venv/lib/python3.11/site-packages/joblib/memory.py

"""
A context object for caching a function's return value each time it
is called with the same input arguments.

"""

# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2009 Gael Varoquaux
# License: BSD Style, 3 clauses.

import asyncio
import datetime
import functools
import inspect
import logging
import os
import pathlib
import pydoc
import re
import textwrap
import time
import tokenize
import traceback
import warnings
import weakref

from . import hashing
from ._store_backends import (
    CacheWarning,  # noqa
    FileSystemStoreBackend,
    StoreBackendBase,
)
from .func_inspect import (
    filter_args,
    format_call,
    format_signature,
    get_func_code,
    get_func_name,
)
from .logger import Logger, format_time, pformat

FIRST_LINE_TEXT = "# first line:"

# TODO: The following object should have a data store object as a sub
# object, and the interface to persist and query should be separated in
# the data store.
#
# This would enable creating 'Memory' objects with a different logic for
# pickling that would simply span a MemorizedFunc with the same
# store (or do we want to copy it to avoid cross-talks?), for instance to
# implement HDF5 pickling.

# TODO: Same remark for the logger, and probably use the Python logging
# mechanism.


def extract_first_line(func_code):
    """Extract the first line information from the function code
    text if available.
    """
    if func_code.startswith(FIRST_LINE_TEXT):
        func_code = func_code.split("\n")
        first_line = int(func_code[0][len(FIRST_LINE_TEXT) :])
        func_code = "\n".join(func_code[1:])
    else:
        first_line = -1
    return func_code, first_line


class JobLibCollisionWarning(UserWarning):
    """Warn that there might be a collision between names of functions."""


_STORE_BACKENDS = {"local": FileSystemStoreBackend}


def register_store_backend(backend_name, backend):
    """Extend available store backends.

    The Memory, MemorizeResult and MemorizeFunc objects are designed to be
    agnostic to the type of store used behind. By default, the local file
    system is used but this function gives the possibility to extend joblib's
    memory pattern with other types of storage such as cloud storage (S3, GCS,
    OpenStack, HadoopFS, etc) or blob DBs.

    Parameters
    ----------
    backend_name: str
        The name identifying the store backend being registered. For example,
        'local' is used with FileSystemStoreBackend.
    backend: StoreBackendBase subclass
        The name of a class that implements the StoreBackendBase interface.

    """
    if not isinstance(backend_name, str):
        raise ValueError(
            "Store backend name should be a string, '{0}' given.".format(backend_name)
        )
    if backend is None or not issubclass(backend, StoreBackendBase):
        raise ValueError(
            "Store backend should inherit StoreBackendBase, '{0}' given.".format(
                backend
            )
        )

    _STORE_BACKENDS[backend_name] = backend


def _store_backend_factory(backend, location, verbose=0, backend_options=None):
    """Return the correct store object for the given location."""
    if backend_options is None:
        backend_options = {}

    if isinstance(location, pathlib.Path):
        location = str(location)

    if isinstance(location, StoreBackendBase):
        return location
    elif isinstance(location, str):
        obj = None
        location = os.path.expanduser(location)
        # The location is not a local file system, we look in the
        # registered backends if there's one matching the given backend
        # name.
        for backend_key, backend_obj in _STORE_BACKENDS.items():
            if backend == backend_key:
                obj = backend_obj()

        # By default, we assume the FileSystemStoreBackend can be used if no
        # matching backend could be found.
        if obj is None:
            raise TypeError(
                "Unknown location {0} or backend {1}".format(location, backend)
            )

        # The store backend is configured with the extra named parameters,
        # some of them are specific to the underlying store backend.
        obj.configure(location, verbose=verbose, backend_options=backend_options)
        return obj
    elif location is not None:
        warnings.warn(
            "Instantiating a backend using a {} as a location is not "
            "supported by joblib. Returning None instead.".format(
                location.__class__.__name__
            ),
            UserWarning,
        )

    return None


def _build_func_identifier(func):
    """Build a roughly unique identifier for the cached function."""
    modules, funcname = get_func_name(func)
    # We reuse historical fs-like way of building a function identifier
    return os.path.join(*modules, funcname)


# An in-memory store to avoid looking at the disk-based function
# source code to check if a function definition has changed
_FUNCTION_HASHES = weakref.WeakKeyDictionary()


###############################################################################
# class `MemorizedResult`
###############################################################################
class MemorizedResult(Logger):
    """Object representing a cached value.

    Attributes
    ----------
    location: str
        The location of joblib cache. Depends on the store backend used.

    func: function or str
        function whose output is cached. The string case is intended only for
        instantiation based on the output of repr() on another instance.
        (namely eval(repr(memorized_instance)) works).

    argument_hash: str
        hash of the function arguments.

    backend: str
        Type of store backend for reading/writing cache files.
        Default is 'local'.

    mmap_mode: {None, 'r+', 'r', 'w+', 'c'}
        The memmapping mode used when loading from cache numpy arrays. See
        numpy.load for the meaning of the different values.

    verbose: int
        verbosity level (0 means no message).

    timestamp, metadata: string
        for internal use only.
    """

    def __init__(
        self,
        location,
        call_id,
        backend="local",
        mmap_mode=None,
        verbose=0,
        timestamp=None,
        metadata=None,
    ):
        Logger.__init__(self)
        self._call_id = call_id
        self.store_backend = _store_backend_factory(backend, location, verbose=verbose)
        self.mmap_mode = mmap_mode

        if metadata is not None:
            self.metadata = metadata
        else:
            self.metadata = self.store_backend.get_metadata(self._call_id)

        self.duration = self.metadata.get("duration", None)
        self.verbose = verbose
        self.timestamp = timestamp

    @property
    def func(self):
        return self.func_id

    @property
    def func_id(self):
        return self._call_id[0]

    @property
    def args_id(self):
        return self._call_id[1]

    def get(self):
        """Read value from cache and return it."""
        try:
            return self.store_backend.load_item(
                self._call_id,
                timestamp=self.timestamp,
                metadata=self.metadata,
                verbose=self.verbose,
            )
        except ValueError as exc:
            new_exc = KeyError(
                "Error while trying to load a MemorizedResult's value. "
                "It seems that this folder is corrupted : {}".format(
                    os.path.join(self.store_backend.location, *self._call_id)
                )
            )
            raise new_exc from exc

    def clear(self):
        """Clear value from cache"""
        self.store_backend.clear_item(self._call_id)

    def __repr__(self):
        return '{}(location="{}", func="{}", args_id="{}")'.format(
            self.__class__.__name__, self.store_backend.location, *self._call_id
        )

    def __getstate__(self):
        state = self.__dict__.copy()
        state["timestamp"] = None
        return state


class NotMemorizedResult(object):
    """Class representing an arbitrary value.

    This class is a replacement for MemorizedResult when there is no cache.
    """

    __slots__ = ("value", "valid")

    def __init__(self, value):
        self.value = value
        self.valid = True

    def get(self):
        if self.valid:
            return self.value
        else:
            raise KeyError("No value stored.")

    def clear(self):
        self.valid = False
        self.value = None

    def __repr__(self):
        if self.valid:
            return "{class_name}({value})".format(
                class_name=self.__class__.__name__, value=pformat(self.value)
            )
        else:
            return self.__class__.__name__ + " with no value"

    # __getstate__ and __setstate__ are required because of __slots__
    def __getstate__(self):
        return {"valid": self.valid, "value": self.value}

    def __setstate__(self, state):
        self.valid = state["valid"]
        self.value = state["value"]


###############################################################################
# class `NotMemorizedFunc`
###############################################################################
class NotMemorizedFunc(object):
    """No-op object decorating a function.

    This class replaces MemorizedFunc when there is no cache. It provides an
    identical API but does not write anything on disk.

    Attributes
    ----------
    func: callable
        Original undecorated function.
    """

    # Should be a light as possible (for speed)
    def __init__(self, func):
        self.func = func

    def __call__(self, *args, **kwargs):
        return self.func(*args, **kwargs)

    def call_and_shelve(self, *args, **kwargs):
        return NotMemorizedResult(self.func(*args, **kwargs))

    def __repr__(self):
        return "{0}(func={1})".format(self.__class__.__name__, self.func)

    def clear(self, warn=True):
        # Argument "warn" is for compatibility with MemorizedFunc.clear
        pass

    def call(self, *args, **kwargs):
        return self.func(*args, **kwargs), {}

    def check_call_in_cache(self, *args, **kwargs):
        return False


###############################################################################
# class `AsyncNotMemorizedFunc`
###############################################################################
class AsyncNotMemorizedFunc(NotMemorizedFunc):
    async def call_and_shelve(self, *args, **kwargs):
        return NotMemorizedResult(await self.func(*args, **kwargs))


###############################################################################
# class `MemorizedFunc`
###############################################################################
class MemorizedFunc(Logger):
    """Callable object decorating a function for caching its return value
    each time it is called.

    Methods are provided to inspect the cache or clean it.

    Attributes
    ----------
    func: callable
        The original, undecorated, function.

    location: string
        The location of joblib cache. Depends on the store backend used.

    backend: str
        Type of store backend for reading/writing cache files.
        Default is 'local', in which case the location is the path to a
        disk storage.

    ignore: list or None
        List of variable names to ignore when choosing whether to
        recompute.

    mmap_mode: {None, 'r+', 'r', 'w+', 'c'}
        The memmapping mode used when loading from cache
        numpy arrays. See numpy.load for the meaning of the different
        values.

    compress: boolean, or integer
        Whether to zip the stored data on disk. If an integer is
        given, it should be between 1 and 9, and sets the amount
        of compression. Note that compressed arrays cannot be
        read by memmapping.

    verbose: int, optional
        The verbosity flag, controls messages that are issued as
        the function is evaluated.

    cache_validation_callback: callable, optional
        Callable to check if a result in cache is valid or is to be recomputed.
        When the function is called with arguments for which a cache exists,
        the callback is called with the cache entry's metadata as its sole
        argument. If it returns True, the cached result is returned, else the
        cache for these arguments is cleared and the result is recomputed.
    """

    # ------------------------------------------------------------------------
    # Public interface
    # ------------------------------------------------------------------------

    def __init__(
        self,
        func,
        location,
        backend="local",
        ignore=None,
        mmap_mode=None,
        compress=False,
        verbose=1,
        timestamp=None,
        cache_validation_callback=None,
    ):
        Logger.__init__(self)
        self.mmap_mode = mmap_mode
        self.compress = compress
        self.func = func
        self.cache_validation_callback = cache_validation_callback
        self.func_id = _build_func_identifier(func)
        self.ignore = ignore if ignore is not None else []
        self._verbose = verbose

        # retrieve store object from backend type and location.
        self.store_backend = _store_backend_factory(
            backend,
            location,
            verbose=verbose,
            backend_options=dict(compress=compress, mmap_mode=mmap_mode),
        )
        if self.store_backend is not None:
            # Create func directory on demand.
            self.store_backend.store_cached_func_code([self.func_id])

        self.timestamp = timestamp if timestamp is not None else time.time()
        try:
            functools.update_wrapper(self, func)
        except Exception:
            pass  # Objects like ufunc don't like that
        if inspect.isfunction(func):
            doc = pydoc.TextDoc().document(func)
            # Remove blank line
            doc = doc.replace("\n", "\n\n", 1)
            # Strip backspace-overprints for compatibility with autodoc
            doc = re.sub("\x08.", "", doc)
        else:
            # Pydoc does a poor job on other objects
            doc = func.__doc__
        self.__doc__ = "Memoized version of %s" % doc

        self._func_code_info = None
        self._func_code_id = None

    def _is_in_cache_and_valid(self, call_id):
        """Check if the function call is cached and valid for given arguments.

        - Compare the function code with the one from the cached function,
        asserting if it has changed.
        - Check if the function call is present in the cache.
        - Call `cache_validation_callback` for user define cache validation.

        Returns True if the function call is in cache and can be used, and
        returns False otherwise.
        """
        # Check if the code of the function has changed
        if not self._check_previous_func_code(stacklevel=4):
            return False

        # Check if this specific call is in the cache
        if not self.store_backend.contains_item(call_id):
            return False

        # Call the user defined cache validation callback
        metadata = self.store_backend.get_metadata(call_id)
        if (
            self.cache_validation_callback is not None
            and not self.cache_validation_callback(metadata)
        ):
            self.store_backend.clear_item(call_id)
            return False

        return True

    def _cached_call(self, args, kwargs, shelving):
        """Call wrapped function and cache result, or read cache if available.

        This function returns the wrapped function output or a reference to
        the cached result.

        Arguments:
        ----------

        args, kwargs: list and dict
            input arguments for wrapped function

        shelving: bool
            True when called via the call_and_shelve function.


        Returns
        -------
        output: Output of the wrapped function if shelving is false, or a
            MemorizedResult reference to the value if shelving is true.
        metadata: dict containing the metadata associated with the call.
        """
        args_id = self._get_args_id(*args, **kwargs)
        call_id = (self.func_id, args_id)
        _, func_name = get_func_name(self.func)
        func_info = self.store_backend.get_cached_func_info([self.func_id])
        location = func_info["location"]

        if self._verbose >= 20:
            logging.basicConfig(level=logging.INFO)
            _, signature = format_signature(self.func, *args, **kwargs)
            self.info(
                textwrap.dedent(
                    f"""
                        Querying {func_name} with signature
                        {signature}.

                        (argument hash {args_id})

                        The store location is {location}.
                        """
                )
            )

        # Compare the function code with the previous to see if the
        # function code has changed and check if the results are present in
        # the cache.
        if self._is_in_cache_and_valid(call_id):
            if shelving:
                return self._get_memorized_result(call_id), {}

            try:
                start_time = time.time()
                output = self._load_item(call_id)
                if self._verbose > 4:
                    self._print_duration(
                        time.time() - start_time, context="cache loaded "
                    )
                return output, {}
            except Exception:
                # XXX: Should use an exception logger
                _, signature = format_signature(self.func, *args, **kwargs)
                self.warn(
                    "Exception while loading results for {}\n {}".format(
                        signature, traceback.format_exc()
                    )
                )

        if self._verbose > 10:
            self.warn(
                f"Computing func {func_name}, argument hash {args_id} "
                f"in location {location}"
            )

        # Returns the output but not the metadata
        return self._call(call_id, args, kwargs, shelving)

    @property
    def func_code_info(self):
        # 3-tuple property containing: the function source code, source file,
        # and first line of the code inside the source file
        if hasattr(self.func, "__code__"):
            if self._func_code_id is None:
                self._func_code_id = id(self.func.__code__)
            elif id(self.func.__code__) != self._func_code_id:
                # Be robust to dynamic reassignments of self.func.__code__
                self._func_code_info = None

        if self._func_code_info is None:
            # Cache the source code of self.func . Provided that get_func_code
            # (which should be called once on self) gets called in the process
            # in which self.func was defined, this caching mechanism prevents
            # undesired cache clearing when the cached function is called in
            # an environment where the introspection utilities get_func_code
            # relies on do not work (typically, in joblib child processes).
            # See #1035 for  more info
            # TODO (pierreglaser): do the same with get_func_name?
            self._func_code_info = get_func_code(self.func)
        return self._func_code_info

    def call_and_shelve(self, *args, **kwargs):
        """Call wrapped function, cache result and return a reference.

        This method returns a reference to the cached result instead of the
        result itself. The reference object is small and picklable, allowing
        to send or store it easily. Call .get() on reference object to get
        result.

        Returns
        -------
        cached_result: MemorizedResult or NotMemorizedResult
            reference to the value returned by the wrapped function. The
            class "NotMemorizedResult" is used when there is no cache
            activated (e.g. location=None in Memory).
        """
        # Return the wrapped output, without the metadata
        return self._cached_call(args, kwargs, shelving=True)[0]

    def __call__(self, *args, **kwargs):
        # Return the output, without the metadata
        return self._cached_call(args, kwargs, shelving=False)[0]

    def __getstate__(self):
        # Make sure self.func's source is introspected prior to being pickled -
        # code introspection utilities typically do not work inside child
        # processes
        _ = self.func_code_info

        # We don't store the timestamp when pickling, to avoid the hash
        # depending from it.
        state = self.__dict__.copy()
        state["timestamp"] = None

        # Invalidate the code id as id(obj) will be different in the child
        state["_func_code_id"] = None

        return state

    def check_call_in_cache(self, *args, **kwargs):
        """Check if the function call is cached and valid for given arguments.

        Does not call the function or do any work besides function inspection
        and argument hashing.

        - Compare the function code with the one from the cached function,
          asserting if it has changed.
        - Check if the function call is present in the cache.
        - Call `cache_validation_callback` for user define cache validation.

        Returns
        -------
        is_call_in_cache: bool
            Whether or not the function call is in cache and can be used.
        """
        call_id = (self.func_id, self._get_args_id(*args, **kwargs))
        return self._is_in_cache_and_valid(call_id)

    # ------------------------------------------------------------------------
    # Private interface
    # ------------------------------------------------------------------------

    def _get_args_id(self, *args, **kwargs):
        """Return the input parameter hash of a result."""
        return hashing.hash(
            filter_args(self.func, self.ignore, args, kwargs),
            coerce_mmap=self.mmap_mode is not None,
        )

    def _hash_func(self):
        """Hash a function to key the online cache"""
        func_code_h = hash(getattr(self.func, "__code__", None))
        return id(self.func), hash(self.func), func_code_h

    def _write_func_code(self, func_code, first_line):
        """Write the function code and the filename to a file."""
        # We store the first line because the filename and the function
        # name is not always enough to identify a function: people
        # sometimes have several functions named the same way in a
        # file. This is bad practice, but joblib should be robust to bad
        # practice.
        func_code = "%s %i\n%s" % (FIRST_LINE_TEXT, first_line, func_code)
        self.store_backend.store_cached_func_code([self.func_id], func_code)

        # Also store in the in-memory store of function hashes
        is_named_callable = (
            hasattr(self.func, "__name__") and self.func.__name__ != "<lambda>"
        )
        if is_named_callable:
            # Don't do this for lambda functions or strange callable
            # objects, as it ends up being too fragile
            func_hash = self._hash_func()
            try:
                _FUNCTION_HASHES[self.func] = func_hash
            except TypeError:
                # Some callable are not hashable
                pass

    def _check_previous_func_code(self, stacklevel=2):
        """
        stacklevel is the depth a which this function is called, to
        issue useful warnings to the user.
        """
        # First check if our function is in the in-memory store.
        # Using the in-memory store not only makes things faster, but it
        # also renders us robust to variations of the files when the
        # in-memory version of the code does not vary
        try:
            if self.func in _FUNCTION_HASHES:
                # We use as an identifier the id of the function and its
                # hash. This is more likely to falsely change than have hash
                # collisions, thus we are on the safe side.
                func_hash = self._hash_func()
                if func_hash == _FUNCTION_HASHES[self.func]:
                    return True
        except TypeError:
            # Some callables are not hashable
            pass

        # Here, we go through some effort to be robust to dynamically
        # changing code and collision. We cannot inspect.getsource
        # because it is not reliable when using IPython's magic "%run".
        func_code, source_file, first_line = self.func_code_info
        try:
            old_func_code, old_first_line = extract_first_line(
                self.store_backend.get_cached_func_code([self.func_id])
            )
        except (IOError, OSError):  # some backend can also raise OSError
            self._write_func_code(func_code, first_line)
            return False
        if old_func_code == func_code:
            return True

        # We have differing code, is this because we are referring to
        # different functions, or because the function we are referring to has
        # changed?

        _, func_name = get_func_name(
            self.func, resolv_alias=False, win_characters=False
        )
        if old_first_line == first_line == -1 or func_name == "<lambda>":
            if not first_line == -1:
                func_description = "{0} ({1}:{2})".format(
                    func_name, source_file, first_line
                )
            else:
                func_description = func_name
            warnings.warn(
                JobLibCollisionWarning(
                    "Cannot detect name collisions for function '{0}'".format(
                        func_description
                    )
                ),
                stacklevel=stacklevel,
            )

        # Fetch the code at the old location and compare it. If it is the
        # same than the code store, we have a collision: the code in the
        # file has not changed, but the name we have is pointing to a new
        # code block.
        if not old_first_line == first_line and source_file is not None:
            if os.path.exists(source_file):
                _, func_name = get_func_name(self.func, resolv_alias=False)
                num_lines = len(func_code.split("\n"))
                with tokenize.open(source_file) as f:
                    on_disk_func_code = f.readlines()[
                        old_first_line - 1 : old_first_line - 1 + num_lines - 1
                    ]
                on_disk_func_code = "".join(on_disk_func_code)
                possible_collision = (
                    on_disk_func_code.rstrip() == old_func_code.rstrip()
                )
            else:
                possible_collision = source_file.startswith("<doctest ")
            if possible_collision:
                warnings.warn(
                    JobLibCollisionWarning(
                        "Possible name collisions between functions "
                        "'%s' (%s:%i) and '%s' (%s:%i)"
                        % (
                            func_name,
                            source_file,
                            old_first_line,
                            func_name,
                            source_file,
                            first_line,
                        )
                    ),
                    stacklevel=stacklevel,
                )

        # The function has changed, wipe the cache directory.
        # XXX: Should be using warnings, and giving stacklevel
        if self._verbose > 10:
            _, func_name = get_func_name(self.func, resolv_alias=False)
            self.warn(
                "Function {0} (identified by {1}) has changed.".format(
                    func_name, self.func_id
                )
            )
        self.clear(warn=True)
        return False

    def clear(self, warn=True):
        """Empty the function's cache."""
        func_id = self.func_id
        if self._verbose > 0 and warn:
            self.warn("Clearing function cache identified by %s" % func_id)
        self.store_backend.clear_path(
            [
                func_id,
            ]
        )

        func_code, _, first_line = self.func_code_info
        self._write_func_code(func_code, first_line)

    def call(self, *args, **kwargs):
        """Force the execution of the function with the given arguments.

        The output values will be persisted, i.e., the cache will be updated
        with any new values.

        Parameters
        ----------
        *args: arguments
            The arguments.
        **kwargs: keyword arguments
            Keyword arguments.

        Returns
        -------
        output : object
            The output of the function call.
        metadata : dict
            The metadata associated with the call.
        """
        call_id = (self.func_id, self._get_args_id(*args, **kwargs))

        # Return the output and the metadata
        return self._call(call_id, args, kwargs)

    def _call(self, call_id, args, kwargs, shelving=False):
        # Return the output and the metadata
        self._before_call(args, kwargs)
        start_time = time.time()
        output = self.func(*args, **kwargs)
        return self._after_call(call_id, args, kwargs, shelving, output, start_time)

    def _before_call(self, args, kwargs):
        if self._verbose > 0:
            print(format_call(self.func, args, kwargs))

    def _after_call(self, call_id, args, kwargs, shelving, output, start_time):
        self.store_backend.dump_item(call_id, output, verbose=self._verbose)
        duration = time.time() - start_time
        if self._verbose > 0:
            self._print_duration(duration)
        metadata = self._persist_input(duration, call_id, args, kwargs)
        if shelving:
            return self._get_memorized_result(call_id, metadata), metadata

        if self.mmap_mode is not None:
            # Memmap the output at the first call to be consistent with
            # later calls
            output = self._load_item(call_id, metadata)
        return output, metadata

    def _persist_input(self, duration, call_id, args, kwargs, this_duration_limit=0.5):
        """Save a small summary of the call using json format in the
        output directory.

        output_dir: string
            directory where to write metadata.

        duration: float
            time taken by hashing input arguments, calling the wrapped
            function and persisting its output.

        args, kwargs: list and dict
            input arguments for wrapped function

        this_duration_limit: float
            Max execution time for this function before issuing a warning.
        """
        start_time = time.time()
        argument_dict = filter_args(self.func, self.ignore, args, kwargs)

        input_repr = dict((k, repr(v)) for k, v in argument_dict.items())
        # This can fail due to race-conditions with multiple
        # concurrent joblibs removing the file or the directory
        metadata = {
            "duration": duration,
            "input_args": input_repr,
            "time": start_time,
        }

        self.store_backend.store_metadata(call_id, metadata)

        this_duration = time.time() - start_time
        if this_duration > this_duration_limit:
            # This persistence should be fast. It will not be if repr() takes
            # time and its output is large, because json.dump will have to
            # write a large file. This should not be an issue with numpy arrays
            # for which repr() always output a short representation, but can
            # be with complex dictionaries. Fixing the problem should be a
            # matter of replacing repr() above by something smarter.
            warnings.warn(
                "Persisting input arguments took %.2fs to run."
                "If this happens often in your code, it can cause "
                "performance problems "
                "(results will be correct in all cases). "
                "The reason for this is probably some large input "
                "arguments for a wrapped function." % this_duration,
                stacklevel=5,
            )
        return metadata

    def _get_memorized_result(self, call_id, metadata=None):
        return MemorizedResult(
            self.store_backend,
            call_id,
            metadata=metadata,
            timestamp=self.timestamp,
            verbose=self._verbose - 1,
        )

    def _load_item(self, call_id, metadata=None):
        return self.store_backend.load_item(
            call_id, metadata=metadata, timestamp=self.timestamp, verbose=self._verbose
        )

    def _print_duration(self, duration, context=""):
        _, name = get_func_name(self.func)
        msg = f"{name} {context}- {format_time(duration)}"
        print(max(0, (80 - len(msg))) * "_" + msg)

    # ------------------------------------------------------------------------
    # Private `object` interface
    # ------------------------------------------------------------------------

    def __repr__(self):
        return "{class_name}(func={func}, location={location})".format(
            class_name=self.__class__.__name__,
            func=self.func,
            location=self.store_backend.location,
        )


###############################################################################
# class `AsyncMemorizedFunc`
###############################################################################
class AsyncMemorizedFunc(MemorizedFunc):
    async def __call__(self, *args, **kwargs):
        out = self._cached_call(args, kwargs, shelving=False)
        out = await out if asyncio.iscoroutine(out) else out
        return out[0]  # Don't return metadata

    async def call_and_shelve(self, *args, **kwargs):
        out = self._cached_call(args, kwargs, shelving=True)
        out = await out if asyncio.iscoroutine(out) else out
        return out[0]  # Don't return metadata

    async def call(self, *args, **kwargs):
        out = super().call(*args, **kwargs)
        return await out if asyncio.iscoroutine(out) else out

    async def _call(self, call_id, args, kwargs, shelving=False):
        self._before_call(args, kwargs)
        start_time = time.time()
        output = await self.func(*args, **kwargs)
        return self._after_call(call_id, args, kwargs, shelving, output, start_time)


###############################################################################
# class `Memory`
###############################################################################
class Memory(Logger):
    """A context object for caching a function's return value each time it
    is called with the same input arguments.

    All values are cached on the filesystem, in a deep directory
    structure.

    Read more in the :ref:`User Guide <memory>`.

    Parameters
    ----------
    location: str, pathlib.Path or None
        The path of the base directory to use as a data store
        or None. If None is given, no caching is done and
        the Memory object is completely transparent. This option
        replaces cachedir since version 0.12.

    backend: str, optional, default='local'
        Type of store backend for reading/writing cache files.
        The 'local' backend is using regular filesystem operations to
        manipulate data (open, mv, etc) in the backend.

    mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
        The memmapping mode used when loading from cache
        numpy arrays. See numpy.load for the meaning of the
        arguments.

    compress: boolean, or integer, optional
        Whether to zip the stored data on disk. If an integer is
        given, it should be between 1 and 9, and sets the amount
        of compression. Note that compressed arrays cannot be
        read by memmapping.

    verbose: int, optional
        Verbosity flag, controls the debug messages that are issued
        as functions are evaluated.

    backend_options: dict, optional
        Contains a dictionary of named parameters used to configure
        the store backend.
    """

    # ------------------------------------------------------------------------
    # Public interface
    # ------------------------------------------------------------------------

    def __init__(
        self,
        location=None,
        backend="local",
        mmap_mode=None,
        compress=False,
        verbose=1,
        backend_options=None,
    ):
        Logger.__init__(self)
        self._verbose = verbose
        self.mmap_mode = mmap_mode
        self.timestamp = time.time()
        self.backend = backend
        self.compress = compress
        if backend_options is None:
            backend_options = {}
        self.backend_options = backend_options

        if compress and mmap_mode is not None:
            warnings.warn("Compressed results cannot be memmapped", stacklevel=2)

        self.location = location
        if isinstance(location, str):
            location = os.path.join(location, "joblib")

        self.store_backend = _store_backend_factory(
            backend,
            location,
            verbose=self._verbose,
            backend_options=dict(
                compress=compress, mmap_mode=mmap_mode, **backend_options
            ),
        )

    def cache(
        self,
        func=None,
        ignore=None,
        verbose=None,
        mmap_mode=False,
        cache_validation_callback=None,
    ):
        """Decorates the given function func to only compute its return
        value for input arguments not cached on disk.

        Parameters
        ----------
        func: callable, optional
            The function to be decorated
        ignore: list of strings
            A list of arguments name to ignore in the hashing
        verbose: integer, optional
            The verbosity mode of the function. By default that
            of the memory object is used.
        mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
            The memmapping mode used when loading from cache
            numpy arrays. See numpy.load for the meaning of the
            arguments. By default that of the memory object is used.
        cache_validation_callback: callable, optional
            Callable to validate whether or not the cache is valid. When
            the cached function is called with arguments for which a cache
            exists, this callable is called with the metadata of the cached
            result as its sole argument. If it returns True, then the
            cached result is returned, else the cache for these arguments
            is cleared and recomputed.

        Returns
        -------
        decorated_func: MemorizedFunc object
            The returned object is a MemorizedFunc object, that is
            callable (behaves like a function), but offers extra
            methods for cache lookup and management. See the
            documentation for :class:`joblib.memory.MemorizedFunc`.
        """
        if cache_validation_callback is not None and not callable(
            cache_validation_callback
        ):
            raise ValueError(
                "cache_validation_callback needs to be callable. "
                f"Got {cache_validation_callback}."
            )
        if func is None:
            # Partial application, to be able to specify extra keyword
            # arguments in decorators
            return functools.partial(
                self.cache,
                ignore=ignore,
                mmap_mode=mmap_mode,
                verbose=verbose,
                cache_validation_callback=cache_validation_callback,
            )
        if self.store_backend is None:
            cls = (
                AsyncNotMemorizedFunc
                if asyncio.iscoroutinefunction(func)
                else NotMemorizedFunc
            )
            return cls(func)
        if verbose is None:
            verbose = self._verbose
        if mmap_mode is False:
            mmap_mode = self.mmap_mode
        if isinstance(func, MemorizedFunc):
            func = func.func
        cls = AsyncMemorizedFunc if asyncio.iscoroutinefunction(func) else MemorizedFunc
        return cls(
            func,
            location=self.store_backend,
            backend=self.backend,
            ignore=ignore,
            mmap_mode=mmap_mode,
            compress=self.compress,
            verbose=verbose,
            timestamp=self.timestamp,
            cache_validation_callback=cache_validation_callback,
        )

    def clear(self, warn=True):
        """Erase the complete cache directory."""
        if warn:
            self.warn("Flushing completely the cache")
        if self.store_backend is not None:
            self.store_backend.clear()

            # As the cache is completely clear, make sure the _FUNCTION_HASHES
            # cache is also reset. Else, for a function that is present in this
            # table, results cached after this clear will be have cache miss
            # as the function code is not re-written.
            _FUNCTION_HASHES.clear()

    def reduce_size(self, bytes_limit=None, items_limit=None, age_limit=None):
        """Remove cache elements to make the cache fit its limits.

        The limitation can impose that the cache size fits in ``bytes_limit``,
        that the number of cache items is no more than ``items_limit``, and
        that all files in cache are not older than ``age_limit``.

        Parameters
        ----------
        bytes_limit: int | str, optional
            Limit in bytes of the size of the cache. By default, the size of
            the cache is unlimited. When reducing the size of the cache,
            ``joblib`` keeps the most recently accessed items first. If a
            str is passed, it is converted to a number of bytes using units
            { K | M | G} for kilo, mega, giga.

        items_limit: int, optional
            Number of items to limit the cache to.  By default, the number of
            items in the cache is unlimited.  When reducing the size of the
            cache, ``joblib`` keeps the most recently accessed items first.

        age_limit: datetime.timedelta, optional
            Maximum age of items to limit the cache to.  When reducing the size
            of the cache, any items last accessed more than the given length of
            time ago are deleted. Example: to remove files older than 5 days,
            use datetime.timedelta(days=5). Negative timedelta are not
            accepted.
        """
        if self.store_backend is None:
            # No cached results, this function does nothing.
            return

        if bytes_limit is None and items_limit is None and age_limit is None:
            # No limitation to impose, returning
            return

        # Defers the actual limits enforcing to the store backend.
        self.store_backend.enforce_store_limits(bytes_limit, items_limit, age_limit)

    def eval(self, func, *args, **kwargs):
        """Eval function func with arguments `*args` and `**kwargs`,
        in the context of the memory.

        This method works similarly to the builtin `apply`, except
        that the function is called only if the cache is not
        up to date.

        """
        if self.store_backend is None:
            return func(*args, **kwargs)
        return self.cache(func)(*args, **kwargs)

    # ------------------------------------------------------------------------
    # Private `object` interface
    # ------------------------------------------------------------------------

    def __repr__(self):
        return "{class_name}(location={location})".format(
            class_name=self.__class__.__name__,
            location=(
                None if self.store_backend is None else self.store_backend.location
            ),
        )

    def __getstate__(self):
        """We don't store the timestamp when pickling, to avoid the hash
        depending from it.
        """
        state = self.__dict__.copy()
        state["timestamp"] = None
        return state


###############################################################################
# cache_validation_callback helpers
###############################################################################


def expires_after(
    days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0
):
    """Helper cache_validation_callback to force recompute after a duration.

    Parameters
    ----------
    days, seconds, microseconds, milliseconds, minutes, hours, weeks: numbers
        argument passed to a timedelta.
    """
    delta = datetime.timedelta(
        days=days,
        seconds=seconds,
        microseconds=microseconds,
        milliseconds=milliseconds,
        minutes=minutes,
        hours=hours,
        weeks=weeks,
    )

    def cache_validation_callback(metadata):
        computation_age = time.time() - metadata["time"]
        return computation_age < delta.total_seconds()

    return cache_validation_callback