced 1', 'Consumed 1',
+                   'Produced 2', 'Consumed 2',
+                   'Produced 3', 'Consumed 3',
+                   'Produced 4', 'Consumed 4',
+                   'Produced 5', 'Consumed 5']),
+              (4, [  # First Batch
+                  'Produced 0', 'Produced 1', 'Produced 2', 'Produced 3',
+                  'Consumed 0', 'Consumed 1', 'Consumed 2', 'Consumed 3',
+                     # Second batch
+                  'Produced 4', 'Produced 5', 'Consumed 4', 'Consumed 5'])])
+def test_dispatch_one_job(backend, batch_size, expected_queue):
+    """ Test that with only one job, Parallel does act as a iterator.
+    """
+    queue = list()
+
+    def producer():
+        for i in range(6):
+            queue.append('Produced %i' % i)
+            yield i
+
+    Parallel(n_jobs=1, batch_size=batch_size, backend=backend)(
+        delayed(consumer)(queue, x) for x in producer())
+    assert queue == expected_queue
+    assert len(queue) == 12
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+def test_dispatch_multiprocessing(backend):
+    """ Check that using pre_dispatch Parallel does indeed dispatch items
+        lazily.
+    """
+    manager = mp.Manager()
+    queue = manager.list()
+
+    def producer():
+        for i in range(6):
+            queue.append('Produced %i' % i)
+            yield i
+
+    Parallel(n_jobs=2, batch_size=1, pre_dispatch=3, backend=backend)(
+        delayed(consumer)(queue, 'any') for _ in producer())
+
+    queue_contents = list(queue)
+    assert queue_contents[0] == 'Produced 0'
+
+    # Only 3 tasks are pre-dispatched out of 6. The 4th task is dispatched only
+    # after any of the first 3 jobs have completed.
+    first_consumption_index = queue_contents[:4].index('Consumed any')
+    assert first_consumption_index > -1
+
+    produced_3_index = queue_contents.index('Produced 3')  # 4th task produced
+    assert produced_3_index > first_consumption_index
+
+    assert len(queue) == 12
+
+
+def test_batching_auto_threading():
+    # batching='auto' with the threading backend leaves the effective batch
+    # size to 1 (no batching) as it has been found to never be beneficial with
+    # this low-overhead backend.
+
+    with Parallel(n_jobs=2, batch_size='auto', backend='threading') as p:
+        p(delayed(id)(i) for i in range(5000))  # many very fast tasks
+        assert p._backend.compute_batch_size() == 1
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_batching_auto_subprocesses(backend):
+    with Parallel(n_jobs=2, batch_size='auto', backend=backend) as p:
+        p(delayed(id)(i) for i in range(5000))  # many very fast tasks
+
+        # It should be strictly larger than 1 but as we don't want heisen
+        # failures on clogged CI worker environment be safe and only check that
+        # it's a strictly positive number.
+        assert p._backend.compute_batch_size() > 0
+
+
+def test_exception_dispatch():
+    """Make sure that exception raised during dispatch are indeed captured"""
+    with raises(ValueError):
+        Parallel(n_jobs=2, pre_dispatch=16, verbose=0)(
+            delayed(exception_raiser)(i) for i in range(30))
+
+
+def nested_function_inner(i):
+    Parallel(n_jobs=2)(
+        delayed(exception_raiser)(j) for j in range(30))
+
+
+def nested_function_outer(i):
+    Parallel(n_jobs=2)(
+        delayed(nested_function_inner)(j) for j in range(30))
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+@pytest.mark.xfail(reason="https://github.com/joblib/loky/pull/255")
+def test_nested_exception_dispatch(backend):
+    """Ensure errors for nested joblib cases gets propagated
+
+    We rely on the Python 3 built-in __cause__ system that already
+    report this kind of information to the user.
+    """
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=2, backend=backend)(
+            delayed(nested_function_outer)(i) for i in range(30))
+
+    # Check that important information such as function names are visible
+    # in the final error message reported to the user
+    report_lines = format_exception(excinfo.type, excinfo.value, excinfo.tb)
+    report = "".join(report_lines)
+    assert 'nested_function_outer' in report
+    assert 'nested_function_inner' in report
+    assert 'exception_raiser' in report
+
+    assert type(excinfo.value) is ValueError
+
+
+class FakeParallelBackend(SequentialBackend):
+    """Pretends to run concurrently while running sequentially."""
+
+    def configure(self, n_jobs=1, parallel=None, **backend_args):
+        self.n_jobs = self.effective_n_jobs(n_jobs)
+        self.parallel = parallel
+        return n_jobs
+
+    def effective_n_jobs(self, n_jobs=1):
+        if n_jobs < 0:
+            n_jobs = max(mp.cpu_count() + 1 + n_jobs, 1)
+        return n_jobs
+
+
+def test_invalid_backend():
+    with raises(ValueError, match="Invalid backend:"):
+        Parallel(backend='unit-testing')
+
+    with raises(ValueError, match="Invalid backend:"):
+        with parallel_config(backend='unit-testing'):
+            pass
+
+    with raises(ValueError, match="Invalid backend:"):
+        with parallel_config(backend='unit-testing'):
+            pass
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+def test_invalid_njobs(backend):
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=0, backend=backend)._initialize_backend()
+    assert "n_jobs == 0 in Parallel has no meaning" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs=0.5, backend=backend)._initialize_backend()
+    assert "n_jobs == 0 in Parallel has no meaning" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs="2.3", backend=backend)._initialize_backend()
+    assert "n_jobs could not be converted to int" in str(excinfo.value)
+
+    with raises(ValueError) as excinfo:
+        Parallel(n_jobs="invalid_str", backend=backend)._initialize_backend()
+    assert "n_jobs could not be converted to int" in str(excinfo.value)
+
+
+@with_multiprocessing
+@parametrize('backend', PARALLEL_BACKENDS)
+@parametrize('n_jobs', ['2', 2.3, 2])
+def test_njobs_converted_to_int(backend, n_jobs):
+    p = Parallel(n_jobs=n_jobs, backend=backend)
+    assert p._effective_n_jobs() == 2
+
+    res = p(delayed(square)(i) for i in range(10))
+    assert all(r == square(i) for i, r in enumerate(res))
+
+
+def test_register_parallel_backend():
+    try:
+        register_parallel_backend("test_backend", FakeParallelBackend)
+        assert "test_backend" in BACKENDS
+        assert BACKENDS["test_backend"] == FakeParallelBackend
+    finally:
+        del BACKENDS["test_backend"]
+
+
+def test_overwrite_default_backend():
+    assert _active_backend_type() == DefaultBackend
+    try:
+        register_parallel_backend("threading", BACKENDS["threading"],
+                                  make_default=True)
+        assert _active_backend_type() == ThreadingBackend
+    finally:
+        # Restore the global default manually
+        parallel.DEFAULT_BACKEND = DEFAULT_BACKEND
+    assert _active_backend_type() == DefaultBackend
+
+
+@skipif(mp is not None, reason="Only without multiprocessing")
+def test_backend_no_multiprocessing():
+    with warns(UserWarning,
+               match="joblib backend '.*' is not available on.*"):
+        Parallel(backend='loky')(delayed(square)(i) for i in range(3))
+
+    # The below should now work without problems
+    with parallel_config(backend='loky'):
+        Parallel()(delayed(square)(i) for i in range(3))
+
+
+def check_backend_context_manager(context, backend_name):
+    with context(backend_name, n_jobs=3):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert active_n_jobs == 3
+        assert effective_n_jobs(3) == 3
+        p = Parallel()
+        assert p.n_jobs == 3
+        if backend_name == 'multiprocessing':
+            assert type(active_backend) is MultiprocessingBackend
+            assert type(p._backend) is MultiprocessingBackend
+        elif backend_name == 'loky':
+            assert type(active_backend) is LokyBackend
+            assert type(p._backend) is LokyBackend
+        elif backend_name == 'threading':
+            assert type(active_backend) is ThreadingBackend
+            assert type(p._backend) is ThreadingBackend
+        elif backend_name.startswith('test_'):
+            assert type(active_backend) is FakeParallelBackend
+            assert type(p._backend) is FakeParallelBackend
+
+
+all_backends_for_context_manager = PARALLEL_BACKENDS[:]
+all_backends_for_context_manager.extend(
+    ['test_backend_%d' % i for i in range(3)]
+)
+
+
+@with_multiprocessing
+@parametrize('backend', all_backends_for_context_manager)
+@parametrize('context', [parallel_backend, parallel_config])
+def test_backend_context_manager(monkeypatch, backend, context):
+    if backend not in BACKENDS:
+        monkeypatch.setitem(BACKENDS, backend, FakeParallelBackend)
+
+    assert _active_backend_type() == DefaultBackend
+    # check that this possible to switch parallel backends sequentially
+    check_backend_context_manager(context, backend)
+
+    # The default backend is restored
+    assert _active_backend_type() == DefaultBackend
+
+    # Check that context manager switching is thread safe:
+    Parallel(n_jobs=2, backend='threading')(
+        delayed(check_backend_context_manager)(context, b)
+        for b in all_backends_for_context_manager if not b)
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+class ParameterizedParallelBackend(SequentialBackend):
+    """Pretends to run conncurrently while running sequentially."""
+
+    def __init__(self, param=None):
+        if param is None:
+            raise ValueError('param should not be None')
+        self.param = param
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_parameterized_backend_context_manager(monkeypatch, context):
+    monkeypatch.setitem(BACKENDS, 'param_backend',
+                        ParameterizedParallelBackend)
+    assert _active_backend_type() == DefaultBackend
+
+    with context('param_backend', param=42, n_jobs=3):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert type(active_backend) is ParameterizedParallelBackend
+        assert active_backend.param == 42
+        assert active_n_jobs == 3
+        p = Parallel()
+        assert p.n_jobs == 3
+        assert p._backend is active_backend
+        results = p(delayed(sqrt)(i) for i in range(5))
+    assert results == [sqrt(i) for i in range(5)]
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_directly_parameterized_backend_context_manager(context):
+    assert _active_backend_type() == DefaultBackend
+
+    # Check that it's possible to pass a backend instance directly,
+    # without registration
+    with context(ParameterizedParallelBackend(param=43), n_jobs=5):
+        active_backend, active_n_jobs = parallel.get_active_backend()
+        assert type(active_backend) is ParameterizedParallelBackend
+        assert active_backend.param == 43
+        assert active_n_jobs == 5
+        p = Parallel()
+        assert p.n_jobs == 5
+        assert p._backend is active_backend
+        results = p(delayed(sqrt)(i) for i in range(5))
+    assert results == [sqrt(i) for i in range(5)]
+
+    # The default backend is again restored
+    assert _active_backend_type() == DefaultBackend
+
+
+def sleep_and_return_pid():
+    sleep(.1)
+    return os.getpid()
+
+
+def get_nested_pids():
+    assert _active_backend_type() == ThreadingBackend
+    # Assert that the nested backend does not change the default number of
+    # jobs used in Parallel
+    assert Parallel()._effective_n_jobs() == 1
+
+    # Assert that the tasks are running only on one process
+    return Parallel(n_jobs=2)(delayed(sleep_and_return_pid)()
+                              for _ in range(2))
+
+
+class MyBackend(joblib._parallel_backends.LokyBackend):
+    """Backend to test backward compatibility with older backends"""
+    def get_nested_backend(self, ):
+        # Older backends only return a backend, without n_jobs indications.
+        return super(MyBackend, self).get_nested_backend()[0]
+
+
+register_parallel_backend('back_compat_backend', MyBackend)
+
+
+@with_multiprocessing
+@parametrize('backend', ['threading', 'loky', 'multiprocessing',
+                         'back_compat_backend'])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_backend_context_manager(context, backend):
+    # Check that by default, nested parallel calls will always use the
+    # ThreadingBackend
+
+    with context(backend):
+        pid_groups = Parallel(n_jobs=2)(
+            delayed(get_nested_pids)()
+            for _ in range(10)
+        )
+        for pid_group in pid_groups:
+            assert len(set(pid_group)) == 1
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, -1, None])
+@parametrize('backend', PARALLEL_BACKENDS)
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_backend_in_sequential(backend, n_jobs, context):
+    # Check that by default, nested parallel calls will always use the
+    # ThreadingBackend
+
+    def check_nested_backend(expected_backend_type, expected_n_job):
+        # Assert that the sequential backend at top level, does not change the
+        # backend for nested calls.
+        assert _active_backend_type() == BACKENDS[expected_backend_type]
+
+        # Assert that the nested backend in SequentialBackend does not change
+        # the default number of jobs used in Parallel
+        expected_n_job = effective_n_jobs(expected_n_job)
+        assert Parallel()._effective_n_jobs() == expected_n_job
+
+    Parallel(n_jobs=1)(
+        delayed(check_nested_backend)(DEFAULT_BACKEND, 1)
+        for _ in range(10)
+    )
+
+    with context(backend, n_jobs=n_jobs):
+        Parallel(n_jobs=1)(
+            delayed(check_nested_backend)(backend, n_jobs)
+            for _ in range(10)
+        )
+
+
+def check_nesting_level(context, inner_backend, expected_level):
+    with context(inner_backend) as ctx:
+        if context is parallel_config:
+            backend = ctx["backend"]
+        if context is parallel_backend:
+            backend = ctx[0]
+        assert backend.nesting_level == expected_level
+
+
+@with_multiprocessing
+@parametrize('outer_backend', PARALLEL_BACKENDS)
+@parametrize('inner_backend', PARALLEL_BACKENDS)
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_nesting_level(context, outer_backend, inner_backend):
+    # Check that the nesting level for the backend is correctly set
+    check_nesting_level(context, outer_backend, 0)
+
+    Parallel(n_jobs=2, backend=outer_backend)(
+        delayed(check_nesting_level)(context, inner_backend, 1)
+        for _ in range(10)
+    )
+
+    with context(inner_backend, n_jobs=2):
+        Parallel()(delayed(check_nesting_level)(context, inner_backend, 1)
+                   for _ in range(10))
+
+
+@with_multiprocessing
+@parametrize("context", [parallel_config, parallel_backend])
+@parametrize('with_retrieve_callback', [True, False])
+def test_retrieval_context(context, with_retrieve_callback):
+    import contextlib
+
+    class MyBackend(ThreadingBackend):
+        i = 0
+        supports_retrieve_callback = with_retrieve_callback
+
+        @contextlib.contextmanager
+        def retrieval_context(self):
+            self.i += 1
+            yield
+
+    register_parallel_backend("retrieval", MyBackend)
+
+    def nested_call(n):
+        return Parallel(n_jobs=2)(delayed(id)(i) for i in range(n))
+
+    with context("retrieval") as ctx:
+        Parallel(n_jobs=2)(
+            delayed(nested_call)(i)
+            for i in range(5)
+        )
+        if context is parallel_config:
+            assert ctx["backend"].i == 1
+        if context is parallel_backend:
+            assert ctx[0].i == 1
+
+
+###############################################################################
+# Test helpers
+
+@parametrize('batch_size', [0, -1, 1.42])
+def test_invalid_batch_size(batch_size):
+    with raises(ValueError):
+        Parallel(batch_size=batch_size)
+
+
+@parametrize('n_tasks, n_jobs, pre_dispatch, batch_size',
+             [(2, 2, 'all', 'auto'),
+              (2, 2, 'n_jobs', 'auto'),
+              (10, 2, 'n_jobs', 'auto'),
+              (517, 2, 'n_jobs', 'auto'),
+              (10, 2, 'n_jobs', 'auto'),
+              (10, 4, 'n_jobs', 'auto'),
+              (200, 12, 'n_jobs', 'auto'),
+              (25, 12, '2 * n_jobs', 1),
+              (250, 12, 'all', 1),
+              (250, 12, '2 * n_jobs', 7),
+              (200, 12, '2 * n_jobs', 'auto')])
+def test_dispatch_race_condition(n_tasks, n_jobs, pre_dispatch, batch_size):
+    # Check that using (async-)dispatch does not yield a race condition on the
+    # iterable generator that is not thread-safe natively.
+    # This is a non-regression test for the "Pool seems closed" class of error
+    params = {'n_jobs': n_jobs, 'pre_dispatch': pre_dispatch,
+              'batch_size': batch_size}
+    expected = [square(i) for i in range(n_tasks)]
+    results = Parallel(**params)(delayed(square)(i) for i in range(n_tasks))
+    assert results == expected
+
+
+@with_multiprocessing
+def test_default_mp_context():
+    mp_start_method = mp.get_start_method()
+    p = Parallel(n_jobs=2, backend='multiprocessing')
+    context = p._backend_args.get('context')
+    start_method = context.get_start_method()
+    assert start_method == mp_start_method
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_no_blas_crash_or_freeze_with_subprocesses(backend):
+    if backend == 'multiprocessing':
+        # Use the spawn backend that is both robust and available on all
+        # platforms
+        backend = mp.get_context('spawn')
+
+    # Check that on recent Python version, the 'spawn' start method can make
+    # it possible to use multiprocessing in conjunction of any BLAS
+    # implementation that happens to be used by numpy with causing a freeze or
+    # a crash
+    rng = np.random.RandomState(42)
+
+    # call BLAS DGEMM to force the initialization of the internal thread-pool
+    # in the main process
+    a = rng.randn(1000, 1000)
+    np.dot(a, a.T)
+
+    # check that the internal BLAS thread-pool is not in an inconsistent state
+    # in the worker processes managed by multiprocessing
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(np.dot)(a, a.T) for i in range(2))
+
+
+UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_NO_MAIN = """\
+from joblib import Parallel, delayed
+
+def square(x):
+    return x ** 2
+
+backend = "{}"
+if backend == "spawn":
+    from multiprocessing import get_context
+    backend = get_context(backend)
+
+print(Parallel(n_jobs=2, backend=backend)(
+      delayed(square)(i) for i in range(5)))
+"""
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_parallel_with_interactively_defined_functions(backend):
+    # When using the "-c" flag, interactive functions defined in __main__
+    # should work with any backend.
+    if backend == "multiprocessing" and mp.get_start_method() != "fork":
+        pytest.skip("Require fork start method to use interactively defined "
+                    "functions with multiprocessing.")
+    code = UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_NO_MAIN.format(backend)
+    check_subprocess_call(
+        [sys.executable, '-c', code], timeout=10,
+        stdout_regex=r'\[0, 1, 4, 9, 16\]')
+
+
+UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_MAIN = """\
+import sys
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed
+
+def run(f, x):
+    return f(x)
+
+{define_func}
+
+if __name__ == "__main__":
+    backend = "{backend}"
+    if backend == "spawn":
+        from multiprocessing import get_context
+        backend = get_context(backend)
+
+    callable_position = "{callable_position}"
+    if callable_position == "delayed":
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(square)(i) for i in range(5)))
+    elif callable_position == "args":
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(run)(square, i) for i in range(5)))
+    else:
+        print(Parallel(n_jobs=2, backend=backend)(
+                delayed(run)(f=square, x=i) for i in range(5)))
+"""
+
+SQUARE_MAIN = """\
+def square(x):
+    return x ** 2
+"""
+SQUARE_LOCAL = """\
+def gen_square():
+    def square(x):
+        return x ** 2
+    return square
+square = gen_square()
+"""
+SQUARE_LAMBDA = """\
+square = lambda x: x ** 2
+"""
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS + ([] if mp is None else ['spawn']))
+@parametrize('define_func', [SQUARE_MAIN, SQUARE_LOCAL, SQUARE_LAMBDA])
+@parametrize('callable_position', ['delayed', 'args', 'kwargs'])
+def test_parallel_with_unpicklable_functions_in_args(
+        backend, define_func, callable_position, tmpdir):
+    if backend in ['multiprocessing', 'spawn'] and (
+            define_func != SQUARE_MAIN or sys.platform == "win32"):
+        pytest.skip("Not picklable with pickle")
+    code = UNPICKLABLE_CALLABLE_SCRIPT_TEMPLATE_MAIN.format(
+        define_func=define_func, backend=backend,
+        callable_position=callable_position,
+        joblib_root_folder=os.path.dirname(os.path.dirname(joblib.__file__)))
+    code_file = tmpdir.join("unpicklable_func_script.py")
+    code_file.write(code)
+    check_subprocess_call(
+        [sys.executable, code_file.strpath], timeout=10,
+        stdout_regex=r'\[0, 1, 4, 9, 16\]')
+
+
+INTERACTIVE_DEFINED_FUNCTION_AND_CLASS_SCRIPT_CONTENT = """\
+import sys
+import faulthandler
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed
+from functools import partial
+
+class MyClass:
+    '''Class defined in the __main__ namespace'''
+    def __init__(self, value):
+        self.value = value
+
+
+def square(x, ignored=None, ignored2=None):
+    '''Function defined in the __main__ namespace'''
+    return x.value ** 2
+
+
+square2 = partial(square, ignored2='something')
+
+# Here, we do not need the `if __name__ == "__main__":` safeguard when
+# using the default `loky` backend (even on Windows).
+
+# To make debugging easier
+faulthandler.dump_traceback_later(30, exit=True)
+
+# The following baroque function call is meant to check that joblib
+# introspection rightfully uses cloudpickle instead of the (faster) pickle
+# module of the standard library when necessary. In particular cloudpickle is
+# necessary for functions and instances of classes interactively defined in the
+# __main__ module.
+
+print(Parallel(backend="loky", n_jobs=2)(
+    delayed(square2)(MyClass(i), ignored=[dict(a=MyClass(1))])
+    for i in range(5)
+))
+""".format(joblib_root_folder=os.path.dirname(
+    os.path.dirname(joblib.__file__)))
+
+
+@with_multiprocessing
+def test_parallel_with_interactively_defined_functions_loky(tmpdir):
+    # loky accepts interactive functions defined in __main__ and does not
+    # require if __name__ == '__main__' even when the __main__ module is
+    # defined by the result of the execution of a filesystem script.
+    script = tmpdir.join('joblib_interactively_defined_function.py')
+    script.write(INTERACTIVE_DEFINED_FUNCTION_AND_CLASS_SCRIPT_CONTENT)
+    check_subprocess_call(
+        [sys.executable, script.strpath],
+        stdout_regex=r'\[0, 1, 4, 9, 16\]',
+        timeout=None,  # rely on faulthandler to kill the process
+    )
+
+
+INTERACTIVELY_DEFINED_SUBCLASS_WITH_METHOD_SCRIPT_CONTENT = """\
+import sys
+# Make sure that joblib is importable in the subprocess launching this
+# script. This is needed in case we run the tests from the joblib root
+# folder without having installed joblib
+sys.path.insert(0, {joblib_root_folder!r})
+
+from joblib import Parallel, delayed, hash
+import multiprocessing as mp
+mp.util.log_to_stderr(5)
+
+class MyList(list):
+    '''MyList is interactively defined by MyList.append is a built-in'''
+    def __hash__(self):
+        # XXX: workaround limitation in cloudpickle
+        return hash(self).__hash__()
+
+l = MyList()
+
+print(Parallel(backend="loky", n_jobs=2)(
+    delayed(l.append)(i) for i in range(3)
+))
+""".format(joblib_root_folder=os.path.dirname(
+    os.path.dirname(joblib.__file__)))
+
+
+@with_multiprocessing
+def test_parallel_with_interactively_defined_bound_method_loky(tmpdir):
+    script = tmpdir.join('joblib_interactive_bound_method_script.py')
+    script.write(INTERACTIVELY_DEFINED_SUBCLASS_WITH_METHOD_SCRIPT_CONTENT)
+    check_subprocess_call([sys.executable, script.strpath],
+                          stdout_regex=r'\[None, None, None\]',
+                          stderr_regex=r'LokyProcess',
+                          timeout=15)
+
+
+def test_parallel_with_exhausted_iterator():
+    exhausted_iterator = iter([])
+    assert Parallel(n_jobs=2)(exhausted_iterator) == []
+
+
+def _cleanup_worker():
+    """Helper function to force gc in each worker."""
+    force_gc_pypy()
+    time.sleep(.1)
+
+
+def check_memmap(a):
+    if not isinstance(a, np.memmap):
+        raise TypeError('Expected np.memmap instance, got %r',
+                        type(a))
+    return a.copy()  # return a regular array instead of a memmap
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_auto_memmap_on_arrays_from_generator(backend):
+    # Non-regression test for a problem with a bad interaction between the
+    # GC collecting arrays recently created during iteration inside the
+    # parallel dispatch loop and the auto-memmap feature of Parallel.
+    # See: https://github.com/joblib/joblib/pull/294
+    def generate_arrays(n):
+        for i in range(n):
+            yield np.ones(10, dtype=np.float32) * i
+    # Use max_nbytes=1 to force the use of memory-mapping even for small
+    # arrays
+    results = Parallel(n_jobs=2, max_nbytes=1, backend=backend)(
+        delayed(check_memmap)(a) for a in generate_arrays(100))
+    for result, expected in zip(results, generate_arrays(len(results))):
+        np.testing.assert_array_equal(expected, result)
+
+    # Second call to force loky to adapt the executor by growing the number
+    # of worker processes. This is a non-regression test for:
+    # https://github.com/joblib/joblib/issues/629.
+    results = Parallel(n_jobs=4, max_nbytes=1, backend=backend)(
+        delayed(check_memmap)(a) for a in generate_arrays(100))
+    for result, expected in zip(results, generate_arrays(len(results))):
+        np.testing.assert_array_equal(expected, result)
+
+
+def identity(arg):
+    return arg
+
+
+@with_numpy
+@with_multiprocessing
+def test_memmap_with_big_offset(tmpdir):
+    fname = tmpdir.join('test.mmap').strpath
+    size = mmap.ALLOCATIONGRANULARITY
+    obj = [np.zeros(size, dtype='uint8'), np.ones(size, dtype='uint8')]
+    dump(obj, fname)
+    memmap = load(fname, mmap_mode='r')
+    result, = Parallel(n_jobs=2)(delayed(identity)(memmap) for _ in [0])
+    assert isinstance(memmap[1], np.memmap)
+    assert memmap[1].offset > size
+    np.testing.assert_array_equal(obj, result)
+
+
+def test_warning_about_timeout_not_supported_by_backend():
+    with warnings.catch_warnings(record=True) as warninfo:
+        Parallel(n_jobs=1, timeout=1)(delayed(square)(i) for i in range(50))
+    assert len(warninfo) == 1
+    w = warninfo[0]
+    assert isinstance(w.message, UserWarning)
+    assert str(w.message) == (
+        "The backend class 'SequentialBackend' does not support timeout. "
+        "You have set 'timeout=1' in Parallel but the 'timeout' parameter "
+        "will not be used.")
+
+
+def set_list_value(input_list, index, value):
+    input_list[index] = value
+    return value
+
+
+@pytest.mark.parametrize('n_jobs', [1, 2, 4])
+def test_parallel_return_order_with_return_as_generator_parameter(n_jobs):
+    # This test inserts values in a list in some expected order
+    # in sequential computing, and then checks that this order has been
+    # respected by Parallel output generator.
+    input_list = [0] * 5
+    result = Parallel(n_jobs=n_jobs, return_as="generator",
+                      backend='threading')(
+        delayed(set_list_value)(input_list, i, i) for i in range(5))
+
+    # Ensure that all the tasks are completed before checking the result
+    result = list(result)
+
+    assert all(v == r for v, r in zip(input_list, result))
+
+
+def _sqrt_with_delay(e, delay):
+    if delay:
+        sleep(30)
+    return sqrt(e)
+
+
+def _test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs):
+    # This test submits 10 tasks, but the second task is super slow. This test
+    # checks that the 9 other tasks return before the slow task is done, when
+    # `return_as` parameter is set to `'generator_unordered'`
+    result = Parallel(n_jobs=n_jobs, return_as="generator_unordered",
+                      backend=backend)(
+        delayed(_sqrt_with_delay)(i**2, (i == 1)) for i in range(10))
+
+    quickly_returned = sorted(next(result) for _ in range(9))
+
+    expected_quickly_returned = [0] + list(range(2, 10))
+
+    assert all(
+        v == r for v, r in zip(expected_quickly_returned, quickly_returned)
+    )
+
+    del result
+    force_gc_pypy()
+
+
+@pytest.mark.parametrize('n_jobs', [2, 4])
+# NB: for this test to work, the backend must be allowed to process tasks
+# concurrently, so at least two jobs with a non-sequential backend are
+# mandatory.
+@with_multiprocessing
+@parametrize('backend', set(RETURN_GENERATOR_BACKENDS) - {"sequential"})
+def test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs):
+    _test_parallel_unordered_generator_returns_fastest_first(backend, n_jobs)
+
+
+@pytest.mark.parametrize('n_jobs', [2, -1])
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_parallel_unordered_generator_returns_fastest_first_with_dask(
+        n_jobs, context
+):
+    with distributed.Client(
+            n_workers=2, threads_per_worker=2
+    ), context("dask"):
+        _test_parallel_unordered_generator_returns_fastest_first(None, n_jobs)
+
+
+@parametrize('backend', ALL_VALID_BACKENDS)
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_abort_backend(n_jobs, backend):
+    delays = ["a"] + [10] * 100
+    with raises(TypeError):
+        t_start = time.time()
+        Parallel(n_jobs=n_jobs, backend=backend)(
+            delayed(time.sleep)(i) for i in delays)
+    dt = time.time() - t_start
+    assert dt < 20
+
+
+def get_large_object(arg):
+    result = np.ones(int(5 * 1e5), dtype=bool)
+    result[0] = False
+    return result
+
+
+def _test_deadlock_with_generator(backend, return_as, n_jobs):
+    # Non-regression test for a race condition in the backends when the pickler
+    # is delayed by a large object.
+    with Parallel(n_jobs=n_jobs, backend=backend,
+                  return_as=return_as) as parallel:
+        result = parallel(delayed(get_large_object)(i) for i in range(10))
+        next(result)
+        next(result)
+        del result
+        # The gc in pypy can be delayed. Force it to make sure this test does
+        # not cause timeout on the CI.
+        force_gc_pypy()
+
+
+@with_numpy
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_deadlock_with_generator(backend, return_as, n_jobs):
+    _test_deadlock_with_generator(backend, return_as, n_jobs)
+
+
+@with_numpy
+@pytest.mark.parametrize('n_jobs', [2, -1])
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_deadlock_with_generator_and_dask(context, return_as, n_jobs):
+    with distributed.Client(
+            n_workers=2, threads_per_worker=2
+    ), context("dask"):
+        _test_deadlock_with_generator(None, return_as, n_jobs)
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call(backend, return_as, n_jobs):
+    # Non-regression test that ensures the dispatch of the tasks starts
+    # immediately when Parallel.__call__ is called. This test relies on the
+    # assumption that only one generator can be submitted at a time.
+    with raises(RuntimeError,
+                match="This Parallel instance is already running"):
+        parallel = Parallel(n_jobs, backend=backend, return_as=return_as)
+        g = parallel(delayed(sleep)(1) for _ in range(10))  # noqa: F841
+        t_start = time.time()
+        gen2 = parallel(delayed(id)(i) for i in range(100))  # noqa: F841
+
+    # Make sure that the error is raised quickly
+    assert time.time() - t_start < 2, (
+        "The error should be raised immediatly when submitting a new task "
+        "but it took more than 2s."
+    )
+
+    del g
+    # The gc in pypy can be delayed. Force it to make sure this test does not
+    # cause timeout on the CI.
+    force_gc_pypy()
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call_managed(backend, return_as, n_jobs):
+    # Non-regression test that ensures the dispatch of the tasks starts
+    # immediately when Parallel.__call__ is called. This test relies on the
+    # assumption that only one generator can be submitted at a time.
+    with Parallel(n_jobs, backend=backend,
+                  return_as=return_as) as parallel:
+        g = parallel(delayed(sleep)(10) for _ in range(10))  # noqa: F841
+        t_start = time.time()
+        with raises(RuntimeError,
+                    match="This Parallel instance is already running"):
+            g2 = parallel(delayed(id)(i) for i in range(100))  # noqa: F841
+
+        # Make sure that the error is raised quickly
+        assert time.time() - t_start < 2, (
+            "The error should be raised immediatly when submitting a new task "
+            "but it took more than 2s."
+        )
+
+    # The gc in pypy can be delayed. Force it to make sure this test does not
+    # cause timeout on the CI.
+    del g
+    force_gc_pypy()
+
+
+@parametrize('backend', RETURN_GENERATOR_BACKENDS)
+@parametrize('return_as_1', ["generator", "generator_unordered"])
+@parametrize('return_as_2', ["generator", "generator_unordered"])
+@parametrize('n_jobs', [1, 2, -2, -1])
+def test_multiple_generator_call_separated(
+        backend, return_as_1, return_as_2, n_jobs
+):
+    # Check that for separated Parallel, both tasks are correctly returned.
+    g = Parallel(n_jobs, backend=backend, return_as=return_as_1)(
+        delayed(sqrt)(i ** 2) for i in range(10)
+    )
+    g2 = Parallel(n_jobs, backend=backend, return_as=return_as_2)(
+        delayed(sqrt)(i ** 2) for i in range(10, 20)
+    )
+
+    if return_as_1 == "generator_unordered":
+        g = sorted(g)
+
+    if return_as_2 == "generator_unordered":
+        g2 = sorted(g2)
+
+    assert all(res == i for res, i in zip(g, range(10)))
+    assert all(res == i for res, i in zip(g2, range(10, 20)))
+
+
+@parametrize('backend, error', [
+    ('loky', True),
+    ('threading', False),
+    ('sequential', False),
+])
+@parametrize('return_as_1', ["generator", "generator_unordered"])
+@parametrize('return_as_2', ["generator", "generator_unordered"])
+def test_multiple_generator_call_separated_gc(
+        backend, return_as_1, return_as_2, error
+):
+
+    if (backend == 'loky') and (mp is None):
+        pytest.skip("Requires multiprocessing")
+
+    # Check that in loky, only one call can be run at a time with
+    # a single executor.
+    parallel = Parallel(2, backend=backend, return_as=return_as_1)
+    g = parallel(delayed(sleep)(10) for i in range(10))
+    g_wr = weakref.finalize(g, lambda: print("Generator collected"))
+    ctx = (
+        raises(RuntimeError, match="The executor underlying Parallel")
+        if error else nullcontext()
+    )
+    with ctx:
+        # For loky, this call will raise an error as the gc of the previous
+        # generator will shutdown the shared executor.
+        # For the other backends, as the worker pools are not shared between
+        # the two calls, this should proceed correctly.
+        t_start = time.time()
+        g = Parallel(2, backend=backend, return_as=return_as_2)(
+            delayed(sqrt)(i ** 2) for i in range(10, 20)
+        )
+
+        # The gc in pypy can be delayed. Force it to test the behavior when it
+        # will eventually be collected.
+        force_gc_pypy()
+
+        if return_as_2 == "generator_unordered":
+            g = sorted(g)
+
+        assert all(res == i for res, i in zip(g, range(10, 20)))
+
+    assert time.time() - t_start < 5
+
+    # Make sure that the computation are stopped for the gc'ed generator
+    retry = 0
+    while g_wr.alive and retry < 3:
+        retry += 1
+        time.sleep(.5)
+    assert time.time() - t_start < 5
+
+    if parallel._effective_n_jobs() != 1:
+        # check that the first parallel object is aborting (the final _aborted
+        # state might be delayed).
+        assert parallel._aborting
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_memmapping_leaks(backend, tmpdir):
+    # Non-regression test for memmapping backends. Ensure that the data
+    # does not stay too long in memory
+    tmpdir = tmpdir.strpath
+
+    # Use max_nbytes=1 to force the use of memory-mapping even for small
+    # arrays
+    with Parallel(n_jobs=2, max_nbytes=1, backend=backend,
+                  temp_folder=tmpdir) as p:
+        p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+
+        # The memmap folder should not be clean in the context scope
+        assert len(os.listdir(tmpdir)) > 0
+
+        # Cleaning of the memmap folder is triggered by the garbage
+        # collection. With pypy the garbage collection has been observed to be
+        # delayed, sometimes up until the shutdown of the interpreter. This
+        # cleanup job executed in the worker ensures that it's triggered
+        # immediately.
+        p(delayed(_cleanup_worker)() for _ in range(2))
+
+    # Make sure that the shared memory is cleaned at the end when we exit
+    # the context
+    for _ in range(100):
+        if not os.listdir(tmpdir):
+            break
+        sleep(.1)
+    else:
+        raise AssertionError('temporary directory of Parallel was not removed')
+
+    # Make sure that the shared memory is cleaned at the end of a call
+    p = Parallel(n_jobs=2, max_nbytes=1, backend=backend)
+    p(delayed(check_memmap)(a) for a in [np.random.random(10)] * 2)
+    p(delayed(_cleanup_worker)() for _ in range(2))
+
+    for _ in range(100):
+        if not os.listdir(tmpdir):
+            break
+        sleep(.1)
+    else:
+        raise AssertionError('temporary directory of Parallel was not removed')
+
+
+@parametrize('backend',
+             ([None, 'threading'] if mp is None
+              else [None, 'loky', 'threading'])
+             )
+def test_lambda_expression(backend):
+    # cloudpickle is used to pickle delayed callables
+    results = Parallel(n_jobs=2, backend=backend)(
+        delayed(lambda x: x ** 2)(i) for i in range(10))
+    assert results == [i ** 2 for i in range(10)]
+
+
+@with_multiprocessing
+@parametrize('backend', PROCESS_BACKENDS)
+def test_backend_batch_statistics_reset(backend):
+    """Test that a parallel backend correctly resets its batch statistics."""
+    n_jobs = 2
+    n_inputs = 500
+    task_time = 2. / n_inputs
+
+    p = Parallel(verbose=10, n_jobs=n_jobs, backend=backend)
+    p(delayed(time.sleep)(task_time) for i in range(n_inputs))
+    assert (p._backend._effective_batch_size ==
+            p._backend._DEFAULT_EFFECTIVE_BATCH_SIZE)
+    assert (p._backend._smoothed_batch_duration ==
+            p._backend._DEFAULT_SMOOTHED_BATCH_DURATION)
+
+    p(delayed(time.sleep)(task_time) for i in range(n_inputs))
+    assert (p._backend._effective_batch_size ==
+            p._backend._DEFAULT_EFFECTIVE_BATCH_SIZE)
+    assert (p._backend._smoothed_batch_duration ==
+            p._backend._DEFAULT_SMOOTHED_BATCH_DURATION)
+
+
+@with_multiprocessing
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_hinting_and_constraints(context):
+    for n_jobs in [1, 2, -1]:
+        assert type(Parallel(n_jobs=n_jobs)._backend) == DefaultBackend
+
+        p = Parallel(n_jobs=n_jobs, prefer='threads')
+        assert type(p._backend) is ThreadingBackend
+
+        p = Parallel(n_jobs=n_jobs, prefer='processes')
+        assert type(p._backend) is DefaultBackend
+
+        p = Parallel(n_jobs=n_jobs, require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+
+    # Explicit backend selection can override backend hinting although it
+    # is useless to pass a hint when selecting a backend.
+    p = Parallel(n_jobs=2, backend='loky', prefer='threads')
+    assert type(p._backend) is LokyBackend
+
+    with context('loky', n_jobs=2):
+        # Explicit backend selection by the user with the context manager
+        # should be respected when combined with backend hints only.
+        p = Parallel(prefer='threads')
+        assert type(p._backend) is LokyBackend
+        assert p.n_jobs == 2
+
+    with context('loky', n_jobs=2):
+        # Locally hard-coded n_jobs value is respected.
+        p = Parallel(n_jobs=3, prefer='threads')
+        assert type(p._backend) is LokyBackend
+        assert p.n_jobs == 3
+
+    with context('loky', n_jobs=2):
+        # Explicit backend selection by the user with the context manager
+        # should be ignored when the Parallel call has hard constraints.
+        # In this case, the default backend that supports shared mem is
+        # used an the default number of processes is used.
+        p = Parallel(require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+        assert p.n_jobs == 1
+
+    with context('loky', n_jobs=2):
+        p = Parallel(n_jobs=3, require='sharedmem')
+        assert type(p._backend) is ThreadingBackend
+        assert p.n_jobs == 3
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_backend_hinting_and_constraints_with_custom_backends(
+    capsys, context
+):
+    # Custom backends can declare that they use threads and have shared memory
+    # semantics:
+    class MyCustomThreadingBackend(ParallelBackendBase):
+        supports_sharedmem = True
+        use_threads = True
+
+        def apply_async(self):
+            pass
+
+        def effective_n_jobs(self, n_jobs):
+            return n_jobs
+
+    with context(MyCustomThreadingBackend()):
+        p = Parallel(n_jobs=2, prefer='processes')  # ignored
+        assert type(p._backend) is MyCustomThreadingBackend
+
+        p = Parallel(n_jobs=2, require='sharedmem')
+        assert type(p._backend) is MyCustomThreadingBackend
+
+    class MyCustomProcessingBackend(ParallelBackendBase):
+        supports_sharedmem = False
+        use_threads = False
+
+        def apply_async(self):
+            pass
+
+        def effective_n_jobs(self, n_jobs):
+            return n_jobs
+
+    with context(MyCustomProcessingBackend()):
+        p = Parallel(n_jobs=2, prefer='processes')
+        assert type(p._backend) is MyCustomProcessingBackend
+
+        out, err = capsys.readouterr()
+        assert out == ""
+        assert err == ""
+
+        p = Parallel(n_jobs=2, require='sharedmem', verbose=10)
+        assert type(p._backend) is ThreadingBackend
+
+        out, err = capsys.readouterr()
+        expected = ("Using ThreadingBackend as joblib backend "
+                    "instead of MyCustomProcessingBackend as the latter "
+                    "does not provide shared memory semantics.")
+        assert out.strip() == expected
+        assert err == ""
+
+    with raises(ValueError):
+        Parallel(backend=MyCustomProcessingBackend(), require='sharedmem')
+
+
+def test_invalid_backend_hinting_and_constraints():
+    with raises(ValueError):
+        Parallel(prefer='invalid')
+
+    with raises(ValueError):
+        Parallel(require='invalid')
+
+    with raises(ValueError):
+        # It is inconsistent to prefer process-based parallelism while
+        # requiring shared memory semantics.
+        Parallel(prefer='processes', require='sharedmem')
+
+    if mp is not None:
+        # It is inconsistent to ask explicitly for a process-based
+        # parallelism while requiring shared memory semantics.
+        with raises(ValueError):
+            Parallel(backend='loky', require='sharedmem')
+        with raises(ValueError):
+            Parallel(backend='multiprocessing', require='sharedmem')
+
+
+def _recursive_backend_info(limit=3, **kwargs):
+    """Perform nested parallel calls and introspect the backend on the way"""
+
+    with Parallel(n_jobs=2) as p:
+        this_level = [(type(p._backend).__name__, p._backend.nesting_level)]
+        if limit == 0:
+            return this_level
+        results = p(delayed(_recursive_backend_info)(limit=limit - 1, **kwargs)
+                    for i in range(1))
+        return this_level + results[0]
+
+
+@with_multiprocessing
+@parametrize('backend', ['loky', 'threading'])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_nested_parallelism_limit(context, backend):
+    with context(backend, n_jobs=2):
+        backend_types_and_levels = _recursive_backend_info()
+
+    if cpu_count() == 1:
+        second_level_backend_type = 'SequentialBackend'
+        max_level = 1
+    else:
+        second_level_backend_type = 'ThreadingBackend'
+        max_level = 2
+
+    top_level_backend_type = backend.title() + 'Backend'
+    expected_types_and_levels = [
+        (top_level_backend_type, 0),
+        (second_level_backend_type, 1),
+        ('SequentialBackend', max_level),
+        ('SequentialBackend', max_level)
+    ]
+    assert backend_types_and_levels == expected_types_and_levels
+
+
+@with_numpy
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is None, reason='This test requires dask')
+def test_nested_parallelism_with_dask(context):
+    with distributed.Client(n_workers=2, threads_per_worker=2):
+        # 10 MB of data as argument to trigger implicit scattering
+        data = np.ones(int(1e7), dtype=np.uint8)
+        for i in range(2):
+            with context('dask'):
+                backend_types_and_levels = _recursive_backend_info(data=data)
+            assert len(backend_types_and_levels) == 4
+            assert all(name == 'DaskDistributedBackend'
+                       for name, _ in backend_types_and_levels)
+
+        # No argument
+        with context('dask'):
+            backend_types_and_levels = _recursive_backend_info()
+        assert len(backend_types_and_levels) == 4
+        assert all(name == 'DaskDistributedBackend'
+                   for name, _ in backend_types_and_levels)
+
+
+def _recursive_parallel(nesting_limit=None):
+    """A horrible function that does recursive parallel calls"""
+    return Parallel()(delayed(_recursive_parallel)() for i in range(2))
+
+
+@pytest.mark.no_cover
+@parametrize("context", [parallel_config, parallel_backend])
+@parametrize(
+    'backend', (['threading'] if mp is None else ['loky', 'threading'])
+)
+def test_thread_bomb_mitigation(context, backend):
+    # Test that recursive parallelism raises a recursion rather than
+    # saturating the operating system resources by creating a unbounded number
+    # of threads.
+    with context(backend, n_jobs=2):
+        with raises(BaseException) as excinfo:
+            _recursive_parallel()
+    exc = excinfo.value
+    if backend == "loky":
+        # Local import because loky may not be importable for lack of
+        # multiprocessing
+        from joblib.externals.loky.process_executor import TerminatedWorkerError # noqa
+        if isinstance(exc, (TerminatedWorkerError, PicklingError)):
+            # The recursion exception can itself cause an error when
+            # pickling it to be send back to the parent process. In this
+            # case the worker crashes but the original traceback is still
+            # printed on stderr. This could be improved but does not seem
+            # simple to do and this is not critical for users (as long
+            # as there is no process or thread bomb happening).
+            pytest.xfail("Loky worker crash when serializing RecursionError")
+
+    assert isinstance(exc, RecursionError)
+
+
+def _run_parallel_sum():
+    env_vars = {}
+    for var in ['OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS',
+                'VECLIB_MAXIMUM_THREADS', 'NUMEXPR_NUM_THREADS',
+                'NUMBA_NUM_THREADS', 'ENABLE_IPC']:
+        env_vars[var] = os.environ.get(var)
+    return env_vars, parallel_sum(100)
+
+
+@parametrize("backend", ([None, 'loky'] if mp is not None else [None]))
+@skipif(parallel_sum is None, reason="Need OpenMP helper compiled")
+def test_parallel_thread_limit(backend):
+    results = Parallel(n_jobs=2, backend=backend)(
+        delayed(_run_parallel_sum)() for _ in range(2)
+    )
+    expected_num_threads = max(cpu_count() // 2, 1)
+    for worker_env_vars, omp_num_threads in results:
+        assert omp_num_threads == expected_num_threads
+        for name, value in worker_env_vars.items():
+            if name.endswith("_THREADS"):
+                assert value == str(expected_num_threads)
+            else:
+                assert name == "ENABLE_IPC"
+                assert value == "1"
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+@skipif(distributed is not None, reason='This test requires dask')
+def test_dask_backend_when_dask_not_installed(context):
+    with raises(ValueError, match='Please install dask'):
+        context('dask')
+
+
+@parametrize("context", [parallel_config, parallel_backend])
+def test_zero_worker_backend(context):
+    # joblib.Parallel should reject with an explicit error message parallel
+    # backends that have no worker.
+    class ZeroWorkerBackend(ThreadingBackend):
+        def configure(self, *args, **kwargs):
+            return 0
+
+        def apply_async(self, func, callback=None):   # pragma: no cover
+            raise TimeoutError("No worker available")
+
+        def effective_n_jobs(self, n_jobs):   # pragma: no cover
+            return 0
+
+    expected_msg = "ZeroWorkerBackend has no active worker"
+    with context(ZeroWorkerBackend()):
+        with pytest.raises(RuntimeError, match=expected_msg):
+            Parallel(n_jobs=2)(delayed(id)(i) for i in range(2))
+
+
+def test_globals_update_at_each_parallel_call():
+    # This is a non-regression test related to joblib issues #836 and #833.
+    # Cloudpickle versions between 0.5.4 and 0.7 introduced a bug where global
+    # variables changes in a parent process between two calls to
+    # joblib.Parallel would not be propagated into the workers.
+    global MY_GLOBAL_VARIABLE
+    MY_GLOBAL_VARIABLE = "original value"
+
+    def check_globals():
+        global MY_GLOBAL_VARIABLE
+        return MY_GLOBAL_VARIABLE
+
+    assert check_globals() == "original value"
+
+    workers_global_variable = Parallel(n_jobs=2)(
+        delayed(check_globals)() for i in range(2))
+    assert set(workers_global_variable) == {"original value"}
+
+    # Change the value of MY_GLOBAL_VARIABLE, and make sure this change gets
+    # propagated into the workers environment
+    MY_GLOBAL_VARIABLE = "changed value"
+    assert check_globals() == "changed value"
+
+    workers_global_variable = Parallel(n_jobs=2)(
+        delayed(check_globals)() for i in range(2))
+    assert set(workers_global_variable) == {"changed value"}
+
+
+##############################################################################
+# Test environment variable in child env, in particular for limiting
+# the maximal number of threads in C-library threadpools.
+#
+
+def _check_numpy_threadpool_limits():
+    import numpy as np
+    # Let's call BLAS on a Matrix Matrix multiplication with dimensions large
+    # enough to ensure that the threadpool managed by the underlying BLAS
+    # implementation is actually used so as to force its initialization.
+    a = np.random.randn(100, 100)
+    np.dot(a, a)
+    from threadpoolctl import threadpool_info
+    return threadpool_info()
+
+
+def _parent_max_num_threads_for(child_module, parent_info):
+    for parent_module in parent_info:
+        if parent_module['filepath'] == child_module['filepath']:
+            return parent_module['num_threads']
+    raise ValueError("An unexpected module was loaded in child:\n{}"
+                     .format(child_module))
+
+
+def check_child_num_threads(workers_info, parent_info, num_threads):
+    # Check that the number of threads reported in workers_info is consistent
+    # with the expectation. We need to be careful to handle the cases where
+    # the requested number of threads is below max_num_thread for the library.
+    for child_threadpool_info in workers_info:
+        for child_module in child_threadpool_info:
+            parent_max_num_threads = _parent_max_num_threads_for(
+                child_module, parent_info)
+            expected = {min(num_threads, parent_max_num_threads), num_threads}
+            assert child_module['num_threads'] in expected
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('n_jobs', [2, 4, -2, -1])
+def test_threadpool_limitation_in_child_loky(n_jobs):
+    # Check that the protection against oversubscription in workers is working
+    # using threadpoolctl functionalities.
+
+    # Skip this test if numpy is not linked to a BLAS library
+    parent_info = _check_numpy_threadpool_limits()
+    if len(parent_info) == 0:
+        pytest.skip(reason="Need a version of numpy linked to BLAS")
+
+    workers_threadpool_infos = Parallel(backend="loky", n_jobs=n_jobs)(
+        delayed(_check_numpy_threadpool_limits)() for i in range(2))
+
+    n_jobs = effective_n_jobs(n_jobs)
+    expected_child_num_threads = max(cpu_count() // n_jobs, 1)
+
+    check_child_num_threads(workers_threadpool_infos, parent_info,
+                            expected_child_num_threads)
+
+
+@with_numpy
+@with_multiprocessing
+@parametrize('inner_max_num_threads', [1, 2, 4, None])
+@parametrize('n_jobs', [2, -1])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_context(
+    context, n_jobs, inner_max_num_threads
+):
+    # Check that the protection against oversubscription in workers is working
+    # using threadpoolctl functionalities.
+
+    # Skip this test if numpy is not linked to a BLAS library
+    parent_info = _check_numpy_threadpool_limits()
+    if len(parent_info) == 0:
+        pytest.skip(reason="Need a version of numpy linked to BLAS")
+
+    with context('loky', inner_max_num_threads=inner_max_num_threads):
+        workers_threadpool_infos = Parallel(n_jobs=n_jobs)(
+            delayed(_check_numpy_threadpool_limits)() for i in range(2))
+
+    n_jobs = effective_n_jobs(n_jobs)
+    if inner_max_num_threads is None:
+        expected_child_num_threads = max(cpu_count() // n_jobs, 1)
+    else:
+        expected_child_num_threads = inner_max_num_threads
+
+    check_child_num_threads(workers_threadpool_infos, parent_info,
+                            expected_child_num_threads)
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, -1])
+@parametrize('var_name', ["OPENBLAS_NUM_THREADS",
+                          "MKL_NUM_THREADS",
+                          "OMP_NUM_THREADS"])
+@parametrize("context", [parallel_config, parallel_backend])
+def test_threadpool_limitation_in_child_override(context, n_jobs, var_name):
+    # Check that environment variables set by the user on the main process
+    # always have the priority.
+
+    # Clean up the existing executor because we change the environment of the
+    # parent at runtime and it is not detected in loky intentionally.
+    get_reusable_executor(reuse=True).shutdown()
+
+    def _get_env(var_name):
+        return os.environ.get(var_name)
+
+    original_var_value = os.environ.get(var_name)
+    try:
+        os.environ[var_name] = "4"
+        # Skip this test if numpy is not linked to a BLAS library
+        results = Parallel(n_jobs=n_jobs)(
+            delayed(_get_env)(var_name) for i in range(2))
+        assert results == ["4", "4"]
+
+        with context('loky', inner_max_num_threads=1):
+            results = Parallel(n_jobs=n_jobs)(
+                delayed(_get_env)(var_name) for i in range(2))
+        assert results == ["1", "1"]
+
+    finally:
+        if original_var_value is None:
+            del os.environ[var_name]
+        else:
+            os.environ[var_name] = original_var_value
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, 4, -1])
+def test_loky_reuse_workers(n_jobs):
+    # Non-regression test for issue #967 where the workers are not reused when
+    # calling multiple Parallel loops.
+
+    def parallel_call(n_jobs):
+        x = range(10)
+        Parallel(n_jobs=n_jobs)(delayed(sum)(x) for i in range(10))
+
+    # Run a parallel loop and get the workers used for computations
+    parallel_call(n_jobs)
+    first_executor = get_reusable_executor(reuse=True)
+
+    # Ensure that the workers are reused for the next calls, as the executor is
+    # not restarted.
+    for _ in range(10):
+        parallel_call(n_jobs)
+        executor = get_reusable_executor(reuse=True)
+        assert executor == first_executor

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/test_store_backends.py

@@ -0,0 +1,94 @@
+
+try:
+    # Python 2.7: use the C pickle to speed up
+    # test_concurrency_safe_write which pickles big python objects
+    import cPickle as cpickle
+except ImportError:
+    import pickle as cpickle
+import functools
+from pickle import PicklingError
+import time
+
+import pytest
+
+from joblib.testing import parametrize, timeout
+from joblib.test.common import with_multiprocessing
+from joblib.backports import concurrency_safe_rename
+from joblib import Parallel, delayed
+from joblib._store_backends import (
+    concurrency_safe_write,
+    FileSystemStoreBackend,
+    CacheWarning,
+)
+
+
+def write_func(output, filename):
+    with open(filename, 'wb') as f:
+        cpickle.dump(output, f)
+
+
+def load_func(expected, filename):
+    for i in range(10):
+        try:
+            with open(filename, 'rb') as f:
+                reloaded = cpickle.load(f)
+            break
+        except (OSError, IOError):
+            # On Windows you can have WindowsError ([Error 5] Access
+            # is denied or [Error 13] Permission denied) when reading the file,
+            # probably because a writer process has a lock on the file
+            time.sleep(0.1)
+    else:
+        raise
+    assert expected == reloaded
+
+
+def concurrency_safe_write_rename(to_write, filename, write_func):
+    temporary_filename = concurrency_safe_write(to_write,
+                                                filename, write_func)
+    concurrency_safe_rename(temporary_filename, filename)
+
+
+@timeout(0)  # No timeout as this test can be long
+@with_multiprocessing
+@parametrize('backend', ['multiprocessing', 'loky', 'threading'])
+def test_concurrency_safe_write(tmpdir, backend):
+    # Add one item to cache
+    filename = tmpdir.join('test.pkl').strpath
+
+    obj = {str(i): i for i in range(int(1e5))}
+    funcs = [functools.partial(concurrency_safe_write_rename,
+                               write_func=write_func)
+             if i % 3 != 2 else load_func for i in range(12)]
+    Parallel(n_jobs=2, backend=backend)(
+        delayed(func)(obj, filename) for func in funcs)
+
+
+def test_warning_on_dump_failure(tmpdir):
+    # Check that a warning is raised when the dump fails for any reason but
+    # a PicklingError.
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise RuntimeError("some exception")
+
+    backend = FileSystemStoreBackend()
+    backend.location = tmpdir.join('test_warning_on_pickling_error').strpath
+    backend.compress = None
+
+    with pytest.warns(CacheWarning, match="some exception"):
+        backend.dump_item("testpath", UnpicklableObject())
+
+
+def test_warning_on_pickling_error(tmpdir):
+    # This is separate from test_warning_on_dump_failure because in the
+    # future we will turn this into an exception.
+    class UnpicklableObject(object):
+        def __reduce__(self):
+            raise PicklingError("not picklable")
+
+    backend = FileSystemStoreBackend()
+    backend.location = tmpdir.join('test_warning_on_pickling_error').strpath
+    backend.compress = None
+
+    with pytest.warns(FutureWarning, match="not picklable"):
+        backend.dump_item("testpath", UnpicklableObject())

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/joblib/test/testutils.py

@@ -0,0 +1,8 @@
+def return_slice_of_data(arr, start_idx, end_idx):
+    return arr[start_idx:end_idx]
+
+
+def print_filename_and_raise(arr):
+    from joblib._memmapping_reducer import _get_backing_memmap
+    print(_get_backing_memmap(arr).filename)
+    raise ValueError

material/dataset/xpsdeeplearning/.venv/lib/python3.10/site-packages/jupyter_console-6.6.3.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip