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.gitattributes

@@ -46,3 +46,4 @@ tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_vendor/distl
 tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Utils.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_vendor/__pycache__/typing_extensions.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
 tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Compiler/__pycache__/Optimize.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Tempita/_tempita.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Tempita/_tempita.cpython-311-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0477f20b0dfa19f79dc484a4a92c0ba4ae204d074ad171452e1d7de8bbd4d6cf
+size 595904

tuning-competition-baseline/.venv/lib/python3.11/site-packages/fsspec/conftest.py

@@ -0,0 +1,55 @@
+import os
+import shutil
+import subprocess
+import sys
+import time
+
+import pytest
+
+import fsspec
+from fsspec.implementations.cached import CachingFileSystem
+
+
+@pytest.fixture()
+def m():
+    """
+    Fixture providing a memory filesystem.
+    """
+    m = fsspec.filesystem("memory")
+    m.store.clear()
+    m.pseudo_dirs.clear()
+    m.pseudo_dirs.append("")
+    try:
+        yield m
+    finally:
+        m.store.clear()
+        m.pseudo_dirs.clear()
+        m.pseudo_dirs.append("")
+
+
+@pytest.fixture
+def ftp_writable(tmpdir):
+    """
+    Fixture providing a writable FTP filesystem.
+    """
+    pytest.importorskip("pyftpdlib")
+    from fsspec.implementations.ftp import FTPFileSystem
+
+    FTPFileSystem.clear_instance_cache()  # remove lingering connections
+    CachingFileSystem.clear_instance_cache()
+    d = str(tmpdir)
+    with open(os.path.join(d, "out"), "wb") as f:
+        f.write(b"hello" * 10000)
+    P = subprocess.Popen(
+        [sys.executable, "-m", "pyftpdlib", "-d", d, "-u", "user", "-P", "pass", "-w"]
+    )
+    try:
+        time.sleep(1)
+        yield "localhost", 2121, "user", "pass"
+    finally:
+        P.terminate()
+        P.wait()
+        try:
+            shutil.rmtree(tmpdir)
+        except Exception:
+            pass

tuning-competition-baseline/.venv/lib/python3.11/site-packages/fsspec/implementations/github.py

@@ -0,0 +1,227 @@
+import requests
+
+from ..spec import AbstractFileSystem
+from ..utils import infer_storage_options
+from .memory import MemoryFile
+
+# TODO: add GIST backend, would be very similar
+
+
+class GithubFileSystem(AbstractFileSystem):
+    """Interface to files in github
+
+    An instance of this class provides the files residing within a remote github
+    repository. You may specify a point in the repos history, by SHA, branch
+    or tag (default is current master).
+
+    Given that code files tend to be small, and that github does not support
+    retrieving partial content, we always fetch whole files.
+
+    When using fsspec.open, allows URIs of the form:
+
+    - "github://path/file", in which case you must specify org, repo and
+      may specify sha in the extra args
+    - 'github://org:repo@/precip/catalog.yml', where the org and repo are
+      part of the URI
+    - 'github://org:repo@sha/precip/catalog.yml', where the sha is also included
+
+    ``sha`` can be the full or abbreviated hex of the commit you want to fetch
+    from, or a branch or tag name (so long as it doesn't contain special characters
+    like "/", "?", which would have to be HTTP-encoded).
+
+    For authorised access, you must provide username and token, which can be made
+    at https://github.com/settings/tokens
+    """
+
+    url = "https://api.github.com/repos/{org}/{repo}/git/trees/{sha}"
+    rurl = "https://raw.githubusercontent.com/{org}/{repo}/{sha}/{path}"
+    protocol = "github"
+    timeout = (60, 60)  # connect, read timeouts
+
+    def __init__(
+        self, org, repo, sha=None, username=None, token=None, timeout=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.org = org
+        self.repo = repo
+        if (username is None) ^ (token is None):
+            raise ValueError("Auth required both username and token")
+        self.username = username
+        self.token = token
+        if timeout is not None:
+            self.timeout = timeout
+        if sha is None:
+            # look up default branch (not necessarily "master")
+            u = "https://api.github.com/repos/{org}/{repo}"
+            r = requests.get(
+                u.format(org=org, repo=repo), timeout=self.timeout, **self.kw
+            )
+            r.raise_for_status()
+            sha = r.json()["default_branch"]
+
+        self.root = sha
+        self.ls("")
+
+    @property
+    def kw(self):
+        if self.username:
+            return {"auth": (self.username, self.token)}
+        return {}
+
+    @classmethod
+    def repos(cls, org_or_user, is_org=True):
+        """List repo names for given org or user
+
+        This may become the top level of the FS
+
+        Parameters
+        ----------
+        org_or_user: str
+            Name of the github org or user to query
+        is_org: bool (default True)
+            Whether the name is an organisation (True) or user (False)
+
+        Returns
+        -------
+        List of string
+        """
+        r = requests.get(
+            f"https://api.github.com/{['users', 'orgs'][is_org]}/{org_or_user}/repos",
+            timeout=cls.timeout,
+        )
+        r.raise_for_status()
+        return [repo["name"] for repo in r.json()]
+
+    @property
+    def tags(self):
+        """Names of tags in the repo"""
+        r = requests.get(
+            f"https://api.github.com/repos/{self.org}/{self.repo}/tags",
+            timeout=self.timeout,
+            **self.kw,
+        )
+        r.raise_for_status()
+        return [t["name"] for t in r.json()]
+
+    @property
+    def branches(self):
+        """Names of branches in the repo"""
+        r = requests.get(
+            f"https://api.github.com/repos/{self.org}/{self.repo}/branches",
+            timeout=self.timeout,
+            **self.kw,
+        )
+        r.raise_for_status()
+        return [t["name"] for t in r.json()]
+
+    @property
+    def refs(self):
+        """Named references, tags and branches"""
+        return {"tags": self.tags, "branches": self.branches}
+
+    def ls(self, path, detail=False, sha=None, _sha=None, **kwargs):
+        """List files at given path
+
+        Parameters
+        ----------
+        path: str
+            Location to list, relative to repo root
+        detail: bool
+            If True, returns list of dicts, one per file; if False, returns
+            list of full filenames only
+        sha: str (optional)
+            List at the given point in the repo history, branch or tag name or commit
+            SHA
+        _sha: str (optional)
+            List this specific tree object (used internally to descend into trees)
+        """
+        path = self._strip_protocol(path)
+        if path == "":
+            _sha = sha or self.root
+        if _sha is None:
+            parts = path.rstrip("/").split("/")
+            so_far = ""
+            _sha = sha or self.root
+            for part in parts:
+                out = self.ls(so_far, True, sha=sha, _sha=_sha)
+                so_far += "/" + part if so_far else part
+                out = [o for o in out if o["name"] == so_far]
+                if not out:
+                    raise FileNotFoundError(path)
+                out = out[0]
+                if out["type"] == "file":
+                    if detail:
+                        return [out]
+                    else:
+                        return path
+                _sha = out["sha"]
+        if path not in self.dircache or sha not in [self.root, None]:
+            r = requests.get(
+                self.url.format(org=self.org, repo=self.repo, sha=_sha),
+                timeout=self.timeout,
+                **self.kw,
+            )
+            if r.status_code == 404:
+                raise FileNotFoundError(path)
+            r.raise_for_status()
+            types = {"blob": "file", "tree": "directory"}
+            out = [
+                {
+                    "name": path + "/" + f["path"] if path else f["path"],
+                    "mode": f["mode"],
+                    "type": types[f["type"]],
+                    "size": f.get("size", 0),
+                    "sha": f["sha"],
+                }
+                for f in r.json()["tree"]
+                if f["type"] in types
+            ]
+            if sha in [self.root, None]:
+                self.dircache[path] = out
+        else:
+            out = self.dircache[path]
+        if detail:
+            return out
+        else:
+            return sorted([f["name"] for f in out])
+
+    def invalidate_cache(self, path=None):
+        self.dircache.clear()
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        opts = infer_storage_options(path)
+        if "username" not in opts:
+            return super()._strip_protocol(path)
+        return opts["path"].lstrip("/")
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        opts = infer_storage_options(path)
+        if "username" not in opts:
+            return {}
+        out = {"org": opts["username"], "repo": opts["password"]}
+        if opts["host"]:
+            out["sha"] = opts["host"]
+        return out
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        sha=None,
+        **kwargs,
+    ):
+        if mode != "rb":
+            raise NotImplementedError
+        url = self.rurl.format(
+            org=self.org, repo=self.repo, path=path, sha=sha or self.root
+        )
+        r = requests.get(url, timeout=self.timeout, **self.kw)
+        if r.status_code == 404:
+            raise FileNotFoundError(path)
+        r.raise_for_status()
+        return MemoryFile(None, None, r.content)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/fsspec/registry.py

@@ -0,0 +1,299 @@
+from __future__ import annotations
+
+import importlib
+import types
+import warnings
+
+__all__ = ["registry", "get_filesystem_class", "default"]
+
+# internal, mutable
+_registry: dict[str, type] = {}
+
+# external, immutable
+registry = types.MappingProxyType(_registry)
+default = "file"
+
+
+def register_implementation(name, cls, clobber=False, errtxt=None):
+    """Add implementation class to the registry
+
+    Parameters
+    ----------
+    name: str
+        Protocol name to associate with the class
+    cls: class or str
+        if a class: fsspec-compliant implementation class (normally inherits from
+        ``fsspec.AbstractFileSystem``, gets added straight to the registry. If a
+        str, the full path to an implementation class like package.module.class,
+        which gets added to known_implementations,
+        so the import is deferred until the filesystem is actually used.
+    clobber: bool (optional)
+        Whether to overwrite a protocol with the same name; if False, will raise
+        instead.
+    errtxt: str (optional)
+        If given, then a failure to import the given class will result in this
+        text being given.
+    """
+    if isinstance(cls, str):
+        if name in known_implementations and clobber is False:
+            if cls != known_implementations[name]["class"]:
+                raise ValueError(
+                    f"Name ({name}) already in the known_implementations and clobber "
+                    f"is False"
+                )
+        else:
+            known_implementations[name] = {
+                "class": cls,
+                "err": errtxt or f"{cls} import failed for protocol {name}",
+            }
+
+    else:
+        if name in registry and clobber is False:
+            if _registry[name] is not cls:
+                raise ValueError(
+                    f"Name ({name}) already in the registry and clobber is False"
+                )
+        else:
+            _registry[name] = cls
+
+
+# protocols mapped to the class which implements them. This dict can be
+# updated with register_implementation
+known_implementations = {
+    "data": {"class": "fsspec.implementations.data.DataFileSystem"},
+    "file": {"class": "fsspec.implementations.local.LocalFileSystem"},
+    "local": {"class": "fsspec.implementations.local.LocalFileSystem"},
+    "memory": {"class": "fsspec.implementations.memory.MemoryFileSystem"},
+    "dropbox": {
+        "class": "dropboxdrivefs.DropboxDriveFileSystem",
+        "err": (
+            'DropboxFileSystem requires "dropboxdrivefs",'
+            '"requests" and "dropbox" to be installed'
+        ),
+    },
+    "http": {
+        "class": "fsspec.implementations.http.HTTPFileSystem",
+        "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed',
+    },
+    "https": {
+        "class": "fsspec.implementations.http.HTTPFileSystem",
+        "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed',
+    },
+    "zip": {"class": "fsspec.implementations.zip.ZipFileSystem"},
+    "tar": {"class": "fsspec.implementations.tar.TarFileSystem"},
+    "gcs": {
+        "class": "gcsfs.GCSFileSystem",
+        "err": "Please install gcsfs to access Google Storage",
+    },
+    "gs": {
+        "class": "gcsfs.GCSFileSystem",
+        "err": "Please install gcsfs to access Google Storage",
+    },
+    "gdrive": {
+        "class": "gdrivefs.GoogleDriveFileSystem",
+        "err": "Please install gdrivefs for access to Google Drive",
+    },
+    "sftp": {
+        "class": "fsspec.implementations.sftp.SFTPFileSystem",
+        "err": 'SFTPFileSystem requires "paramiko" to be installed',
+    },
+    "ssh": {
+        "class": "fsspec.implementations.sftp.SFTPFileSystem",
+        "err": 'SFTPFileSystem requires "paramiko" to be installed',
+    },
+    "ftp": {"class": "fsspec.implementations.ftp.FTPFileSystem"},
+    "hdfs": {
+        "class": "fsspec.implementations.arrow.HadoopFileSystem",
+        "err": "pyarrow and local java libraries required for HDFS",
+    },
+    "arrow_hdfs": {
+        "class": "fsspec.implementations.arrow.HadoopFileSystem",
+        "err": "pyarrow and local java libraries required for HDFS",
+    },
+    "webhdfs": {
+        "class": "fsspec.implementations.webhdfs.WebHDFS",
+        "err": 'webHDFS access requires "requests" to be installed',
+    },
+    "s3": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"},
+    "s3a": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"},
+    "wandb": {"class": "wandbfs.WandbFS", "err": "Install wandbfs to access wandb"},
+    "oci": {
+        "class": "ocifs.OCIFileSystem",
+        "err": "Install ocifs to access OCI Object Storage",
+    },
+    "ocilake": {
+        "class": "ocifs.OCIFileSystem",
+        "err": "Install ocifs to access OCI Data Lake",
+    },
+    "asynclocal": {
+        "class": "morefs.asyn_local.AsyncLocalFileSystem",
+        "err": "Install 'morefs[asynclocalfs]' to use AsyncLocalFileSystem",
+    },
+    "adl": {
+        "class": "adlfs.AzureDatalakeFileSystem",
+        "err": "Install adlfs to access Azure Datalake Gen1",
+    },
+    "abfs": {
+        "class": "adlfs.AzureBlobFileSystem",
+        "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage",
+    },
+    "az": {
+        "class": "adlfs.AzureBlobFileSystem",
+        "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage",
+    },
+    "cached": {"class": "fsspec.implementations.cached.CachingFileSystem"},
+    "blockcache": {"class": "fsspec.implementations.cached.CachingFileSystem"},
+    "filecache": {"class": "fsspec.implementations.cached.WholeFileCacheFileSystem"},
+    "simplecache": {"class": "fsspec.implementations.cached.SimpleCacheFileSystem"},
+    "dask": {
+        "class": "fsspec.implementations.dask.DaskWorkerFileSystem",
+        "err": "Install dask distributed to access worker file system",
+    },
+    "dbfs": {
+        "class": "fsspec.implementations.dbfs.DatabricksFileSystem",
+        "err": "Install the requests package to use the DatabricksFileSystem",
+    },
+    "github": {
+        "class": "fsspec.implementations.github.GithubFileSystem",
+        "err": "Install the requests package to use the github FS",
+    },
+    "git": {
+        "class": "fsspec.implementations.git.GitFileSystem",
+        "err": "Install pygit2 to browse local git repos",
+    },
+    "smb": {
+        "class": "fsspec.implementations.smb.SMBFileSystem",
+        "err": 'SMB requires "smbprotocol" or "smbprotocol[kerberos]" installed',
+    },
+    "jupyter": {
+        "class": "fsspec.implementations.jupyter.JupyterFileSystem",
+        "err": "Jupyter FS requires requests to be installed",
+    },
+    "jlab": {
+        "class": "fsspec.implementations.jupyter.JupyterFileSystem",
+        "err": "Jupyter FS requires requests to be installed",
+    },
+    "libarchive": {
+        "class": "fsspec.implementations.libarchive.LibArchiveFileSystem",
+        "err": "LibArchive requires to be installed",
+    },
+    "reference": {"class": "fsspec.implementations.reference.ReferenceFileSystem"},
+    "generic": {"class": "fsspec.generic.GenericFileSystem"},
+    "oss": {
+        "class": "ossfs.OSSFileSystem",
+        "err": "Install ossfs to access Alibaba Object Storage System",
+    },
+    "webdav": {
+        "class": "webdav4.fsspec.WebdavFileSystem",
+        "err": "Install webdav4 to access WebDAV",
+    },
+    "dvc": {
+        "class": "dvc.api.DVCFileSystem",
+        "err": "Install dvc to access DVCFileSystem",
+    },
+    "hf": {
+        "class": "huggingface_hub.HfFileSystem",
+        "err": "Install huggingface_hub to access HfFileSystem",
+    },
+    "root": {
+        "class": "fsspec_xrootd.XRootDFileSystem",
+        "err": "Install fsspec-xrootd to access xrootd storage system."
+        + " Note: 'root' is the protocol name for xrootd storage systems,"
+        + " not referring to root directories",
+    },
+    "dir": {"class": "fsspec.implementations.dirfs.DirFileSystem"},
+    "box": {
+        "class": "boxfs.BoxFileSystem",
+        "err": "Please install boxfs to access BoxFileSystem",
+    },
+    "lakefs": {
+        "class": "lakefs_spec.LakeFSFileSystem",
+        "err": "Please install lakefs-spec to access LakeFSFileSystem",
+    },
+}
+
+
+def get_filesystem_class(protocol):
+    """Fetch named protocol implementation from the registry
+
+    The dict ``known_implementations`` maps protocol names to the locations
+    of classes implementing the corresponding file-system. When used for the
+    first time, appropriate imports will happen and the class will be placed in
+    the registry. All subsequent calls will fetch directly from the registry.
+
+    Some protocol implementations require additional dependencies, and so the
+    import may fail. In this case, the string in the "err" field of the
+    ``known_implementations`` will be given as the error message.
+    """
+    if not protocol:
+        protocol = default
+
+    if protocol not in registry:
+        if protocol not in known_implementations:
+            raise ValueError(f"Protocol not known: {protocol}")
+        bit = known_implementations[protocol]
+        try:
+            register_implementation(protocol, _import_class(bit["class"]))
+        except ImportError as e:
+            raise ImportError(bit["err"]) from e
+    cls = registry[protocol]
+    if getattr(cls, "protocol", None) in ("abstract", None):
+        cls.protocol = protocol
+
+    return cls
+
+
+s3_msg = """Your installed version of s3fs is very old and known to cause
+severe performance issues, see also https://github.com/dask/dask/issues/10276
+
+To fix, you should specify a lower version bound on s3fs, or
+update the current installation.
+"""
+
+
+def _import_class(cls, minv=None):
+    """Take a string FQP and return the imported class or identifier
+
+    clas is of the form "package.module.klass" or "package.module:subobject.klass"
+    """
+    if ":" in cls:
+        mod, name = cls.rsplit(":", 1)
+        s3 = mod == "s3fs"
+        mod = importlib.import_module(mod)
+        if s3 and mod.__version__.split(".") < ["0", "5"]:
+            warnings.warn(s3_msg)
+        for part in name.split("."):
+            mod = getattr(mod, part)
+        return mod
+    else:
+        mod, name = cls.rsplit(".", 1)
+        s3 = mod == "s3fs"
+        mod = importlib.import_module(mod)
+        if s3 and mod.__version__.split(".") < ["0", "5"]:
+            warnings.warn(s3_msg)
+        return getattr(mod, name)
+
+
+def filesystem(protocol, **storage_options):
+    """Instantiate filesystems for given protocol and arguments
+
+    ``storage_options`` are specific to the protocol being chosen, and are
+    passed directly to the class.
+    """
+    if protocol == "arrow_hdfs":
+        warnings.warn(
+            "The 'arrow_hdfs' protocol has been deprecated and will be "
+            "removed in the future. Specify it as 'hdfs'.",
+            DeprecationWarning,
+        )
+
+    cls = get_filesystem_class(protocol)
+    return cls(**storage_options)
+
+
+def available_protocols():
+    """Return a list of the implemented protocols.
+
+    Note that any given protocol may require extra packages to be importable.
+    """
+    return list(known_implementations)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/fsspec/transaction.py

@@ -0,0 +1,85 @@
+from collections import deque
+
+
+class Transaction:
+    """Filesystem transaction write context
+
+    Gathers files for deferred commit or discard, so that several write
+    operations can be finalized semi-atomically. This works by having this
+    instance as the ``.transaction`` attribute of the given filesystem
+    """
+
+    def __init__(self, fs):
+        """
+        Parameters
+        ----------
+        fs: FileSystem instance
+        """
+        self.fs = fs
+        self.files = deque()
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """End transaction and commit, if exit is not due to exception"""
+        # only commit if there was no exception
+        self.complete(commit=exc_type is None)
+        self.fs._intrans = False
+        self.fs._transaction = None
+
+    def start(self):
+        """Start a transaction on this FileSystem"""
+        self.files = deque()  # clean up after previous failed completions
+        self.fs._intrans = True
+
+    def complete(self, commit=True):
+        """Finish transaction: commit or discard all deferred files"""
+        while self.files:
+            f = self.files.popleft()
+            if commit:
+                f.commit()
+            else:
+                f.discard()
+        self.fs._intrans = False
+
+
+class FileActor:
+    def __init__(self):
+        self.files = []
+
+    def commit(self):
+        for f in self.files:
+            f.commit()
+        self.files.clear()
+
+    def discard(self):
+        for f in self.files:
+            f.discard()
+        self.files.clear()
+
+    def append(self, f):
+        self.files.append(f)
+
+
+class DaskTransaction(Transaction):
+    def __init__(self, fs):
+        """
+        Parameters
+        ----------
+        fs: FileSystem instance
+        """
+        import distributed
+
+        super().__init__(fs)
+        client = distributed.default_client()
+        self.files = client.submit(FileActor, actor=True).result()
+
+    def complete(self, commit=True):
+        """Finish transaction: commit or discard all deferred files"""
+        if commit:
+            self.files.commit().result()
+        else:
+            self.files.discard().result()
+        self.fs._intrans = False

tuning-competition-baseline/.venv/lib/python3.11/site-packages/functorch/dim/__init__.py

@@ -0,0 +1,179 @@
+import dis
+import inspect
+from typing import Sequence, Union
+
+import torch
+
+import functorch._C
+from functorch._C import dim as _C
+from .tree_map import tree_flatten, tree_map
+from .wrap_type import wrap_type
+
+_C._patch_tensor_class()
+dims, DimList, dimlists = _C.dims, _C.DimList, _C.dimlists
+
+
+class DimensionMismatchError(Exception):
+    pass
+
+
+class DimensionBindError(Exception):
+    pass
+
+
+from . import op_properties
+
+# use dict to avoid writing C++ bindings for set
+pointwise = dict.fromkeys(op_properties.pointwise, True)
+
+use_c = True
+if not use_c:
+    from . import reference
+
+
+class _Tensor:
+    # fast path around slow wrapping/unwrapping logic for simply queries used
+    # by the implementation...
+
+    @property
+    def dims(self):
+        return tuple(d for d in self._levels if isinstance(d, Dim))
+
+    def dim(self):
+        return self.ndim
+
+    if use_c:
+        __torch_function__ = classmethod(_C.__torch_function__)
+        expand = _C._instancemethod(_C.expand)
+    else:
+        __torch_function__ = reference.__torch_function__
+        expand = reference.expand
+
+    index = _C._instancemethod(_C.index)
+
+    def __repr__(self):
+        tensor, levels, ndim = self._tensor, self._levels, self.ndim
+        return f"{tensor}\nwith dims={tuple(l + ndim if isinstance(l, int) else l for l in levels)} sizes={tuple(tensor.size())}"
+
+
+TensorLike = (_Tensor, torch.Tensor)
+
+
+class Dim(_C.Dim, _Tensor):
+    # note that _C.Dim comes before tensor because we want the Dim API for things like size to take precendence.
+    # Tensor defines format, but we want to print Dims with special formatting
+    __format__ = object.__format__
+
+
+class Tensor(_Tensor, _C.Tensor):
+    if not use_c:
+        from_batched = staticmethod(_C.Tensor_from_batched)
+    from_positional = staticmethod(_C.Tensor_from_positional)
+    sum = _C._instancemethod(_C.Tensor_sum)
+
+
+def cat(tensors, dim, new_dim):
+    n = dims()
+    return stack(tensors, n, dim).index([n, dim], new_dim)
+
+
+if use_c:
+    _wrap = _C._wrap
+
+    def _def(name, *args, **kwargs):
+        orig = getattr(torch.Tensor, name)
+        setattr(_Tensor, name, _C._instancemethod(_wrap(orig, *args, **kwargs)))
+
+    t__getitem__ = _C._instancemethod(_C.__getitem__)
+    stack = _C.stack
+    split = _C._instancemethod(_C.split)
+else:
+    _wrap, _def = reference._wrap, reference._def
+    t__getitem__ = reference.t__getitem__
+    stack = reference.stack
+    split = reference.split
+
+# note: there is no python reference
+t__setitem__ = _C._instancemethod(_C.__setitem__)
+# this is patched in the C API because otherwise torch.Tensor will
+# no longer be considered a sequence and things will break
+# torch.Tensor.__getitem__ = t__getitem__
+
+_Tensor.__getitem__ = t__getitem__
+# torch.Tensor.__setitem__ = t__setitem__
+_Tensor.__setitem__ = t__setitem__
+
+torch.Tensor.split = split
+_Tensor.split = split
+torch.Tensor.expand = _C._instancemethod(_C.expand)
+torch.Tensor.index = _C._instancemethod(_C.index)
+wrap_type(use_c, _Tensor, torch.Tensor, _Tensor.__torch_function__)
+del _Tensor.ndim
+
+if use_c:
+    _Tensor.order = _C._instancemethod(_C.order)
+else:
+    _Tensor.order = reference.positional
+
+_def("mean")
+_def("sum")
+_def("all")
+_def("amax")
+_def("amin")
+_def("aminmax")
+_def("any")
+_def("count_nonzero")
+_def("logsumexp")
+_def("nanmean")
+_def("nansum")
+_def("prod")
+_def("std", keepdim_offset=2)
+_def("var", keepdim_offset=2)
+_def("max", single_dim=True)
+_def("min", single_dim=True)
+_def("argmax", single_dim=True)
+_def("argmin", single_dim=True)
+_def("kthvalue", single_dim=True)
+_def("median", single_dim=True)
+_def("nanmedian", single_dim=True)
+_def("mode", single_dim=True)
+_def("sort", reduce=False)
+_def("argsort", reduce=False)
+_def("unbind", single_dim=True)
+_def("chunk", dim_offset=1, reduce=False)
+_def("cummax", single_dim=True, reduce=False)
+_def("cummin", single_dim=True, reduce=False)
+_def("cumprod", single_dim=True, reduce=False)
+_def("cumprod_", single_dim=True, reduce=False)
+_def("cumsum", single_dim=True, reduce=False)
+_def("cumsum_", single_dim=True, reduce=False)
+_def("logcumsumexp", single_dim=True, reduce=False)
+_def("renorm", dim_offset=1, single_dim=True, reduce=False)
+_def("softmax", single_dim=True, reduce=False)
+softmax = _wrap(torch.nn.functional.softmax, single_dim=True, reduce=False)
+
+# stuff to handle in the future, because they require special
+# binding logic for dims
+# cross
+# diag_embed
+# diagonal
+# diagonal_scatter
+# diff
+# nanquantile
+# quantile
+# roll
+# rot90
+# topk (new dimes on output)
+# should these all be subsumed by inplace indexing?
+# index_add_
+# index_add
+# index_copy
+# index_copy_
+# index_fill
+# index_fill_
+# index_select
+# scatter
+# scatter_
+# scatter_add
+# scatter_add_
+# scatter_reduce

tuning-competition-baseline/.venv/lib/python3.11/site-packages/functorch/dim/batch_tensor.py

@@ -0,0 +1,25 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from contextlib import contextmanager
+
+from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers
+
+_enabled = False
+
+
+@contextmanager
+def _enable_layers(dims):
+    global _enabled
+    assert not _enabled
+    input = sorted((d._level, d.size) for d in dims if not isinstance(d, int))
+    n = len(input)
+    try:
+        _vmap_add_layers(input)
+        _enabled = True
+        yield
+    finally:
+        _enabled = False
+        _vmap_remove_layers(n)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/ctx_base.py

@@ -0,0 +1,494 @@
+from operator import gt, lt
+
+from .libmp.backend import xrange
+
+from .functions.functions import SpecialFunctions
+from .functions.rszeta import RSCache
+from .calculus.quadrature import QuadratureMethods
+from .calculus.inverselaplace import LaplaceTransformInversionMethods
+from .calculus.calculus import CalculusMethods
+from .calculus.optimization import OptimizationMethods
+from .calculus.odes import ODEMethods
+from .matrices.matrices import MatrixMethods
+from .matrices.calculus import MatrixCalculusMethods
+from .matrices.linalg import LinearAlgebraMethods
+from .matrices.eigen import Eigen
+from .identification import IdentificationMethods
+from .visualization import VisualizationMethods
+
+from . import libmp
+
+class Context(object):
+    pass
+
+class StandardBaseContext(Context,
+    SpecialFunctions,
+    RSCache,
+    QuadratureMethods,
+    LaplaceTransformInversionMethods,
+    CalculusMethods,
+    MatrixMethods,
+    MatrixCalculusMethods,
+    LinearAlgebraMethods,
+    Eigen,
+    IdentificationMethods,
+    OptimizationMethods,
+    ODEMethods,
+    VisualizationMethods):
+
+    NoConvergence = libmp.NoConvergence
+    ComplexResult = libmp.ComplexResult
+
+    def __init__(ctx):
+        ctx._aliases = {}
+        # Call those that need preinitialization (e.g. for wrappers)
+        SpecialFunctions.__init__(ctx)
+        RSCache.__init__(ctx)
+        QuadratureMethods.__init__(ctx)
+        LaplaceTransformInversionMethods.__init__(ctx)
+        CalculusMethods.__init__(ctx)
+        MatrixMethods.__init__(ctx)
+
+    def _init_aliases(ctx):
+        for alias, value in ctx._aliases.items():
+            try:
+                setattr(ctx, alias, getattr(ctx, value))
+            except AttributeError:
+                pass
+
+    _fixed_precision = False
+
+    # XXX
+    verbose = False
+
+    def warn(ctx, msg):
+        print("Warning:", msg)
+
+    def bad_domain(ctx, msg):
+        raise ValueError(msg)
+
+    def _re(ctx, x):
+        if hasattr(x, "real"):
+            return x.real
+        return x
+
+    def _im(ctx, x):
+        if hasattr(x, "imag"):
+            return x.imag
+        return ctx.zero
+
+    def _as_points(ctx, x):
+        return x
+
+    def fneg(ctx, x, **kwargs):
+        return -ctx.convert(x)
+
+    def fadd(ctx, x, y, **kwargs):
+        return ctx.convert(x)+ctx.convert(y)
+
+    def fsub(ctx, x, y, **kwargs):
+        return ctx.convert(x)-ctx.convert(y)
+
+    def fmul(ctx, x, y, **kwargs):
+        return ctx.convert(x)*ctx.convert(y)
+
+    def fdiv(ctx, x, y, **kwargs):
+        return ctx.convert(x)/ctx.convert(y)
+
+    def fsum(ctx, args, absolute=False, squared=False):
+        if absolute:
+            if squared:
+                return sum((abs(x)**2 for x in args), ctx.zero)
+            return sum((abs(x) for x in args), ctx.zero)
+        if squared:
+            return sum((x**2 for x in args), ctx.zero)
+        return sum(args, ctx.zero)
+
+    def fdot(ctx, xs, ys=None, conjugate=False):
+        if ys is not None:
+            xs = zip(xs, ys)
+        if conjugate:
+            cf = ctx.conj
+            return sum((x*cf(y) for (x,y) in xs), ctx.zero)
+        else:
+            return sum((x*y for (x,y) in xs), ctx.zero)
+
+    def fprod(ctx, args):
+        prod = ctx.one
+        for arg in args:
+            prod *= arg
+        return prod
+
+    def nprint(ctx, x, n=6, **kwargs):
+        """
+        Equivalent to ``print(nstr(x, n))``.
+        """
+        print(ctx.nstr(x, n, **kwargs))
+
+    def chop(ctx, x, tol=None):
+        """
+        Chops off small real or imaginary parts, or converts
+        numbers close to zero to exact zeros. The input can be a
+        single number or an iterable::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> chop(5+1e-10j, tol=1e-9)
+            mpf('5.0')
+            >>> nprint(chop([1.0, 1e-20, 3+1e-18j, -4, 2]))
+            [1.0, 0.0, 3.0, -4.0, 2.0]
+
+        The tolerance defaults to ``100*eps``.
+        """
+        if tol is None:
+            tol = 100*ctx.eps
+        try:
+            x = ctx.convert(x)
+            absx = abs(x)
+            if abs(x) < tol:
+                return ctx.zero
+            if ctx._is_complex_type(x):
+                #part_tol = min(tol, absx*tol)
+                part_tol = max(tol, absx*tol)
+                if abs(x.imag) < part_tol:
+                    return x.real
+                if abs(x.real) < part_tol:
+                    return ctx.mpc(0, x.imag)
+        except TypeError:
+            if isinstance(x, ctx.matrix):
+                return x.apply(lambda a: ctx.chop(a, tol))
+            if hasattr(x, "__iter__"):
+                return [ctx.chop(a, tol) for a in x]
+        return x
+
+    def almosteq(ctx, s, t, rel_eps=None, abs_eps=None):
+        r"""
+        Determine whether the difference between `s` and `t` is smaller
+        than a given epsilon, either relatively or absolutely.
+
+        Both a maximum relative difference and a maximum difference
+        ('epsilons') may be specified. The absolute difference is
+        defined as `|s-t|` and the relative difference is defined
+        as `|s-t|/\max(|s|, |t|)`.
+
+        If only one epsilon is given, both are set to the same value.
+        If none is given, both epsilons are set to `2^{-p+m}` where
+        `p` is the current working precision and `m` is a small
+        integer. The default setting typically allows :func:`~mpmath.almosteq`
+        to be used to check for mathematical equality
+        in the presence of small rounding errors.
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15
+            >>> almosteq(3.141592653589793, 3.141592653589790)
+            True
+            >>> almosteq(3.141592653589793, 3.141592653589700)
+            False
+            >>> almosteq(3.141592653589793, 3.141592653589700, 1e-10)
+            True
+            >>> almosteq(1e-20, 2e-20)
+            True
+            >>> almosteq(1e-20, 2e-20, rel_eps=0, abs_eps=0)
+            False
+
+        """
+        t = ctx.convert(t)
+        if abs_eps is None and rel_eps is None:
+            rel_eps = abs_eps = ctx.ldexp(1, -ctx.prec+4)
+        if abs_eps is None:
+            abs_eps = rel_eps
+        elif rel_eps is None:
+            rel_eps = abs_eps
+        diff = abs(s-t)
+        if diff <= abs_eps:
+            return True
+        abss = abs(s)
+        abst = abs(t)
+        if abss < abst:
+            err = diff/abst
+        else:
+            err = diff/abss
+        return err <= rel_eps
+
+    def arange(ctx, *args):
+        r"""
+        This is a generalized version of Python's :func:`~mpmath.range` function
+        that accepts fractional endpoints and step sizes and
+        returns a list of ``mpf`` instances. Like :func:`~mpmath.range`,
+        :func:`~mpmath.arange` can be called with 1, 2 or 3 arguments:
+
+        ``arange(b)``
+            `[0, 1, 2, \ldots, x]`
+        ``arange(a, b)``
+            `[a, a+1, a+2, \ldots, x]`
+        ``arange(a, b, h)``
+            `[a, a+h, a+h, \ldots, x]`
+
+        where `b-1 \le x < b` (in the third case, `b-h \le x < b`).
+
+        Like Python's :func:`~mpmath.range`, the endpoint is not included. To
+        produce ranges where the endpoint is included, :func:`~mpmath.linspace`
+        is more convenient.
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> arange(4)
+            [mpf('0.0'), mpf('1.0'), mpf('2.0'), mpf('3.0')]
+            >>> arange(1, 2, 0.25)
+            [mpf('1.0'), mpf('1.25'), mpf('1.5'), mpf('1.75')]
+            >>> arange(1, -1, -0.75)
+            [mpf('1.0'), mpf('0.25'), mpf('-0.5')]
+
+        """
+        if not len(args) <= 3:
+            raise TypeError('arange expected at most 3 arguments, got %i'
+                            % len(args))
+        if not len(args) >= 1:
+            raise TypeError('arange expected at least 1 argument, got %i'
+                            % len(args))
+        # set default
+        a = 0
+        dt = 1
+        # interpret arguments
+        if len(args) == 1:
+            b = args[0]
+        elif len(args) >= 2:
+            a = args[0]
+            b = args[1]
+        if len(args) == 3:
+            dt = args[2]
+        a, b, dt = ctx.mpf(a), ctx.mpf(b), ctx.mpf(dt)
+        assert a + dt != a, 'dt is too small and would cause an infinite loop'
+        # adapt code for sign of dt
+        if a > b:
+            if dt > 0:
+                return []
+            op = gt
+        else:
+            if dt < 0:
+                return []
+            op = lt
+        # create list
+        result = []
+        i = 0
+        t = a
+        while 1:
+            t = a + dt*i
+            i += 1
+            if op(t, b):
+                result.append(t)
+            else:
+                break
+        return result
+
+    def linspace(ctx, *args, **kwargs):
+        """
+        ``linspace(a, b, n)`` returns a list of `n` evenly spaced
+        samples from `a` to `b`. The syntax ``linspace(mpi(a,b), n)``
+        is also valid.
+
+        This function is often more convenient than :func:`~mpmath.arange`
+        for partitioning an interval into subintervals, since
+        the endpoint is included::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> linspace(1, 4, 4)
+            [mpf('1.0'), mpf('2.0'), mpf('3.0'), mpf('4.0')]
+
+        You may also provide the keyword argument ``endpoint=False``::
+
+            >>> linspace(1, 4, 4, endpoint=False)
+            [mpf('1.0'), mpf('1.75'), mpf('2.5'), mpf('3.25')]
+
+        """
+        if len(args) == 3:
+            a = ctx.mpf(args[0])
+            b = ctx.mpf(args[1])
+            n = int(args[2])
+        elif len(args) == 2:
+            assert hasattr(args[0], '_mpi_')
+            a = args[0].a
+            b = args[0].b
+            n = int(args[1])
+        else:
+            raise TypeError('linspace expected 2 or 3 arguments, got %i' \
+                            % len(args))
+        if n < 1:
+            raise ValueError('n must be greater than 0')
+        if not 'endpoint' in kwargs or kwargs['endpoint']:
+            if n == 1:
+                return [ctx.mpf(a)]
+            step = (b - a) / ctx.mpf(n - 1)
+            y = [i*step + a for i in xrange(n)]
+            y[-1] = b
+        else:
+            step = (b - a) / ctx.mpf(n)
+            y = [i*step + a for i in xrange(n)]
+        return y
+
+    def cos_sin(ctx, z, **kwargs):
+        return ctx.cos(z, **kwargs), ctx.sin(z, **kwargs)
+
+    def cospi_sinpi(ctx, z, **kwargs):
+        return ctx.cospi(z, **kwargs), ctx.sinpi(z, **kwargs)
+
+    def _default_hyper_maxprec(ctx, p):
+        return int(1000 * p**0.25 + 4*p)
+
+    _gcd = staticmethod(libmp.gcd)
+    list_primes = staticmethod(libmp.list_primes)
+    isprime = staticmethod(libmp.isprime)
+    bernfrac = staticmethod(libmp.bernfrac)
+    moebius = staticmethod(libmp.moebius)
+    _ifac = staticmethod(libmp.ifac)
+    _eulernum = staticmethod(libmp.eulernum)
+    _stirling1 = staticmethod(libmp.stirling1)
+    _stirling2 = staticmethod(libmp.stirling2)
+
+    def sum_accurately(ctx, terms, check_step=1):
+        prec = ctx.prec
+        try:
+            extraprec = 10
+            while 1:
+                ctx.prec = prec + extraprec + 5
+                max_mag = ctx.ninf
+                s = ctx.zero
+                k = 0
+                for term in terms():
+                    s += term
+                    if (not k % check_step) and term:
+                        term_mag = ctx.mag(term)
+                        max_mag = max(max_mag, term_mag)
+                        sum_mag = ctx.mag(s)
+                        if sum_mag - term_mag > ctx.prec:
+                            break
+                    k += 1
+                cancellation = max_mag - sum_mag
+                if cancellation != cancellation:
+                    break
+                if cancellation < extraprec or ctx._fixed_precision:
+                    break
+                extraprec += min(ctx.prec, cancellation)
+            return s
+        finally:
+            ctx.prec = prec
+
+    def mul_accurately(ctx, factors, check_step=1):
+        prec = ctx.prec
+        try:
+            extraprec = 10
+            while 1:
+                ctx.prec = prec + extraprec + 5
+                max_mag = ctx.ninf
+                one = ctx.one
+                s = one
+                k = 0
+                for factor in factors():
+                    s *= factor
+                    term = factor - one
+                    if (not k % check_step):
+                        term_mag = ctx.mag(term)
+                        max_mag = max(max_mag, term_mag)
+                        sum_mag = ctx.mag(s-one)
+                        #if sum_mag - term_mag > ctx.prec:
+                        #    break
+                        if -term_mag > ctx.prec:
+                            break
+                    k += 1
+                cancellation = max_mag - sum_mag
+                if cancellation != cancellation:
+                    break
+                if cancellation < extraprec or ctx._fixed_precision:
+                    break
+                extraprec += min(ctx.prec, cancellation)
+            return s
+        finally:
+            ctx.prec = prec
+
+    def power(ctx, x, y):
+        r"""Converts `x` and `y` to mpmath numbers and evaluates
+        `x^y = \exp(y \log(x))`::
+
+            >>> from mpmath import *
+            >>> mp.dps = 30; mp.pretty = True
+            >>> power(2, 0.5)
+            1.41421356237309504880168872421
+
+        This shows the leading few digits of a large Mersenne prime
+        (performing the exact calculation ``2**43112609-1`` and
+        displaying the result in Python would be very slow)::
+
+            >>> power(2, 43112609)-1
+            3.16470269330255923143453723949e+12978188
+        """
+        return ctx.convert(x) ** ctx.convert(y)
+
+    def _zeta_int(ctx, n):
+        return ctx.zeta(n)
+
+    def maxcalls(ctx, f, N):
+        """
+        Return a wrapped copy of *f* that raises ``NoConvergence`` when *f*
+        has been called more than *N* times::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15
+            >>> f = maxcalls(sin, 10)
+            >>> print(sum(f(n) for n in range(10)))
+            1.95520948210738
+            >>> f(10) # doctest: +IGNORE_EXCEPTION_DETAIL
+            Traceback (most recent call last):
+              ...
+            NoConvergence: maxcalls: function evaluated 10 times
+
+        """
+        counter = [0]
+        def f_maxcalls_wrapped(*args, **kwargs):
+            counter[0] += 1
+            if counter[0] > N:
+                raise ctx.NoConvergence("maxcalls: function evaluated %i times" % N)
+            return f(*args, **kwargs)
+        return f_maxcalls_wrapped
+
+    def memoize(ctx, f):
+        """
+        Return a wrapped copy of *f* that caches computed values, i.e.
+        a memoized copy of *f*. Values are only reused if the cached precision
+        is equal to or higher than the working precision::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> f = memoize(maxcalls(sin, 1))
+            >>> f(2)
+            0.909297426825682
+            >>> f(2)
+            0.909297426825682
+            >>> mp.dps = 25
+            >>> f(2) # doctest: +IGNORE_EXCEPTION_DETAIL
+            Traceback (most recent call last):
+              ...
+            NoConvergence: maxcalls: function evaluated 1 times
+
+        """
+        f_cache = {}
+        def f_cached(*args, **kwargs):
+            if kwargs:
+                key = args, tuple(kwargs.items())
+            else:
+                key = args
+            prec = ctx.prec
+            if key in f_cache:
+                cprec, cvalue = f_cache[key]
+                if cprec >= prec:
+                    return +cvalue
+            value = f(*args, **kwargs)
+            f_cache[key] = (prec, value)
+            return value
+        f_cached.__name__ = f.__name__
+        f_cached.__doc__ = f.__doc__
+        return f_cached

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/ctx_fp.py

@@ -0,0 +1,253 @@
+from .ctx_base import StandardBaseContext
+
+import math
+import cmath
+from . import math2
+
+from . import function_docs
+
+from .libmp import mpf_bernoulli, to_float, int_types
+from . import libmp
+
+class FPContext(StandardBaseContext):
+    """
+    Context for fast low-precision arithmetic (53-bit precision, giving at most
+    about 15-digit accuracy), using Python's builtin float and complex.
+    """
+
+    def __init__(ctx):
+        StandardBaseContext.__init__(ctx)
+
+        # Override SpecialFunctions implementation
+        ctx.loggamma = math2.loggamma
+        ctx._bernoulli_cache = {}
+        ctx.pretty = False
+
+        ctx._init_aliases()
+
+    _mpq = lambda cls, x: float(x[0])/x[1]
+
+    NoConvergence = libmp.NoConvergence
+
+    def _get_prec(ctx): return 53
+    def _set_prec(ctx, p): return
+    def _get_dps(ctx): return 15
+    def _set_dps(ctx, p): return
+
+    _fixed_precision = True
+
+    prec = property(_get_prec, _set_prec)
+    dps = property(_get_dps, _set_dps)
+
+    zero = 0.0
+    one = 1.0
+    eps = math2.EPS
+    inf = math2.INF
+    ninf = math2.NINF
+    nan = math2.NAN
+    j = 1j
+
+    # Called by SpecialFunctions.__init__()
+    @classmethod
+    def _wrap_specfun(cls, name, f, wrap):
+        if wrap:
+            def f_wrapped(ctx, *args, **kwargs):
+                convert = ctx.convert
+                args = [convert(a) for a in args]
+                return f(ctx, *args, **kwargs)
+        else:
+            f_wrapped = f
+        f_wrapped.__doc__ = function_docs.__dict__.get(name, f.__doc__)
+        setattr(cls, name, f_wrapped)
+
+    def bernoulli(ctx, n):
+        cache = ctx._bernoulli_cache
+        if n in cache:
+            return cache[n]
+        cache[n] = to_float(mpf_bernoulli(n, 53, 'n'), strict=True)
+        return cache[n]
+
+    pi = math2.pi
+    e = math2.e
+    euler = math2.euler
+    sqrt2 = 1.4142135623730950488
+    sqrt5 = 2.2360679774997896964
+    phi = 1.6180339887498948482
+    ln2 = 0.69314718055994530942
+    ln10 = 2.302585092994045684
+    euler = 0.57721566490153286061
+    catalan = 0.91596559417721901505
+    khinchin = 2.6854520010653064453
+    apery = 1.2020569031595942854
+    glaisher = 1.2824271291006226369
+
+    absmin = absmax = abs
+
+    def is_special(ctx, x):
+        return x - x != 0.0
+
+    def isnan(ctx, x):
+        return x != x
+
+    def isinf(ctx, x):
+        return abs(x) == math2.INF
+
+    def isnormal(ctx, x):
+        if x:
+            return x - x == 0.0
+        return False
+
+    def isnpint(ctx, x):
+        if type(x) is complex:
+            if x.imag:
+                return False
+            x = x.real
+        return x <= 0.0 and round(x) == x
+
+    mpf = float
+    mpc = complex
+
+    def convert(ctx, x):
+        try:
+            return float(x)
+        except:
+            return complex(x)
+
+    power = staticmethod(math2.pow)
+    sqrt = staticmethod(math2.sqrt)
+    exp = staticmethod(math2.exp)
+    ln = log = staticmethod(math2.log)
+    cos = staticmethod(math2.cos)
+    sin = staticmethod(math2.sin)
+    tan = staticmethod(math2.tan)
+    cos_sin = staticmethod(math2.cos_sin)
+    acos = staticmethod(math2.acos)
+    asin = staticmethod(math2.asin)
+    atan = staticmethod(math2.atan)
+    cosh = staticmethod(math2.cosh)
+    sinh = staticmethod(math2.sinh)
+    tanh = staticmethod(math2.tanh)
+    gamma = staticmethod(math2.gamma)
+    rgamma = staticmethod(math2.rgamma)
+    fac = factorial = staticmethod(math2.factorial)
+    floor = staticmethod(math2.floor)
+    ceil = staticmethod(math2.ceil)
+    cospi = staticmethod(math2.cospi)
+    sinpi = staticmethod(math2.sinpi)
+    cbrt = staticmethod(math2.cbrt)
+    _nthroot = staticmethod(math2.nthroot)
+    _ei = staticmethod(math2.ei)
+    _e1 = staticmethod(math2.e1)
+    _zeta = _zeta_int = staticmethod(math2.zeta)
+
+    # XXX: math2
+    def arg(ctx, z):
+        z = complex(z)
+        return math.atan2(z.imag, z.real)
+
+    def expj(ctx, x):
+        return ctx.exp(ctx.j*x)
+
+    def expjpi(ctx, x):
+        return ctx.exp(ctx.j*ctx.pi*x)
+
+    ldexp = math.ldexp
+    frexp = math.frexp
+
+    def mag(ctx, z):
+        if z:
+            return ctx.frexp(abs(z))[1]
+        return ctx.ninf
+
+    def isint(ctx, z):
+        if hasattr(z, "imag"):   # float/int don't have .real/.imag in py2.5
+            if z.imag:
+                return False
+            z = z.real
+        try:
+            return z == int(z)
+        except:
+            return False
+
+    def nint_distance(ctx, z):
+        if hasattr(z, "imag"):   # float/int don't have .real/.imag in py2.5
+            n = round(z.real)
+        else:
+            n = round(z)
+        if n == z:
+            return n, ctx.ninf
+        return n, ctx.mag(abs(z-n))
+
+    def _convert_param(ctx, z):
+        if type(z) is tuple:
+            p, q = z
+            return ctx.mpf(p) / q, 'R'
+        if hasattr(z, "imag"):    # float/int don't have .real/.imag in py2.5
+            intz = int(z.real)
+        else:
+            intz = int(z)
+        if z == intz:
+            return intz, 'Z'
+        return z, 'R'
+
+    def _is_real_type(ctx, z):
+        return isinstance(z, float) or isinstance(z, int_types)
+
+    def _is_complex_type(ctx, z):
+        return isinstance(z, complex)
+
+    def hypsum(ctx, p, q, types, coeffs, z, maxterms=6000, **kwargs):
+        coeffs = list(coeffs)
+        num = range(p)
+        den = range(p,p+q)
+        tol = ctx.eps
+        s = t = 1.0
+        k = 0
+        while 1:
+            for i in num: t *= (coeffs[i]+k)
+            for i in den: t /= (coeffs[i]+k)
+            k += 1; t /= k; t *= z; s += t
+            if abs(t) < tol:
+                return s
+            if k > maxterms:
+                raise ctx.NoConvergence
+
+    def atan2(ctx, x, y):
+        return math.atan2(x, y)
+
+    def psi(ctx, m, z):
+        m = int(m)
+        if m == 0:
+            return ctx.digamma(z)
+        return (-1)**(m+1) * ctx.fac(m) * ctx.zeta(m+1, z)
+
+    digamma = staticmethod(math2.digamma)
+
+    def harmonic(ctx, x):
+        x = ctx.convert(x)
+        if x == 0 or x == 1:
+            return x
+        return ctx.digamma(x+1) + ctx.euler
+
+    nstr = str
+
+    def to_fixed(ctx, x, prec):
+        return int(math.ldexp(x, prec))
+
+    def rand(ctx):
+        import random
+        return random.random()
+
+    _erf = staticmethod(math2.erf)
+    _erfc = staticmethod(math2.erfc)
+
+    def sum_accurately(ctx, terms, check_step=1):
+        s = ctx.zero
+        k = 0
+        for term in terms():
+            s += term
+            if (not k % check_step) and term:
+                if abs(term) <= 1e-18*abs(s):
+                    break
+            k += 1
+        return s

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/ctx_iv.py

@@ -0,0 +1,551 @@
+import operator
+
+from . import libmp
+
+from .libmp.backend import basestring
+
+from .libmp import (
+    int_types, MPZ_ONE,
+    prec_to_dps, dps_to_prec, repr_dps,
+    round_floor, round_ceiling,
+    fzero, finf, fninf, fnan,
+    mpf_le, mpf_neg,
+    from_int, from_float, from_str, from_rational,
+    mpi_mid, mpi_delta, mpi_str,
+    mpi_abs, mpi_pos, mpi_neg, mpi_add, mpi_sub,
+    mpi_mul, mpi_div, mpi_pow_int, mpi_pow,
+    mpi_from_str,
+    mpci_pos, mpci_neg, mpci_add, mpci_sub, mpci_mul, mpci_div, mpci_pow,
+    mpci_abs, mpci_pow, mpci_exp, mpci_log,
+    ComplexResult,
+    mpf_hash, mpc_hash)
+from .matrices.matrices import _matrix
+
+mpi_zero = (fzero, fzero)
+
+from .ctx_base import StandardBaseContext
+
+new = object.__new__
+
+def convert_mpf_(x, prec, rounding):
+    if hasattr(x, "_mpf_"): return x._mpf_
+    if isinstance(x, int_types): return from_int(x, prec, rounding)
+    if isinstance(x, float): return from_float(x, prec, rounding)
+    if isinstance(x, basestring): return from_str(x, prec, rounding)
+    raise NotImplementedError
+
+
+class ivmpf(object):
+    """
+    Interval arithmetic class. Precision is controlled by iv.prec.
+    """
+
+    def __new__(cls, x=0):
+        return cls.ctx.convert(x)
+
+    def cast(self, cls, f_convert):
+        a, b = self._mpi_
+        if a == b:
+            return cls(f_convert(a))
+        raise ValueError
+
+    def __int__(self):
+        return self.cast(int, libmp.to_int)
+
+    def __float__(self):
+        return self.cast(float, libmp.to_float)
+
+    def __complex__(self):
+        return self.cast(complex, libmp.to_float)
+
+    def __hash__(self):
+        a, b = self._mpi_
+        if a == b:
+            return mpf_hash(a)
+        else:
+            return hash(self._mpi_)
+
+    @property
+    def real(self): return self
+
+    @property
+    def imag(self): return self.ctx.zero
+
+    def conjugate(self): return self
+
+    @property
+    def a(self):
+        a, b = self._mpi_
+        return self.ctx.make_mpf((a, a))
+
+    @property
+    def b(self):
+        a, b = self._mpi_
+        return self.ctx.make_mpf((b, b))
+
+    @property
+    def mid(self):
+        ctx = self.ctx
+        v = mpi_mid(self._mpi_, ctx.prec)
+        return ctx.make_mpf((v, v))
+
+    @property
+    def delta(self):
+        ctx = self.ctx
+        v = mpi_delta(self._mpi_, ctx.prec)
+        return ctx.make_mpf((v,v))
+
+    @property
+    def _mpci_(self):
+        return self._mpi_, mpi_zero
+
+    def _compare(*args):
+        raise TypeError("no ordering relation is defined for intervals")
+
+    __gt__ = _compare
+    __le__ = _compare
+    __gt__ = _compare
+    __ge__ = _compare
+
+    def __contains__(self, t):
+        t = self.ctx.mpf(t)
+        return (self.a <= t.a) and (t.b <= self.b)
+
+    def __str__(self):
+        return mpi_str(self._mpi_, self.ctx.prec)
+
+    def __repr__(self):
+        if self.ctx.pretty:
+            return str(self)
+        a, b = self._mpi_
+        n = repr_dps(self.ctx.prec)
+        a = libmp.to_str(a, n)
+        b = libmp.to_str(b, n)
+        return "mpi(%r, %r)" % (a, b)
+
+    def _compare(s, t, cmpfun):
+        if not hasattr(t, "_mpi_"):
+            try:
+                t = s.ctx.convert(t)
+            except:
+                return NotImplemented
+        return cmpfun(s._mpi_, t._mpi_)
+
+    def __eq__(s, t): return s._compare(t, libmp.mpi_eq)
+    def __ne__(s, t): return s._compare(t, libmp.mpi_ne)
+    def __lt__(s, t): return s._compare(t, libmp.mpi_lt)
+    def __le__(s, t): return s._compare(t, libmp.mpi_le)
+    def __gt__(s, t): return s._compare(t, libmp.mpi_gt)
+    def __ge__(s, t): return s._compare(t, libmp.mpi_ge)
+
+    def __abs__(self):
+        return self.ctx.make_mpf(mpi_abs(self._mpi_, self.ctx.prec))
+    def __pos__(self):
+        return self.ctx.make_mpf(mpi_pos(self._mpi_, self.ctx.prec))
+    def __neg__(self):
+        return self.ctx.make_mpf(mpi_neg(self._mpi_, self.ctx.prec))
+
+    def ae(s, t, rel_eps=None, abs_eps=None):
+        return s.ctx.almosteq(s, t, rel_eps, abs_eps)
+
+class ivmpc(object):
+
+    def __new__(cls, re=0, im=0):
+        re = cls.ctx.convert(re)
+        im = cls.ctx.convert(im)
+        y = new(cls)
+        y._mpci_ = re._mpi_, im._mpi_
+        return y
+
+    def __hash__(self):
+        (a, b), (c,d) = self._mpci_
+        if a == b and c == d:
+            return mpc_hash((a, c))
+        else:
+            return hash(self._mpci_)
+
+    def __repr__(s):
+        if s.ctx.pretty:
+            return str(s)
+        return "iv.mpc(%s, %s)" % (repr(s.real), repr(s.imag))
+
+    def __str__(s):
+        return "(%s + %s*j)" % (str(s.real), str(s.imag))
+
+    @property
+    def a(self):
+        (a, b), (c,d) = self._mpci_
+        return self.ctx.make_mpf((a, a))
+
+    @property
+    def b(self):
+        (a, b), (c,d) = self._mpci_
+        return self.ctx.make_mpf((b, b))
+
+    @property
+    def c(self):
+        (a, b), (c,d) = self._mpci_
+        return self.ctx.make_mpf((c, c))
+
+    @property
+    def d(self):
+        (a, b), (c,d) = self._mpci_
+        return self.ctx.make_mpf((d, d))
+
+    @property
+    def real(s):
+        return s.ctx.make_mpf(s._mpci_[0])
+
+    @property
+    def imag(s):
+        return s.ctx.make_mpf(s._mpci_[1])
+
+    def conjugate(s):
+        a, b = s._mpci_
+        return s.ctx.make_mpc((a, mpf_neg(b)))
+
+    def overlap(s, t):
+        t = s.ctx.convert(t)
+        real_overlap = (s.a <= t.a <= s.b) or (s.a <= t.b <= s.b) or (t.a <= s.a <= t.b) or (t.a <= s.b <= t.b)
+        imag_overlap = (s.c <= t.c <= s.d) or (s.c <= t.d <= s.d) or (t.c <= s.c <= t.d) or (t.c <= s.d <= t.d)
+        return real_overlap and imag_overlap
+
+    def __contains__(s, t):
+        t = s.ctx.convert(t)
+        return t.real in s.real and t.imag in s.imag
+
+    def _compare(s, t, ne=False):
+        if not isinstance(t, s.ctx._types):
+            try:
+                t = s.ctx.convert(t)
+            except:
+                return NotImplemented
+        if hasattr(t, '_mpi_'):
+            tval = t._mpi_, mpi_zero
+        elif hasattr(t, '_mpci_'):
+            tval = t._mpci_
+        if ne:
+            return s._mpci_ != tval
+        return s._mpci_ == tval
+
+    def __eq__(s, t): return s._compare(t)
+    def __ne__(s, t): return s._compare(t, True)
+
+    def __lt__(s, t): raise TypeError("complex intervals cannot be ordered")
+    __le__ = __gt__ = __ge__ = __lt__
+
+    def __neg__(s): return s.ctx.make_mpc(mpci_neg(s._mpci_, s.ctx.prec))
+    def __pos__(s): return s.ctx.make_mpc(mpci_pos(s._mpci_, s.ctx.prec))
+    def __abs__(s): return s.ctx.make_mpf(mpci_abs(s._mpci_, s.ctx.prec))
+
+    def ae(s, t, rel_eps=None, abs_eps=None):
+        return s.ctx.almosteq(s, t, rel_eps, abs_eps)
+
+def _binary_op(f_real, f_complex):
+    def g_complex(ctx, sval, tval):
+        return ctx.make_mpc(f_complex(sval, tval, ctx.prec))
+    def g_real(ctx, sval, tval):
+        try:
+            return ctx.make_mpf(f_real(sval, tval, ctx.prec))
+        except ComplexResult:
+            sval = (sval, mpi_zero)
+            tval = (tval, mpi_zero)
+            return g_complex(ctx, sval, tval)
+    def lop_real(s, t):
+        if isinstance(t, _matrix): return NotImplemented
+        ctx = s.ctx
+        if not isinstance(t, ctx._types): t = ctx.convert(t)
+        if hasattr(t, "_mpi_"): return g_real(ctx, s._mpi_, t._mpi_)
+        if hasattr(t, "_mpci_"): return g_complex(ctx, (s._mpi_, mpi_zero), t._mpci_)
+        return NotImplemented
+    def rop_real(s, t):
+        ctx = s.ctx
+        if not isinstance(t, ctx._types): t = ctx.convert(t)
+        if hasattr(t, "_mpi_"): return g_real(ctx, t._mpi_, s._mpi_)
+        if hasattr(t, "_mpci_"): return g_complex(ctx, t._mpci_, (s._mpi_, mpi_zero))
+        return NotImplemented
+    def lop_complex(s, t):
+        if isinstance(t, _matrix): return NotImplemented
+        ctx = s.ctx
+        if not isinstance(t, s.ctx._types):
+            try:
+                t = s.ctx.convert(t)
+            except (ValueError, TypeError):
+                return NotImplemented
+        return g_complex(ctx, s._mpci_, t._mpci_)
+    def rop_complex(s, t):
+        ctx = s.ctx
+        if not isinstance(t, s.ctx._types):
+            t = s.ctx.convert(t)
+        return g_complex(ctx, t._mpci_, s._mpci_)
+    return lop_real, rop_real, lop_complex, rop_complex
+
+ivmpf.__add__, ivmpf.__radd__, ivmpc.__add__, ivmpc.__radd__ = _binary_op(mpi_add, mpci_add)
+ivmpf.__sub__, ivmpf.__rsub__, ivmpc.__sub__, ivmpc.__rsub__ = _binary_op(mpi_sub, mpci_sub)
+ivmpf.__mul__, ivmpf.__rmul__, ivmpc.__mul__, ivmpc.__rmul__ = _binary_op(mpi_mul, mpci_mul)
+ivmpf.__div__, ivmpf.__rdiv__, ivmpc.__div__, ivmpc.__rdiv__ = _binary_op(mpi_div, mpci_div)
+ivmpf.__pow__, ivmpf.__rpow__, ivmpc.__pow__, ivmpc.__rpow__ = _binary_op(mpi_pow, mpci_pow)
+
+ivmpf.__truediv__ = ivmpf.__div__; ivmpf.__rtruediv__ = ivmpf.__rdiv__
+ivmpc.__truediv__ = ivmpc.__div__; ivmpc.__rtruediv__ = ivmpc.__rdiv__
+
+class ivmpf_constant(ivmpf):
+    def __new__(cls, f):
+        self = new(cls)
+        self._f = f
+        return self
+    def _get_mpi_(self):
+        prec = self.ctx._prec[0]
+        a = self._f(prec, round_floor)
+        b = self._f(prec, round_ceiling)
+        return a, b
+    _mpi_ = property(_get_mpi_)
+
+class MPIntervalContext(StandardBaseContext):
+
+    def __init__(ctx):
+        ctx.mpf = type('ivmpf', (ivmpf,), {})
+        ctx.mpc = type('ivmpc', (ivmpc,), {})
+        ctx._types = (ctx.mpf, ctx.mpc)
+        ctx._constant = type('ivmpf_constant', (ivmpf_constant,), {})
+        ctx._prec = [53]
+        ctx._set_prec(53)
+        ctx._constant._ctxdata = ctx.mpf._ctxdata = ctx.mpc._ctxdata = [ctx.mpf, new, ctx._prec]
+        ctx._constant.ctx = ctx.mpf.ctx = ctx.mpc.ctx = ctx
+        ctx.pretty = False
+        StandardBaseContext.__init__(ctx)
+        ctx._init_builtins()
+
+    def _mpi(ctx, a, b=None):
+        if b is None:
+            return ctx.mpf(a)
+        return ctx.mpf((a,b))
+
+    def _init_builtins(ctx):
+        ctx.one = ctx.mpf(1)
+        ctx.zero = ctx.mpf(0)
+        ctx.inf = ctx.mpf('inf')
+        ctx.ninf = -ctx.inf
+        ctx.nan = ctx.mpf('nan')
+        ctx.j = ctx.mpc(0,1)
+        ctx.exp = ctx._wrap_mpi_function(libmp.mpi_exp, libmp.mpci_exp)
+        ctx.sqrt = ctx._wrap_mpi_function(libmp.mpi_sqrt)
+        ctx.ln = ctx._wrap_mpi_function(libmp.mpi_log, libmp.mpci_log)
+        ctx.cos = ctx._wrap_mpi_function(libmp.mpi_cos, libmp.mpci_cos)
+        ctx.sin = ctx._wrap_mpi_function(libmp.mpi_sin, libmp.mpci_sin)
+        ctx.tan = ctx._wrap_mpi_function(libmp.mpi_tan)
+        ctx.gamma = ctx._wrap_mpi_function(libmp.mpi_gamma, libmp.mpci_gamma)
+        ctx.loggamma = ctx._wrap_mpi_function(libmp.mpi_loggamma, libmp.mpci_loggamma)
+        ctx.rgamma = ctx._wrap_mpi_function(libmp.mpi_rgamma, libmp.mpci_rgamma)
+        ctx.factorial = ctx._wrap_mpi_function(libmp.mpi_factorial, libmp.mpci_factorial)
+        ctx.fac = ctx.factorial
+
+        ctx.eps = ctx._constant(lambda prec, rnd: (0, MPZ_ONE, 1-prec, 1))
+        ctx.pi = ctx._constant(libmp.mpf_pi)
+        ctx.e = ctx._constant(libmp.mpf_e)
+        ctx.ln2 = ctx._constant(libmp.mpf_ln2)
+        ctx.ln10 = ctx._constant(libmp.mpf_ln10)
+        ctx.phi = ctx._constant(libmp.mpf_phi)
+        ctx.euler = ctx._constant(libmp.mpf_euler)
+        ctx.catalan = ctx._constant(libmp.mpf_catalan)
+        ctx.glaisher = ctx._constant(libmp.mpf_glaisher)
+        ctx.khinchin = ctx._constant(libmp.mpf_khinchin)
+        ctx.twinprime = ctx._constant(libmp.mpf_twinprime)
+
+    def _wrap_mpi_function(ctx, f_real, f_complex=None):
+        def g(x, **kwargs):
+            if kwargs:
+                prec = kwargs.get('prec', ctx._prec[0])
+            else:
+                prec = ctx._prec[0]
+            x = ctx.convert(x)
+            if hasattr(x, "_mpi_"):
+                return ctx.make_mpf(f_real(x._mpi_, prec))
+            if hasattr(x, "_mpci_"):
+                return ctx.make_mpc(f_complex(x._mpci_, prec))
+            raise ValueError
+        return g
+
+    @classmethod
+    def _wrap_specfun(cls, name, f, wrap):
+        if wrap:
+            def f_wrapped(ctx, *args, **kwargs):
+                convert = ctx.convert
+                args = [convert(a) for a in args]
+                prec = ctx.prec
+                try:
+                    ctx.prec += 10
+                    retval = f(ctx, *args, **kwargs)
+                finally:
+                    ctx.prec = prec
+                return +retval
+        else:
+            f_wrapped = f
+        setattr(cls, name, f_wrapped)
+
+    def _set_prec(ctx, n):
+        ctx._prec[0] = max(1, int(n))
+        ctx._dps = prec_to_dps(n)
+
+    def _set_dps(ctx, n):
+        ctx._prec[0] = dps_to_prec(n)
+        ctx._dps = max(1, int(n))
+
+    prec = property(lambda ctx: ctx._prec[0], _set_prec)
+    dps = property(lambda ctx: ctx._dps, _set_dps)
+
+    def make_mpf(ctx, v):
+        a = new(ctx.mpf)
+        a._mpi_ = v
+        return a
+
+    def make_mpc(ctx, v):
+        a = new(ctx.mpc)
+        a._mpci_ = v
+        return a
+
+    def _mpq(ctx, pq):
+        p, q = pq
+        a = libmp.from_rational(p, q, ctx.prec, round_floor)
+        b = libmp.from_rational(p, q, ctx.prec, round_ceiling)
+        return ctx.make_mpf((a, b))
+
+    def convert(ctx, x):
+        if isinstance(x, (ctx.mpf, ctx.mpc)):
+            return x
+        if isinstance(x, ctx._constant):
+            return +x
+        if isinstance(x, complex) or hasattr(x, "_mpc_"):
+            re = ctx.convert(x.real)
+            im = ctx.convert(x.imag)
+            return ctx.mpc(re,im)
+        if isinstance(x, basestring):
+            v = mpi_from_str(x, ctx.prec)
+            return ctx.make_mpf(v)
+        if hasattr(x, "_mpi_"):
+            a, b = x._mpi_
+        else:
+            try:
+                a, b = x
+            except (TypeError, ValueError):
+                a = b = x
+            if hasattr(a, "_mpi_"):
+                a = a._mpi_[0]
+            else:
+                a = convert_mpf_(a, ctx.prec, round_floor)
+            if hasattr(b, "_mpi_"):
+                b = b._mpi_[1]
+            else:
+                b = convert_mpf_(b, ctx.prec, round_ceiling)
+        if a == fnan or b == fnan:
+            a = fninf
+            b = finf
+        assert mpf_le(a, b), "endpoints must be properly ordered"
+        return ctx.make_mpf((a, b))
+
+    def nstr(ctx, x, n=5, **kwargs):
+        x = ctx.convert(x)
+        if hasattr(x, "_mpi_"):
+            return libmp.mpi_to_str(x._mpi_, n, **kwargs)
+        if hasattr(x, "_mpci_"):
+            re = libmp.mpi_to_str(x._mpci_[0], n, **kwargs)
+            im = libmp.mpi_to_str(x._mpci_[1], n, **kwargs)
+            return "(%s + %s*j)" % (re, im)
+
+    def mag(ctx, x):
+        x = ctx.convert(x)
+        if isinstance(x, ctx.mpc):
+            return max(ctx.mag(x.real), ctx.mag(x.imag)) + 1
+        a, b = libmp.mpi_abs(x._mpi_)
+        sign, man, exp, bc = b
+        if man:
+            return exp+bc
+        if b == fzero:
+            return ctx.ninf
+        if b == fnan:
+            return ctx.nan
+        return ctx.inf
+
+    def isnan(ctx, x):
+        return False
+
+    def isinf(ctx, x):
+        return x == ctx.inf
+
+    def isint(ctx, x):
+        x = ctx.convert(x)
+        a, b = x._mpi_
+        if a == b:
+            sign, man, exp, bc = a
+            if man:
+                return exp >= 0
+            return a == fzero
+        return None
+
+    def ldexp(ctx, x, n):
+        a, b = ctx.convert(x)._mpi_
+        a = libmp.mpf_shift(a, n)
+        b = libmp.mpf_shift(b, n)
+        return ctx.make_mpf((a,b))
+
+    def absmin(ctx, x):
+        return abs(ctx.convert(x)).a
+
+    def absmax(ctx, x):
+        return abs(ctx.convert(x)).b
+
+    def atan2(ctx, y, x):
+        y = ctx.convert(y)._mpi_
+        x = ctx.convert(x)._mpi_
+        return ctx.make_mpf(libmp.mpi_atan2(y,x,ctx.prec))
+
+    def _convert_param(ctx, x):
+        if isinstance(x, libmp.int_types):
+            return x, 'Z'
+        if isinstance(x, tuple):
+            p, q = x
+            return (ctx.mpf(p) / ctx.mpf(q), 'R')
+        x = ctx.convert(x)
+        if isinstance(x, ctx.mpf):
+            return x, 'R'
+        if isinstance(x, ctx.mpc):
+            return x, 'C'
+        raise ValueError
+
+    def _is_real_type(ctx, z):
+        return isinstance(z, ctx.mpf) or isinstance(z, int_types)
+
+    def _is_complex_type(ctx, z):
+        return isinstance(z, ctx.mpc)
+
+    def hypsum(ctx, p, q, types, coeffs, z, maxterms=6000, **kwargs):
+        coeffs = list(coeffs)
+        num = range(p)
+        den = range(p,p+q)
+        #tol = ctx.eps
+        s = t = ctx.one
+        k = 0
+        while 1:
+            for i in num: t *= (coeffs[i]+k)
+            for i in den: t /= (coeffs[i]+k)
+            k += 1; t /= k; t *= z; s += t
+            if t == 0:
+                return s
+            #if abs(t) < tol:
+            #    return s
+            if k > maxterms:
+                raise ctx.NoConvergence
+
+
+# Register with "numbers" ABC
+#     We do not subclass, hence we do not use the @abstractmethod checks. While
+#     this is less invasive it may turn out that we do not actually support
+#     parts of the expected interfaces.  See
+#     http://docs.python.org/2/library/numbers.html for list of abstract
+#     methods.
+try:
+    import numbers
+    numbers.Complex.register(ivmpc)
+    numbers.Real.register(ivmpf)
+except ImportError:
+    pass

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/ctx_mp.py

@@ -0,0 +1,1339 @@
+"""
+This module defines the mpf, mpc classes, and standard functions for
+operating with them.
+"""
+__docformat__ = 'plaintext'
+
+import functools
+
+import re
+
+from .ctx_base import StandardBaseContext
+
+from .libmp.backend import basestring, BACKEND
+
+from . import libmp
+
+from .libmp import (MPZ, MPZ_ZERO, MPZ_ONE, int_types, repr_dps,
+    round_floor, round_ceiling, dps_to_prec, round_nearest, prec_to_dps,
+    ComplexResult, to_pickable, from_pickable, normalize,
+    from_int, from_float, from_str, to_int, to_float, to_str,
+    from_rational, from_man_exp,
+    fone, fzero, finf, fninf, fnan,
+    mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_mul_int,
+    mpf_div, mpf_rdiv_int, mpf_pow_int, mpf_mod,
+    mpf_eq, mpf_cmp, mpf_lt, mpf_gt, mpf_le, mpf_ge,
+    mpf_hash, mpf_rand,
+    mpf_sum,
+    bitcount, to_fixed,
+    mpc_to_str,
+    mpc_to_complex, mpc_hash, mpc_pos, mpc_is_nonzero, mpc_neg, mpc_conjugate,
+    mpc_abs, mpc_add, mpc_add_mpf, mpc_sub, mpc_sub_mpf, mpc_mul, mpc_mul_mpf,
+    mpc_mul_int, mpc_div, mpc_div_mpf, mpc_pow, mpc_pow_mpf, mpc_pow_int,
+    mpc_mpf_div,
+    mpf_pow,
+    mpf_pi, mpf_degree, mpf_e, mpf_phi, mpf_ln2, mpf_ln10,
+    mpf_euler, mpf_catalan, mpf_apery, mpf_khinchin,
+    mpf_glaisher, mpf_twinprime, mpf_mertens,
+    int_types)
+
+from . import function_docs
+from . import rational
+
+new = object.__new__
+
+get_complex = re.compile(r'^\(?(?P<re>[\+\-]?\d*(\.\d*)?(e[\+\-]?\d+)?)??'
+                         r'(?P<im>[\+\-]?\d*(\.\d*)?(e[\+\-]?\d+)?j)?\)?$')
+
+if BACKEND == 'sage':
+    from sage.libs.mpmath.ext_main import Context as BaseMPContext
+    # pickle hack
+    import sage.libs.mpmath.ext_main as _mpf_module
+else:
+    from .ctx_mp_python import PythonMPContext as BaseMPContext
+    from . import ctx_mp_python as _mpf_module
+
+from .ctx_mp_python import _mpf, _mpc, mpnumeric
+
+class MPContext(BaseMPContext, StandardBaseContext):
+    """
+    Context for multiprecision arithmetic with a global precision.
+    """
+
+    def __init__(ctx):
+        BaseMPContext.__init__(ctx)
+        ctx.trap_complex = False
+        ctx.pretty = False
+        ctx.types = [ctx.mpf, ctx.mpc, ctx.constant]
+        ctx._mpq = rational.mpq
+        ctx.default()
+        StandardBaseContext.__init__(ctx)
+
+        ctx.mpq = rational.mpq
+        ctx.init_builtins()
+
+        ctx.hyp_summators = {}
+
+        ctx._init_aliases()
+
+        # XXX: automate
+        try:
+            ctx.bernoulli.im_func.func_doc = function_docs.bernoulli
+            ctx.primepi.im_func.func_doc = function_docs.primepi
+            ctx.psi.im_func.func_doc = function_docs.psi
+            ctx.atan2.im_func.func_doc = function_docs.atan2
+        except AttributeError:
+            # python 3
+            ctx.bernoulli.__func__.func_doc = function_docs.bernoulli
+            ctx.primepi.__func__.func_doc = function_docs.primepi
+            ctx.psi.__func__.func_doc = function_docs.psi
+            ctx.atan2.__func__.func_doc = function_docs.atan2
+
+        ctx.digamma.func_doc = function_docs.digamma
+        ctx.cospi.func_doc = function_docs.cospi
+        ctx.sinpi.func_doc = function_docs.sinpi
+
+    def init_builtins(ctx):
+
+        mpf = ctx.mpf
+        mpc = ctx.mpc
+
+        # Exact constants
+        ctx.one = ctx.make_mpf(fone)
+        ctx.zero = ctx.make_mpf(fzero)
+        ctx.j = ctx.make_mpc((fzero,fone))
+        ctx.inf = ctx.make_mpf(finf)
+        ctx.ninf = ctx.make_mpf(fninf)
+        ctx.nan = ctx.make_mpf(fnan)
+
+        eps = ctx.constant(lambda prec, rnd: (0, MPZ_ONE, 1-prec, 1),
+            "epsilon of working precision", "eps")
+        ctx.eps = eps
+
+        # Approximate constants
+        ctx.pi = ctx.constant(mpf_pi, "pi", "pi")
+        ctx.ln2 = ctx.constant(mpf_ln2, "ln(2)", "ln2")
+        ctx.ln10 = ctx.constant(mpf_ln10, "ln(10)", "ln10")
+        ctx.phi = ctx.constant(mpf_phi, "Golden ratio phi", "phi")
+        ctx.e = ctx.constant(mpf_e, "e = exp(1)", "e")
+        ctx.euler = ctx.constant(mpf_euler, "Euler's constant", "euler")
+        ctx.catalan = ctx.constant(mpf_catalan, "Catalan's constant", "catalan")
+        ctx.khinchin = ctx.constant(mpf_khinchin, "Khinchin's constant", "khinchin")
+        ctx.glaisher = ctx.constant(mpf_glaisher, "Glaisher's constant", "glaisher")
+        ctx.apery = ctx.constant(mpf_apery, "Apery's constant", "apery")
+        ctx.degree = ctx.constant(mpf_degree, "1 deg = pi / 180", "degree")
+        ctx.twinprime = ctx.constant(mpf_twinprime, "Twin prime constant", "twinprime")
+        ctx.mertens = ctx.constant(mpf_mertens, "Mertens' constant", "mertens")
+
+        # Standard functions
+        ctx.sqrt = ctx._wrap_libmp_function(libmp.mpf_sqrt, libmp.mpc_sqrt)
+        ctx.cbrt = ctx._wrap_libmp_function(libmp.mpf_cbrt, libmp.mpc_cbrt)
+        ctx.ln = ctx._wrap_libmp_function(libmp.mpf_log, libmp.mpc_log)
+        ctx.atan = ctx._wrap_libmp_function(libmp.mpf_atan, libmp.mpc_atan)
+        ctx.exp = ctx._wrap_libmp_function(libmp.mpf_exp, libmp.mpc_exp)
+        ctx.expj = ctx._wrap_libmp_function(libmp.mpf_expj, libmp.mpc_expj)
+        ctx.expjpi = ctx._wrap_libmp_function(libmp.mpf_expjpi, libmp.mpc_expjpi)
+        ctx.sin = ctx._wrap_libmp_function(libmp.mpf_sin, libmp.mpc_sin)
+        ctx.cos = ctx._wrap_libmp_function(libmp.mpf_cos, libmp.mpc_cos)
+        ctx.tan = ctx._wrap_libmp_function(libmp.mpf_tan, libmp.mpc_tan)
+        ctx.sinh = ctx._wrap_libmp_function(libmp.mpf_sinh, libmp.mpc_sinh)
+        ctx.cosh = ctx._wrap_libmp_function(libmp.mpf_cosh, libmp.mpc_cosh)
+        ctx.tanh = ctx._wrap_libmp_function(libmp.mpf_tanh, libmp.mpc_tanh)
+        ctx.asin = ctx._wrap_libmp_function(libmp.mpf_asin, libmp.mpc_asin)
+        ctx.acos = ctx._wrap_libmp_function(libmp.mpf_acos, libmp.mpc_acos)
+        ctx.atan = ctx._wrap_libmp_function(libmp.mpf_atan, libmp.mpc_atan)
+        ctx.asinh = ctx._wrap_libmp_function(libmp.mpf_asinh, libmp.mpc_asinh)
+        ctx.acosh = ctx._wrap_libmp_function(libmp.mpf_acosh, libmp.mpc_acosh)
+        ctx.atanh = ctx._wrap_libmp_function(libmp.mpf_atanh, libmp.mpc_atanh)
+        ctx.sinpi = ctx._wrap_libmp_function(libmp.mpf_sin_pi, libmp.mpc_sin_pi)
+        ctx.cospi = ctx._wrap_libmp_function(libmp.mpf_cos_pi, libmp.mpc_cos_pi)
+        ctx.floor = ctx._wrap_libmp_function(libmp.mpf_floor, libmp.mpc_floor)
+        ctx.ceil = ctx._wrap_libmp_function(libmp.mpf_ceil, libmp.mpc_ceil)
+        ctx.nint = ctx._wrap_libmp_function(libmp.mpf_nint, libmp.mpc_nint)
+        ctx.frac = ctx._wrap_libmp_function(libmp.mpf_frac, libmp.mpc_frac)
+        ctx.fib = ctx.fibonacci = ctx._wrap_libmp_function(libmp.mpf_fibonacci, libmp.mpc_fibonacci)
+
+        ctx.gamma = ctx._wrap_libmp_function(libmp.mpf_gamma, libmp.mpc_gamma)
+        ctx.rgamma = ctx._wrap_libmp_function(libmp.mpf_rgamma, libmp.mpc_rgamma)
+        ctx.loggamma = ctx._wrap_libmp_function(libmp.mpf_loggamma, libmp.mpc_loggamma)
+        ctx.fac = ctx.factorial = ctx._wrap_libmp_function(libmp.mpf_factorial, libmp.mpc_factorial)
+
+        ctx.digamma = ctx._wrap_libmp_function(libmp.mpf_psi0, libmp.mpc_psi0)
+        ctx.harmonic = ctx._wrap_libmp_function(libmp.mpf_harmonic, libmp.mpc_harmonic)
+        ctx.ei = ctx._wrap_libmp_function(libmp.mpf_ei, libmp.mpc_ei)
+        ctx.e1 = ctx._wrap_libmp_function(libmp.mpf_e1, libmp.mpc_e1)
+        ctx._ci = ctx._wrap_libmp_function(libmp.mpf_ci, libmp.mpc_ci)
+        ctx._si = ctx._wrap_libmp_function(libmp.mpf_si, libmp.mpc_si)
+        ctx.ellipk = ctx._wrap_libmp_function(libmp.mpf_ellipk, libmp.mpc_ellipk)
+        ctx._ellipe = ctx._wrap_libmp_function(libmp.mpf_ellipe, libmp.mpc_ellipe)
+        ctx.agm1 = ctx._wrap_libmp_function(libmp.mpf_agm1, libmp.mpc_agm1)
+        ctx._erf = ctx._wrap_libmp_function(libmp.mpf_erf, None)
+        ctx._erfc = ctx._wrap_libmp_function(libmp.mpf_erfc, None)
+        ctx._zeta = ctx._wrap_libmp_function(libmp.mpf_zeta, libmp.mpc_zeta)
+        ctx._altzeta = ctx._wrap_libmp_function(libmp.mpf_altzeta, libmp.mpc_altzeta)
+
+        # Faster versions
+        ctx.sqrt = getattr(ctx, "_sage_sqrt", ctx.sqrt)
+        ctx.exp = getattr(ctx, "_sage_exp", ctx.exp)
+        ctx.ln = getattr(ctx, "_sage_ln", ctx.ln)
+        ctx.cos = getattr(ctx, "_sage_cos", ctx.cos)
+        ctx.sin = getattr(ctx, "_sage_sin", ctx.sin)
+
+    def to_fixed(ctx, x, prec):
+        return x.to_fixed(prec)
+
+    def hypot(ctx, x, y):
+        r"""
+        Computes the Euclidean norm of the vector `(x, y)`, equal
+        to `\sqrt{x^2 + y^2}`. Both `x` and `y` must be real."""
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        return ctx.make_mpf(libmp.mpf_hypot(x._mpf_, y._mpf_, *ctx._prec_rounding))
+
+    def _gamma_upper_int(ctx, n, z):
+        n = int(ctx._re(n))
+        if n == 0:
+            return ctx.e1(z)
+        if not hasattr(z, '_mpf_'):
+            raise NotImplementedError
+        prec, rounding = ctx._prec_rounding
+        real, imag = libmp.mpf_expint(n, z._mpf_, prec, rounding, gamma=True)
+        if imag is None:
+            return ctx.make_mpf(real)
+        else:
+            return ctx.make_mpc((real, imag))
+
+    def _expint_int(ctx, n, z):
+        n = int(n)
+        if n == 1:
+            return ctx.e1(z)
+        if not hasattr(z, '_mpf_'):
+            raise NotImplementedError
+        prec, rounding = ctx._prec_rounding
+        real, imag = libmp.mpf_expint(n, z._mpf_, prec, rounding)
+        if imag is None:
+            return ctx.make_mpf(real)
+        else:
+            return ctx.make_mpc((real, imag))
+
+    def _nthroot(ctx, x, n):
+        if hasattr(x, '_mpf_'):
+            try:
+                return ctx.make_mpf(libmp.mpf_nthroot(x._mpf_, n, *ctx._prec_rounding))
+            except ComplexResult:
+                if ctx.trap_complex:
+                    raise
+                x = (x._mpf_, libmp.fzero)
+        else:
+            x = x._mpc_
+        return ctx.make_mpc(libmp.mpc_nthroot(x, n, *ctx._prec_rounding))
+
+    def _besselj(ctx, n, z):
+        prec, rounding = ctx._prec_rounding
+        if hasattr(z, '_mpf_'):
+            return ctx.make_mpf(libmp.mpf_besseljn(n, z._mpf_, prec, rounding))
+        elif hasattr(z, '_mpc_'):
+            return ctx.make_mpc(libmp.mpc_besseljn(n, z._mpc_, prec, rounding))
+
+    def _agm(ctx, a, b=1):
+        prec, rounding = ctx._prec_rounding
+        if hasattr(a, '_mpf_') and hasattr(b, '_mpf_'):
+            try:
+                v = libmp.mpf_agm(a._mpf_, b._mpf_, prec, rounding)
+                return ctx.make_mpf(v)
+            except ComplexResult:
+                pass
+        if hasattr(a, '_mpf_'): a = (a._mpf_, libmp.fzero)
+        else: a = a._mpc_
+        if hasattr(b, '_mpf_'): b = (b._mpf_, libmp.fzero)
+        else: b = b._mpc_
+        return ctx.make_mpc(libmp.mpc_agm(a, b, prec, rounding))
+
+    def bernoulli(ctx, n):
+        return ctx.make_mpf(libmp.mpf_bernoulli(int(n), *ctx._prec_rounding))
+
+    def _zeta_int(ctx, n):
+        return ctx.make_mpf(libmp.mpf_zeta_int(int(n), *ctx._prec_rounding))
+
+    def atan2(ctx, y, x):
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        return ctx.make_mpf(libmp.mpf_atan2(y._mpf_, x._mpf_, *ctx._prec_rounding))
+
+    def psi(ctx, m, z):
+        z = ctx.convert(z)
+        m = int(m)
+        if ctx._is_real_type(z):
+            return ctx.make_mpf(libmp.mpf_psi(m, z._mpf_, *ctx._prec_rounding))
+        else:
+            return ctx.make_mpc(libmp.mpc_psi(m, z._mpc_, *ctx._prec_rounding))
+
+    def cos_sin(ctx, x, **kwargs):
+        if type(x) not in ctx.types:
+            x = ctx.convert(x)
+        prec, rounding = ctx._parse_prec(kwargs)
+        if hasattr(x, '_mpf_'):
+            c, s = libmp.mpf_cos_sin(x._mpf_, prec, rounding)
+            return ctx.make_mpf(c), ctx.make_mpf(s)
+        elif hasattr(x, '_mpc_'):
+            c, s = libmp.mpc_cos_sin(x._mpc_, prec, rounding)
+            return ctx.make_mpc(c), ctx.make_mpc(s)
+        else:
+            return ctx.cos(x, **kwargs), ctx.sin(x, **kwargs)
+
+    def cospi_sinpi(ctx, x, **kwargs):
+        if type(x) not in ctx.types:
+            x = ctx.convert(x)
+        prec, rounding = ctx._parse_prec(kwargs)
+        if hasattr(x, '_mpf_'):
+            c, s = libmp.mpf_cos_sin_pi(x._mpf_, prec, rounding)
+            return ctx.make_mpf(c), ctx.make_mpf(s)
+        elif hasattr(x, '_mpc_'):
+            c, s = libmp.mpc_cos_sin_pi(x._mpc_, prec, rounding)
+            return ctx.make_mpc(c), ctx.make_mpc(s)
+        else:
+            return ctx.cos(x, **kwargs), ctx.sin(x, **kwargs)
+
+    def clone(ctx):
+        """
+        Create a copy of the context, with the same working precision.
+        """
+        a = ctx.__class__()
+        a.prec = ctx.prec
+        return a
+
+    # Several helper methods
+    # TODO: add more of these, make consistent, write docstrings, ...
+
+    def _is_real_type(ctx, x):
+        if hasattr(x, '_mpc_') or type(x) is complex:
+            return False
+        return True
+
+    def _is_complex_type(ctx, x):
+        if hasattr(x, '_mpc_') or type(x) is complex:
+            return True
+        return False
+
+    def isnan(ctx, x):
+        """
+        Return *True* if *x* is a NaN (not-a-number), or for a complex
+        number, whether either the real or complex part is NaN;
+        otherwise return *False*::
+
+            >>> from mpmath import *
+            >>> isnan(3.14)
+            False
+            >>> isnan(nan)
+            True
+            >>> isnan(mpc(3.14,2.72))
+            False
+            >>> isnan(mpc(3.14,nan))
+            True
+
+        """
+        if hasattr(x, "_mpf_"):
+            return x._mpf_ == fnan
+        if hasattr(x, "_mpc_"):
+            return fnan in x._mpc_
+        if isinstance(x, int_types) or isinstance(x, rational.mpq):
+            return False
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'):
+            return ctx.isnan(x)
+        raise TypeError("isnan() needs a number as input")
+
+    def isfinite(ctx, x):
+        """
+        Return *True* if *x* is a finite number, i.e. neither
+        an infinity or a NaN.
+
+            >>> from mpmath import *
+            >>> isfinite(inf)
+            False
+            >>> isfinite(-inf)
+            False
+            >>> isfinite(3)
+            True
+            >>> isfinite(nan)
+            False
+            >>> isfinite(3+4j)
+            True
+            >>> isfinite(mpc(3,inf))
+            False
+            >>> isfinite(mpc(nan,3))
+            False
+
+        """
+        if ctx.isinf(x) or ctx.isnan(x):
+            return False
+        return True
+
+    def isnpint(ctx, x):
+        """
+        Determine if *x* is a nonpositive integer.
+        """
+        if not x:
+            return True
+        if hasattr(x, '_mpf_'):
+            sign, man, exp, bc = x._mpf_
+            return sign and exp >= 0
+        if hasattr(x, '_mpc_'):
+            return not x.imag and ctx.isnpint(x.real)
+        if type(x) in int_types:
+            return x <= 0
+        if isinstance(x, ctx.mpq):
+            p, q = x._mpq_
+            if not p:
+                return True
+            return q == 1 and p <= 0
+        return ctx.isnpint(ctx.convert(x))
+
+    def __str__(ctx):
+        lines = ["Mpmath settings:",
+            ("  mp.prec = %s" % ctx.prec).ljust(30) + "[default: 53]",
+            ("  mp.dps = %s" % ctx.dps).ljust(30) + "[default: 15]",
+            ("  mp.trap_complex = %s" % ctx.trap_complex).ljust(30) + "[default: False]",
+        ]
+        return "\n".join(lines)
+
+    @property
+    def _repr_digits(ctx):
+        return repr_dps(ctx._prec)
+
+    @property
+    def _str_digits(ctx):
+        return ctx._dps
+
+    def extraprec(ctx, n, normalize_output=False):
+        """
+        The block
+
+            with extraprec(n):
+                <code>
+
+        increases the precision n bits, executes <code>, and then
+        restores the precision.
+
+        extraprec(n)(f) returns a decorated version of the function f
+        that increases the working precision by n bits before execution,
+        and restores the parent precision afterwards. With
+        normalize_output=True, it rounds the return value to the parent
+        precision.
+        """
+        return PrecisionManager(ctx, lambda p: p + n, None, normalize_output)
+
+    def extradps(ctx, n, normalize_output=False):
+        """
+        This function is analogous to extraprec (see documentation)
+        but changes the decimal precision instead of the number of bits.
+        """
+        return PrecisionManager(ctx, None, lambda d: d + n, normalize_output)
+
+    def workprec(ctx, n, normalize_output=False):
+        """
+        The block
+
+            with workprec(n):
+                <code>
+
+        sets the precision to n bits, executes <code>, and then restores
+        the precision.
+
+        workprec(n)(f) returns a decorated version of the function f
+        that sets the precision to n bits before execution,
+        and restores the precision afterwards. With normalize_output=True,
+        it rounds the return value to the parent precision.
+        """
+        return PrecisionManager(ctx, lambda p: n, None, normalize_output)
+
+    def workdps(ctx, n, normalize_output=False):
+        """
+        This function is analogous to workprec (see documentation)
+        but changes the decimal precision instead of the number of bits.
+        """
+        return PrecisionManager(ctx, None, lambda d: n, normalize_output)
+
+    def autoprec(ctx, f, maxprec=None, catch=(), verbose=False):
+        r"""
+        Return a wrapped copy of *f* that repeatedly evaluates *f*
+        with increasing precision until the result converges to the
+        full precision used at the point of the call.
+
+        This heuristically protects against rounding errors, at the cost of
+        roughly a 2x slowdown compared to manually setting the optimal
+        precision. This method can, however, easily be fooled if the results
+        from *f* depend "discontinuously" on the precision, for instance
+        if catastrophic cancellation can occur. Therefore, :func:`~mpmath.autoprec`
+        should be used judiciously.
+
+        **Examples**
+
+        Many functions are sensitive to perturbations of the input arguments.
+        If the arguments are decimal numbers, they may have to be converted
+        to binary at a much higher precision. If the amount of required
+        extra precision is unknown, :func:`~mpmath.autoprec` is convenient::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15
+            >>> mp.pretty = True
+            >>> besselj(5, 125 * 10**28)    # Exact input
+            -8.03284785591801e-17
+            >>> besselj(5, '1.25e30')   # Bad
+            7.12954868316652e-16
+            >>> autoprec(besselj)(5, '1.25e30')   # Good
+            -8.03284785591801e-17
+
+        The following fails to converge because `\sin(\pi) = 0` whereas all
+        finite-precision approximations of `\pi` give nonzero values::
+
+            >>> autoprec(sin)(pi) # doctest: +IGNORE_EXCEPTION_DETAIL
+            Traceback (most recent call last):
+              ...
+            NoConvergence: autoprec: prec increased to 2910 without convergence
+
+        As the following example shows, :func:`~mpmath.autoprec` can protect against
+        cancellation, but is fooled by too severe cancellation::
+
+            >>> x = 1e-10
+            >>> exp(x)-1; expm1(x); autoprec(lambda t: exp(t)-1)(x)
+            1.00000008274037e-10
+            1.00000000005e-10
+            1.00000000005e-10
+            >>> x = 1e-50
+            >>> exp(x)-1; expm1(x); autoprec(lambda t: exp(t)-1)(x)
+            0.0
+            1.0e-50
+            0.0
+
+        With *catch*, an exception or list of exceptions to intercept
+        may be specified. The raised exception is interpreted
+        as signaling insufficient precision. This permits, for example,
+        evaluating a function where a too low precision results in a
+        division by zero::
+
+            >>> f = lambda x: 1/(exp(x)-1)
+            >>> f(1e-30)
+            Traceback (most recent call last):
+              ...
+            ZeroDivisionError
+            >>> autoprec(f, catch=ZeroDivisionError)(1e-30)
+            1.0e+30
+
+
+        """
+        def f_autoprec_wrapped(*args, **kwargs):
+            prec = ctx.prec
+            if maxprec is None:
+                maxprec2 = ctx._default_hyper_maxprec(prec)
+            else:
+                maxprec2 = maxprec
+            try:
+                ctx.prec = prec + 10
+                try:
+                    v1 = f(*args, **kwargs)
+                except catch:
+                    v1 = ctx.nan
+                prec2 = prec + 20
+                while 1:
+                    ctx.prec = prec2
+                    try:
+                        v2 = f(*args, **kwargs)
+                    except catch:
+                        v2 = ctx.nan
+                    if v1 == v2:
+                        break
+                    err = ctx.mag(v2-v1) - ctx.mag(v2)
+                    if err < (-prec):
+                        break
+                    if verbose:
+                        print("autoprec: target=%s, prec=%s, accuracy=%s" \
+                            % (prec, prec2, -err))
+                    v1 = v2
+                    if prec2 >= maxprec2:
+                        raise ctx.NoConvergence(\
+                        "autoprec: prec increased to %i without convergence"\
+                        % prec2)
+                    prec2 += int(prec2*2)
+                    prec2 = min(prec2, maxprec2)
+            finally:
+                ctx.prec = prec
+            return +v2
+        return f_autoprec_wrapped
+
+    def nstr(ctx, x, n=6, **kwargs):
+        """
+        Convert an ``mpf`` or ``mpc`` to a decimal string literal with *n*
+        significant digits. The small default value for *n* is chosen to
+        make this function useful for printing collections of numbers
+        (lists, matrices, etc).
+
+        If *x* is a list or tuple, :func:`~mpmath.nstr` is applied recursively
+        to each element. For unrecognized classes, :func:`~mpmath.nstr`
+        simply returns ``str(x)``.
+
+        The companion function :func:`~mpmath.nprint` prints the result
+        instead of returning it.
+
+        The keyword arguments *strip_zeros*, *min_fixed*, *max_fixed*
+        and *show_zero_exponent* are forwarded to :func:`~mpmath.libmp.to_str`.
+
+        The number will be printed in fixed-point format if the position
+        of the leading digit is strictly between min_fixed
+        (default = min(-dps/3,-5)) and max_fixed (default = dps).
+
+        To force fixed-point format always, set min_fixed = -inf,
+        max_fixed = +inf. To force floating-point format, set
+        min_fixed >= max_fixed.
+
+            >>> from mpmath import *
+            >>> nstr([+pi, ldexp(1,-500)])
+            '[3.14159, 3.05494e-151]'
+            >>> nprint([+pi, ldexp(1,-500)])
+            [3.14159, 3.05494e-151]
+            >>> nstr(mpf("5e-10"), 5)
+            '5.0e-10'
+            >>> nstr(mpf("5e-10"), 5, strip_zeros=False)
+            '5.0000e-10'
+            >>> nstr(mpf("5e-10"), 5, strip_zeros=False, min_fixed=-11)
+            '0.00000000050000'
+            >>> nstr(mpf(0), 5, show_zero_exponent=True)
+            '0.0e+0'
+
+        """
+        if isinstance(x, list):
+            return "[%s]" % (", ".join(ctx.nstr(c, n, **kwargs) for c in x))
+        if isinstance(x, tuple):
+            return "(%s)" % (", ".join(ctx.nstr(c, n, **kwargs) for c in x))
+        if hasattr(x, '_mpf_'):
+            return to_str(x._mpf_, n, **kwargs)
+        if hasattr(x, '_mpc_'):
+            return "(" + mpc_to_str(x._mpc_, n, **kwargs)  + ")"
+        if isinstance(x, basestring):
+            return repr(x)
+        if isinstance(x, ctx.matrix):
+            return x.__nstr__(n, **kwargs)
+        return str(x)
+
+    def _convert_fallback(ctx, x, strings):
+        if strings and isinstance(x, basestring):
+            if 'j' in x.lower():
+                x = x.lower().replace(' ', '')
+                match = get_complex.match(x)
+                re = match.group('re')
+                if not re:
+                    re = 0
+                im = match.group('im').rstrip('j')
+                return ctx.mpc(ctx.convert(re), ctx.convert(im))
+        if hasattr(x, "_mpi_"):
+            a, b = x._mpi_
+            if a == b:
+                return ctx.make_mpf(a)
+            else:
+                raise ValueError("can only create mpf from zero-width interval")
+        raise TypeError("cannot create mpf from " + repr(x))
+
+    def mpmathify(ctx, *args, **kwargs):
+        return ctx.convert(*args, **kwargs)
+
+    def _parse_prec(ctx, kwargs):
+        if kwargs:
+            if kwargs.get('exact'):
+                return 0, 'f'
+            prec, rounding = ctx._prec_rounding
+            if 'rounding' in kwargs:
+                rounding = kwargs['rounding']
+            if 'prec' in kwargs:
+                prec = kwargs['prec']
+                if prec == ctx.inf:
+                    return 0, 'f'
+                else:
+                    prec = int(prec)
+            elif 'dps' in kwargs:
+                dps = kwargs['dps']
+                if dps == ctx.inf:
+                    return 0, 'f'
+                prec = dps_to_prec(dps)
+            return prec, rounding
+        return ctx._prec_rounding
+
+    _exact_overflow_msg = "the exact result does not fit in memory"
+
+    _hypsum_msg = """hypsum() failed to converge to the requested %i bits of accuracy
+using a working precision of %i bits. Try with a higher maxprec,
+maxterms, or set zeroprec."""
+
+    def hypsum(ctx, p, q, flags, coeffs, z, accurate_small=True, **kwargs):
+        if hasattr(z, "_mpf_"):
+            key = p, q, flags, 'R'
+            v = z._mpf_
+        elif hasattr(z, "_mpc_"):
+            key = p, q, flags, 'C'
+            v = z._mpc_
+        if key not in ctx.hyp_summators:
+            ctx.hyp_summators[key] = libmp.make_hyp_summator(key)[1]
+        summator = ctx.hyp_summators[key]
+        prec = ctx.prec
+        maxprec = kwargs.get('maxprec', ctx._default_hyper_maxprec(prec))
+        extraprec = 50
+        epsshift = 25
+        # Jumps in magnitude occur when parameters are close to negative
+        # integers. We must ensure that these terms are included in
+        # the sum and added accurately
+        magnitude_check = {}
+        max_total_jump = 0
+        for i, c in enumerate(coeffs):
+            if flags[i] == 'Z':
+                if i >= p and c <= 0:
+                    ok = False
+                    for ii, cc in enumerate(coeffs[:p]):
+                        # Note: c <= cc or c < cc, depending on convention
+                        if flags[ii] == 'Z' and cc <= 0 and c <= cc:
+                            ok = True
+                    if not ok:
+                        raise ZeroDivisionError("pole in hypergeometric series")
+                continue
+            n, d = ctx.nint_distance(c)
+            n = -int(n)
+            d = -d
+            if i >= p and n >= 0 and d > 4:
+                if n in magnitude_check:
+                    magnitude_check[n] += d
+                else:
+                    magnitude_check[n] = d
+                extraprec = max(extraprec, d - prec + 60)
+            max_total_jump += abs(d)
+        while 1:
+            if extraprec > maxprec:
+                raise ValueError(ctx._hypsum_msg % (prec, prec+extraprec))
+            wp = prec + extraprec
+            if magnitude_check:
+                mag_dict = dict((n,None) for n in magnitude_check)
+            else:
+                mag_dict = {}
+            zv, have_complex, magnitude = summator(coeffs, v, prec, wp, \
+                epsshift, mag_dict, **kwargs)
+            cancel = -magnitude
+            jumps_resolved = True
+            if extraprec < max_total_jump:
+                for n in mag_dict.values():
+                    if (n is None) or (n < prec):
+                        jumps_resolved = False
+                        break
+            accurate = (cancel < extraprec-25-5 or not accurate_small)
+            if jumps_resolved:
+                if accurate:
+                    break
+                # zero?
+                zeroprec = kwargs.get('zeroprec')
+                if zeroprec is not None:
+                    if cancel > zeroprec:
+                        if have_complex:
+                            return ctx.mpc(0)
+                        else:
+                            return ctx.zero
+
+            # Some near-singularities were not included, so increase
+            # precision and repeat until they are
+            extraprec *= 2
+            # Possible workaround for bad roundoff in fixed-point arithmetic
+            epsshift += 5
+            extraprec += 5
+
+        if type(zv) is tuple:
+            if have_complex:
+                return ctx.make_mpc(zv)
+            else:
+                return ctx.make_mpf(zv)
+        else:
+            return zv
+
+    def ldexp(ctx, x, n):
+        r"""
+        Computes `x 2^n` efficiently. No rounding is performed.
+        The argument `x` must be a real floating-point number (or
+        possible to convert into one) and `n` must be a Python ``int``.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> ldexp(1, 10)
+            mpf('1024.0')
+            >>> ldexp(1, -3)
+            mpf('0.125')
+
+        """
+        x = ctx.convert(x)
+        return ctx.make_mpf(libmp.mpf_shift(x._mpf_, n))
+
+    def frexp(ctx, x):
+        r"""
+        Given a real number `x`, returns `(y, n)` with `y \in [0.5, 1)`,
+        `n` a Python integer, and such that `x = y 2^n`. No rounding is
+        performed.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> frexp(7.5)
+            (mpf('0.9375'), 3)
+
+        """
+        x = ctx.convert(x)
+        y, n = libmp.mpf_frexp(x._mpf_)
+        return ctx.make_mpf(y), n
+
+    def fneg(ctx, x, **kwargs):
+        """
+        Negates the number *x*, giving a floating-point result, optionally
+        using a custom precision and rounding mode.
+
+        See the documentation of :func:`~mpmath.fadd` for a detailed description
+        of how to specify precision and rounding.
+
+        **Examples**
+
+        An mpmath number is returned::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fneg(2.5)
+            mpf('-2.5')
+            >>> fneg(-5+2j)
+            mpc(real='5.0', imag='-2.0')
+
+        Precise control over rounding is possible::
+
+            >>> x = fadd(2, 1e-100, exact=True)
+            >>> fneg(x)
+            mpf('-2.0')
+            >>> fneg(x, rounding='f')
+            mpf('-2.0000000000000004')
+
+        Negating with and without roundoff::
+
+            >>> n = 200000000000000000000001
+            >>> print(int(-mpf(n)))
+            -200000000000000016777216
+            >>> print(int(fneg(n)))
+            -200000000000000016777216
+            >>> print(int(fneg(n, prec=log(n,2)+1)))
+            -200000000000000000000001
+            >>> print(int(fneg(n, dps=log(n,10)+1)))
+            -200000000000000000000001
+            >>> print(int(fneg(n, prec=inf)))
+            -200000000000000000000001
+            >>> print(int(fneg(n, dps=inf)))
+            -200000000000000000000001
+            >>> print(int(fneg(n, exact=True)))
+            -200000000000000000000001
+
+        """
+        prec, rounding = ctx._parse_prec(kwargs)
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_'):
+            return ctx.make_mpf(mpf_neg(x._mpf_, prec, rounding))
+        if hasattr(x, '_mpc_'):
+            return ctx.make_mpc(mpc_neg(x._mpc_, prec, rounding))
+        raise ValueError("Arguments need to be mpf or mpc compatible numbers")
+
+    def fadd(ctx, x, y, **kwargs):
+        """
+        Adds the numbers *x* and *y*, giving a floating-point result,
+        optionally using a custom precision and rounding mode.
+
+        The default precision is the working precision of the context.
+        You can specify a custom precision in bits by passing the *prec* keyword
+        argument, or by providing an equivalent decimal precision with the *dps*
+        keyword argument. If the precision is set to ``+inf``, or if the flag
+        *exact=True* is passed, an exact addition with no rounding is performed.
+
+        When the precision is finite, the optional *rounding* keyword argument
+        specifies the direction of rounding. Valid options are ``'n'`` for
+        nearest (default), ``'f'`` for floor, ``'c'`` for ceiling, ``'d'``
+        for down, ``'u'`` for up.
+
+        **Examples**
+
+        Using :func:`~mpmath.fadd` with precision and rounding control::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fadd(2, 1e-20)
+            mpf('2.0')
+            >>> fadd(2, 1e-20, rounding='u')
+            mpf('2.0000000000000004')
+            >>> nprint(fadd(2, 1e-20, prec=100), 25)
+            2.00000000000000000001
+            >>> nprint(fadd(2, 1e-20, dps=15), 25)
+            2.0
+            >>> nprint(fadd(2, 1e-20, dps=25), 25)
+            2.00000000000000000001
+            >>> nprint(fadd(2, 1e-20, exact=True), 25)
+            2.00000000000000000001
+
+        Exact addition avoids cancellation errors, enforcing familiar laws
+        of numbers such as `x+y-x = y`, which don't hold in floating-point
+        arithmetic with finite precision::
+
+            >>> x, y = mpf(2), mpf('1e-1000')
+            >>> print(x + y - x)
+            0.0
+            >>> print(fadd(x, y, prec=inf) - x)
+            1.0e-1000
+            >>> print(fadd(x, y, exact=True) - x)
+            1.0e-1000
+
+        Exact addition can be inefficient and may be impossible to perform
+        with large magnitude differences::
+
+            >>> fadd(1, '1e-100000000000000000000', prec=inf)
+            Traceback (most recent call last):
+              ...
+            OverflowError: the exact result does not fit in memory
+
+        """
+        prec, rounding = ctx._parse_prec(kwargs)
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        try:
+            if hasattr(x, '_mpf_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpf(mpf_add(x._mpf_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_add_mpf(y._mpc_, x._mpf_, prec, rounding))
+            if hasattr(x, '_mpc_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpc(mpc_add_mpf(x._mpc_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_add(x._mpc_, y._mpc_, prec, rounding))
+        except (ValueError, OverflowError):
+            raise OverflowError(ctx._exact_overflow_msg)
+        raise ValueError("Arguments need to be mpf or mpc compatible numbers")
+
+    def fsub(ctx, x, y, **kwargs):
+        """
+        Subtracts the numbers *x* and *y*, giving a floating-point result,
+        optionally using a custom precision and rounding mode.
+
+        See the documentation of :func:`~mpmath.fadd` for a detailed description
+        of how to specify precision and rounding.
+
+        **Examples**
+
+        Using :func:`~mpmath.fsub` with precision and rounding control::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fsub(2, 1e-20)
+            mpf('2.0')
+            >>> fsub(2, 1e-20, rounding='d')
+            mpf('1.9999999999999998')
+            >>> nprint(fsub(2, 1e-20, prec=100), 25)
+            1.99999999999999999999
+            >>> nprint(fsub(2, 1e-20, dps=15), 25)
+            2.0
+            >>> nprint(fsub(2, 1e-20, dps=25), 25)
+            1.99999999999999999999
+            >>> nprint(fsub(2, 1e-20, exact=True), 25)
+            1.99999999999999999999
+
+        Exact subtraction avoids cancellation errors, enforcing familiar laws
+        of numbers such as `x-y+y = x`, which don't hold in floating-point
+        arithmetic with finite precision::
+
+            >>> x, y = mpf(2), mpf('1e1000')
+            >>> print(x - y + y)
+            0.0
+            >>> print(fsub(x, y, prec=inf) + y)
+            2.0
+            >>> print(fsub(x, y, exact=True) + y)
+            2.0
+
+        Exact addition can be inefficient and may be impossible to perform
+        with large magnitude differences::
+
+            >>> fsub(1, '1e-100000000000000000000', prec=inf)
+            Traceback (most recent call last):
+              ...
+            OverflowError: the exact result does not fit in memory
+
+        """
+        prec, rounding = ctx._parse_prec(kwargs)
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        try:
+            if hasattr(x, '_mpf_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpf(mpf_sub(x._mpf_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_sub((x._mpf_, fzero), y._mpc_, prec, rounding))
+            if hasattr(x, '_mpc_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpc(mpc_sub_mpf(x._mpc_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_sub(x._mpc_, y._mpc_, prec, rounding))
+        except (ValueError, OverflowError):
+            raise OverflowError(ctx._exact_overflow_msg)
+        raise ValueError("Arguments need to be mpf or mpc compatible numbers")
+
+    def fmul(ctx, x, y, **kwargs):
+        """
+        Multiplies the numbers *x* and *y*, giving a floating-point result,
+        optionally using a custom precision and rounding mode.
+
+        See the documentation of :func:`~mpmath.fadd` for a detailed description
+        of how to specify precision and rounding.
+
+        **Examples**
+
+        The result is an mpmath number::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fmul(2, 5.0)
+            mpf('10.0')
+            >>> fmul(0.5j, 0.5)
+            mpc(real='0.0', imag='0.25')
+
+        Avoiding roundoff::
+
+            >>> x, y = 10**10+1, 10**15+1
+            >>> print(x*y)
+            10000000001000010000000001
+            >>> print(mpf(x) * mpf(y))
+            1.0000000001e+25
+            >>> print(int(mpf(x) * mpf(y)))
+            10000000001000011026399232
+            >>> print(int(fmul(x, y)))
+            10000000001000011026399232
+            >>> print(int(fmul(x, y, dps=25)))
+            10000000001000010000000001
+            >>> print(int(fmul(x, y, exact=True)))
+            10000000001000010000000001
+
+        Exact multiplication with complex numbers can be inefficient and may
+        be impossible to perform with large magnitude differences between
+        real and imaginary parts::
+
+            >>> x = 1+2j
+            >>> y = mpc(2, '1e-100000000000000000000')
+            >>> fmul(x, y)
+            mpc(real='2.0', imag='4.0')
+            >>> fmul(x, y, rounding='u')
+            mpc(real='2.0', imag='4.0000000000000009')
+            >>> fmul(x, y, exact=True)
+            Traceback (most recent call last):
+              ...
+            OverflowError: the exact result does not fit in memory
+
+        """
+        prec, rounding = ctx._parse_prec(kwargs)
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        try:
+            if hasattr(x, '_mpf_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpf(mpf_mul(x._mpf_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_mul_mpf(y._mpc_, x._mpf_, prec, rounding))
+            if hasattr(x, '_mpc_'):
+                if hasattr(y, '_mpf_'):
+                    return ctx.make_mpc(mpc_mul_mpf(x._mpc_, y._mpf_, prec, rounding))
+                if hasattr(y, '_mpc_'):
+                    return ctx.make_mpc(mpc_mul(x._mpc_, y._mpc_, prec, rounding))
+        except (ValueError, OverflowError):
+            raise OverflowError(ctx._exact_overflow_msg)
+        raise ValueError("Arguments need to be mpf or mpc compatible numbers")
+
+    def fdiv(ctx, x, y, **kwargs):
+        """
+        Divides the numbers *x* and *y*, giving a floating-point result,
+        optionally using a custom precision and rounding mode.
+
+        See the documentation of :func:`~mpmath.fadd` for a detailed description
+        of how to specify precision and rounding.
+
+        **Examples**
+
+        The result is an mpmath number::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fdiv(3, 2)
+            mpf('1.5')
+            >>> fdiv(2, 3)
+            mpf('0.66666666666666663')
+            >>> fdiv(2+4j, 0.5)
+            mpc(real='4.0', imag='8.0')
+
+        The rounding direction and precision can be controlled::
+
+            >>> fdiv(2, 3, dps=3)    # Should be accurate to at least 3 digits
+            mpf('0.6666259765625')
+            >>> fdiv(2, 3, rounding='d')
+            mpf('0.66666666666666663')
+            >>> fdiv(2, 3, prec=60)
+            mpf('0.66666666666666667')
+            >>> fdiv(2, 3, rounding='u')
+            mpf('0.66666666666666674')
+
+        Checking the error of a division by performing it at higher precision::
+
+            >>> fdiv(2, 3) - fdiv(2, 3, prec=100)
+            mpf('-3.7007434154172148e-17')
+
+        Unlike :func:`~mpmath.fadd`, :func:`~mpmath.fmul`, etc., exact division is not
+        allowed since the quotient of two floating-point numbers generally
+        does not have an exact floating-point representation. (In the
+        future this might be changed to allow the case where the division
+        is actually exact.)
+
+            >>> fdiv(2, 3, exact=True)
+            Traceback (most recent call last):
+              ...
+            ValueError: division is not an exact operation
+
+        """
+        prec, rounding = ctx._parse_prec(kwargs)
+        if not prec:
+            raise ValueError("division is not an exact operation")
+        x = ctx.convert(x)
+        y = ctx.convert(y)
+        if hasattr(x, '_mpf_'):
+            if hasattr(y, '_mpf_'):
+                return ctx.make_mpf(mpf_div(x._mpf_, y._mpf_, prec, rounding))
+            if hasattr(y, '_mpc_'):
+                return ctx.make_mpc(mpc_div((x._mpf_, fzero), y._mpc_, prec, rounding))
+        if hasattr(x, '_mpc_'):
+            if hasattr(y, '_mpf_'):
+                return ctx.make_mpc(mpc_div_mpf(x._mpc_, y._mpf_, prec, rounding))
+            if hasattr(y, '_mpc_'):
+                return ctx.make_mpc(mpc_div(x._mpc_, y._mpc_, prec, rounding))
+        raise ValueError("Arguments need to be mpf or mpc compatible numbers")
+
+    def nint_distance(ctx, x):
+        r"""
+        Return `(n,d)` where `n` is the nearest integer to `x` and `d` is
+        an estimate of `\log_2(|x-n|)`. If `d < 0`, `-d` gives the precision
+        (measured in bits) lost to cancellation when computing `x-n`.
+
+            >>> from mpmath import *
+            >>> n, d = nint_distance(5)
+            >>> print(n); print(d)
+            5
+            -inf
+            >>> n, d = nint_distance(mpf(5))
+            >>> print(n); print(d)
+            5
+            -inf
+            >>> n, d = nint_distance(mpf(5.00000001))
+            >>> print(n); print(d)
+            5
+            -26
+            >>> n, d = nint_distance(mpf(4.99999999))
+            >>> print(n); print(d)
+            5
+            -26
+            >>> n, d = nint_distance(mpc(5,10))
+            >>> print(n); print(d)
+            5
+            4
+            >>> n, d = nint_distance(mpc(5,0.000001))
+            >>> print(n); print(d)
+            5
+            -19
+
+        """
+        typx = type(x)
+        if typx in int_types:
+            return int(x), ctx.ninf
+        elif typx is rational.mpq:
+            p, q = x._mpq_
+            n, r = divmod(p, q)
+            if 2*r >= q:
+                n += 1
+            elif not r:
+                return n, ctx.ninf
+            # log(p/q-n) = log((p-nq)/q) = log(p-nq) - log(q)
+            d = bitcount(abs(p-n*q)) - bitcount(q)
+            return n, d
+        if hasattr(x, "_mpf_"):
+            re = x._mpf_
+            im_dist = ctx.ninf
+        elif hasattr(x, "_mpc_"):
+            re, im = x._mpc_
+            isign, iman, iexp, ibc = im
+            if iman:
+                im_dist = iexp + ibc
+            elif im == fzero:
+                im_dist = ctx.ninf
+            else:
+                raise ValueError("requires a finite number")
+        else:
+            x = ctx.convert(x)
+            if hasattr(x, "_mpf_") or hasattr(x, "_mpc_"):
+                return ctx.nint_distance(x)
+            else:
+                raise TypeError("requires an mpf/mpc")
+        sign, man, exp, bc = re
+        mag = exp+bc
+        # |x| < 0.5
+        if mag < 0:
+            n = 0
+            re_dist = mag
+        elif man:
+            # exact integer
+            if exp >= 0:
+                n = man << exp
+                re_dist = ctx.ninf
+            # exact half-integer
+            elif exp == -1:
+                n = (man>>1)+1
+                re_dist = 0
+            else:
+                d = (-exp-1)
+                t = man >> d
+                if t & 1:
+                    t += 1
+                    man = (t<<d) - man
+                else:
+                    man -= (t<<d)
+                n = t>>1   # int(t)>>1
+                re_dist = exp+bitcount(man)
+            if sign:
+                n = -n
+        elif re == fzero:
+            re_dist = ctx.ninf
+            n = 0
+        else:
+            raise ValueError("requires a finite number")
+        return n, max(re_dist, im_dist)
+
+    def fprod(ctx, factors):
+        r"""
+        Calculates a product containing a finite number of factors (for
+        infinite products, see :func:`~mpmath.nprod`). The factors will be
+        converted to mpmath numbers.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fprod([1, 2, 0.5, 7])
+            mpf('7.0')
+
+        """
+        orig = ctx.prec
+        try:
+            v = ctx.one
+            for p in factors:
+                v *= p
+        finally:
+            ctx.prec = orig
+        return +v
+
+    def rand(ctx):
+        """
+        Returns an ``mpf`` with value chosen randomly from `[0, 1)`.
+        The number of randomly generated bits in the mantissa is equal
+        to the working precision.
+        """
+        return ctx.make_mpf(mpf_rand(ctx._prec))
+
+    def fraction(ctx, p, q):
+        """
+        Given Python integers `(p, q)`, returns a lazy ``mpf`` representing
+        the fraction `p/q`. The value is updated with the precision.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15
+            >>> a = fraction(1,100)
+            >>> b = mpf(1)/100
+            >>> print(a); print(b)
+            0.01
+            0.01
+            >>> mp.dps = 30
+            >>> print(a); print(b)      # a will be accurate
+            0.01
+            0.0100000000000000002081668171172
+            >>> mp.dps = 15
+        """
+        return ctx.constant(lambda prec, rnd: from_rational(p, q, prec, rnd),
+            '%s/%s' % (p, q))
+
+    def absmin(ctx, x):
+        return abs(ctx.convert(x))
+
+    def absmax(ctx, x):
+        return abs(ctx.convert(x))
+
+    def _as_points(ctx, x):
+        # XXX: remove this?
+        if hasattr(x, '_mpi_'):
+            a, b = x._mpi_
+            return [ctx.make_mpf(a), ctx.make_mpf(b)]
+        return x
+
+    '''
+    def _zetasum(ctx, s, a, b):
+        """
+        Computes sum of k^(-s) for k = a, a+1, ..., b with a, b both small
+        integers.
+        """
+        a = int(a)
+        b = int(b)
+        s = ctx.convert(s)
+        prec, rounding = ctx._prec_rounding
+        if hasattr(s, '_mpf_'):
+            v = ctx.make_mpf(libmp.mpf_zetasum(s._mpf_, a, b, prec))
+        elif hasattr(s, '_mpc_'):
+            v = ctx.make_mpc(libmp.mpc_zetasum(s._mpc_, a, b, prec))
+        return v
+    '''
+
+    def _zetasum_fast(ctx, s, a, n, derivatives=[0], reflect=False):
+        if not (ctx.isint(a) and hasattr(s, "_mpc_")):
+            raise NotImplementedError
+        a = int(a)
+        prec = ctx._prec
+        xs, ys = libmp.mpc_zetasum(s._mpc_, a, n, derivatives, reflect, prec)
+        xs = [ctx.make_mpc(x) for x in xs]
+        ys = [ctx.make_mpc(y) for y in ys]
+        return xs, ys
+
+class PrecisionManager:
+    def __init__(self, ctx, precfun, dpsfun, normalize_output=False):
+        self.ctx = ctx
+        self.precfun = precfun
+        self.dpsfun = dpsfun
+        self.normalize_output = normalize_output
+    def __call__(self, f):
+        @functools.wraps(f)
+        def g(*args, **kwargs):
+            orig = self.ctx.prec
+            try:
+                if self.precfun:
+                    self.ctx.prec = self.precfun(self.ctx.prec)
+                else:
+                    self.ctx.dps = self.dpsfun(self.ctx.dps)
+                if self.normalize_output:
+                    v = f(*args, **kwargs)
+                    if type(v) is tuple:
+                        return tuple([+a for a in v])
+                    return +v
+                else:
+                    return f(*args, **kwargs)
+            finally:
+                self.ctx.prec = orig
+        return g
+    def __enter__(self):
+        self.origp = self.ctx.prec
+        if self.precfun:
+            self.ctx.prec = self.precfun(self.ctx.prec)
+        else:
+            self.ctx.dps = self.dpsfun(self.ctx.dps)
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.ctx.prec = self.origp
+        return False
+
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/ctx_mp_python.py

@@ -0,0 +1,1149 @@
+#from ctx_base import StandardBaseContext
+
+from .libmp.backend import basestring, exec_
+
+from .libmp import (MPZ, MPZ_ZERO, MPZ_ONE, int_types, repr_dps,
+    round_floor, round_ceiling, dps_to_prec, round_nearest, prec_to_dps,
+    ComplexResult, to_pickable, from_pickable, normalize,
+    from_int, from_float, from_npfloat, from_Decimal, from_str, to_int, to_float, to_str,
+    from_rational, from_man_exp,
+    fone, fzero, finf, fninf, fnan,
+    mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_mul_int,
+    mpf_div, mpf_rdiv_int, mpf_pow_int, mpf_mod,
+    mpf_eq, mpf_cmp, mpf_lt, mpf_gt, mpf_le, mpf_ge,
+    mpf_hash, mpf_rand,
+    mpf_sum,
+    bitcount, to_fixed,
+    mpc_to_str,
+    mpc_to_complex, mpc_hash, mpc_pos, mpc_is_nonzero, mpc_neg, mpc_conjugate,
+    mpc_abs, mpc_add, mpc_add_mpf, mpc_sub, mpc_sub_mpf, mpc_mul, mpc_mul_mpf,
+    mpc_mul_int, mpc_div, mpc_div_mpf, mpc_pow, mpc_pow_mpf, mpc_pow_int,
+    mpc_mpf_div,
+    mpf_pow,
+    mpf_pi, mpf_degree, mpf_e, mpf_phi, mpf_ln2, mpf_ln10,
+    mpf_euler, mpf_catalan, mpf_apery, mpf_khinchin,
+    mpf_glaisher, mpf_twinprime, mpf_mertens,
+    int_types)
+
+from . import rational
+from . import function_docs
+
+new = object.__new__
+
+class mpnumeric(object):
+    """Base class for mpf and mpc."""
+    __slots__ = []
+    def __new__(cls, val):
+        raise NotImplementedError
+
+class _mpf(mpnumeric):
+    """
+    An mpf instance holds a real-valued floating-point number. mpf:s
+    work analogously to Python floats, but support arbitrary-precision
+    arithmetic.
+    """
+    __slots__ = ['_mpf_']
+
+    def __new__(cls, val=fzero, **kwargs):
+        """A new mpf can be created from a Python float, an int, a
+        or a decimal string representing a number in floating-point
+        format."""
+        prec, rounding = cls.context._prec_rounding
+        if kwargs:
+            prec = kwargs.get('prec', prec)
+            if 'dps' in kwargs:
+                prec = dps_to_prec(kwargs['dps'])
+            rounding = kwargs.get('rounding', rounding)
+        if type(val) is cls:
+            sign, man, exp, bc = val._mpf_
+            if (not man) and exp:
+                return val
+            v = new(cls)
+            v._mpf_ = normalize(sign, man, exp, bc, prec, rounding)
+            return v
+        elif type(val) is tuple:
+            if len(val) == 2:
+                v = new(cls)
+                v._mpf_ = from_man_exp(val[0], val[1], prec, rounding)
+                return v
+            if len(val) == 4:
+                if val not in (finf, fninf, fnan):
+                    sign, man, exp, bc = val
+                    val = normalize(sign, MPZ(man), exp, bc, prec, rounding)
+                v = new(cls)
+                v._mpf_ = val
+                return v
+            raise ValueError
+        else:
+            v = new(cls)
+            v._mpf_ = mpf_pos(cls.mpf_convert_arg(val, prec, rounding), prec, rounding)
+            return v
+
+    @classmethod
+    def mpf_convert_arg(cls, x, prec, rounding):
+        if isinstance(x, int_types): return from_int(x)
+        if isinstance(x, float): return from_float(x)
+        if isinstance(x, basestring): return from_str(x, prec, rounding)
+        if isinstance(x, cls.context.constant): return x.func(prec, rounding)
+        if hasattr(x, '_mpf_'): return x._mpf_
+        if hasattr(x, '_mpmath_'):
+            t = cls.context.convert(x._mpmath_(prec, rounding))
+            if hasattr(t, '_mpf_'):
+                return t._mpf_
+        if hasattr(x, '_mpi_'):
+            a, b = x._mpi_
+            if a == b:
+                return a
+            raise ValueError("can only create mpf from zero-width interval")
+        raise TypeError("cannot create mpf from " + repr(x))
+
+    @classmethod
+    def mpf_convert_rhs(cls, x):
+        if isinstance(x, int_types): return from_int(x)
+        if isinstance(x, float): return from_float(x)
+        if isinstance(x, complex_types): return cls.context.mpc(x)
+        if isinstance(x, rational.mpq):
+            p, q = x._mpq_
+            return from_rational(p, q, cls.context.prec)
+        if hasattr(x, '_mpf_'): return x._mpf_
+        if hasattr(x, '_mpmath_'):
+            t = cls.context.convert(x._mpmath_(*cls.context._prec_rounding))
+            if hasattr(t, '_mpf_'):
+                return t._mpf_
+            return t
+        return NotImplemented
+
+    @classmethod
+    def mpf_convert_lhs(cls, x):
+        x = cls.mpf_convert_rhs(x)
+        if type(x) is tuple:
+            return cls.context.make_mpf(x)
+        return x
+
+    man_exp = property(lambda self: self._mpf_[1:3])
+    man = property(lambda self: self._mpf_[1])
+    exp = property(lambda self: self._mpf_[2])
+    bc = property(lambda self: self._mpf_[3])
+
+    real = property(lambda self: self)
+    imag = property(lambda self: self.context.zero)
+
+    conjugate = lambda self: self
+
+    def __getstate__(self): return to_pickable(self._mpf_)
+    def __setstate__(self, val): self._mpf_ = from_pickable(val)
+
+    def __repr__(s):
+        if s.context.pretty:
+            return str(s)
+        return "mpf('%s')" % to_str(s._mpf_, s.context._repr_digits)
+
+    def __str__(s): return to_str(s._mpf_, s.context._str_digits)
+    def __hash__(s): return mpf_hash(s._mpf_)
+    def __int__(s): return int(to_int(s._mpf_))
+    def __long__(s): return long(to_int(s._mpf_))
+    def __float__(s): return to_float(s._mpf_, rnd=s.context._prec_rounding[1])
+    def __complex__(s): return complex(float(s))
+    def __nonzero__(s): return s._mpf_ != fzero
+
+    __bool__ = __nonzero__
+
+    def __abs__(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpf_ = mpf_abs(s._mpf_, prec, rounding)
+        return v
+
+    def __pos__(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpf_ = mpf_pos(s._mpf_, prec, rounding)
+        return v
+
+    def __neg__(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpf_ = mpf_neg(s._mpf_, prec, rounding)
+        return v
+
+    def _cmp(s, t, func):
+        if hasattr(t, '_mpf_'):
+            t = t._mpf_
+        else:
+            t = s.mpf_convert_rhs(t)
+            if t is NotImplemented:
+                return t
+        return func(s._mpf_, t)
+
+    def __cmp__(s, t): return s._cmp(t, mpf_cmp)
+    def __lt__(s, t): return s._cmp(t, mpf_lt)
+    def __gt__(s, t): return s._cmp(t, mpf_gt)
+    def __le__(s, t): return s._cmp(t, mpf_le)
+    def __ge__(s, t): return s._cmp(t, mpf_ge)
+
+    def __ne__(s, t):
+        v = s.__eq__(t)
+        if v is NotImplemented:
+            return v
+        return not v
+
+    def __rsub__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if type(t) in int_types:
+            v = new(cls)
+            v._mpf_ = mpf_sub(from_int(t), s._mpf_, prec, rounding)
+            return v
+        t = s.mpf_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t - s
+
+    def __rdiv__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if isinstance(t, int_types):
+            v = new(cls)
+            v._mpf_ = mpf_rdiv_int(t, s._mpf_, prec, rounding)
+            return v
+        t = s.mpf_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t / s
+
+    def __rpow__(s, t):
+        t = s.mpf_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t ** s
+
+    def __rmod__(s, t):
+        t = s.mpf_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t % s
+
+    def sqrt(s):
+        return s.context.sqrt(s)
+
+    def ae(s, t, rel_eps=None, abs_eps=None):
+        return s.context.almosteq(s, t, rel_eps, abs_eps)
+
+    def to_fixed(self, prec):
+        return to_fixed(self._mpf_, prec)
+
+    def __round__(self, *args):
+        return round(float(self), *args)
+
+mpf_binary_op = """
+def %NAME%(self, other):
+    mpf, new, (prec, rounding) = self._ctxdata
+    sval = self._mpf_
+    if hasattr(other, '_mpf_'):
+        tval = other._mpf_
+        %WITH_MPF%
+    ttype = type(other)
+    if ttype in int_types:
+        %WITH_INT%
+    elif ttype is float:
+        tval = from_float(other)
+        %WITH_MPF%
+    elif hasattr(other, '_mpc_'):
+        tval = other._mpc_
+        mpc = type(other)
+        %WITH_MPC%
+    elif ttype is complex:
+        tval = from_float(other.real), from_float(other.imag)
+        mpc = self.context.mpc
+        %WITH_MPC%
+    if isinstance(other, mpnumeric):
+        return NotImplemented
+    try:
+        other = mpf.context.convert(other, strings=False)
+    except TypeError:
+        return NotImplemented
+    return self.%NAME%(other)
+"""
+
+return_mpf = "; obj = new(mpf); obj._mpf_ = val; return obj"
+return_mpc = "; obj = new(mpc); obj._mpc_ = val; return obj"
+
+mpf_pow_same = """
+        try:
+            val = mpf_pow(sval, tval, prec, rounding) %s
+        except ComplexResult:
+            if mpf.context.trap_complex:
+                raise
+            mpc = mpf.context.mpc
+            val = mpc_pow((sval, fzero), (tval, fzero), prec, rounding) %s
+""" % (return_mpf, return_mpc)
+
+def binary_op(name, with_mpf='', with_int='', with_mpc=''):
+    code = mpf_binary_op
+    code = code.replace("%WITH_INT%", with_int)
+    code = code.replace("%WITH_MPC%", with_mpc)
+    code = code.replace("%WITH_MPF%", with_mpf)
+    code = code.replace("%NAME%", name)
+    np = {}
+    exec_(code, globals(), np)
+    return np[name]
+
+_mpf.__eq__ = binary_op('__eq__',
+    'return mpf_eq(sval, tval)',
+    'return mpf_eq(sval, from_int(other))',
+    'return (tval[1] == fzero) and mpf_eq(tval[0], sval)')
+
+_mpf.__add__ = binary_op('__add__',
+    'val = mpf_add(sval, tval, prec, rounding)' + return_mpf,
+    'val = mpf_add(sval, from_int(other), prec, rounding)' + return_mpf,
+    'val = mpc_add_mpf(tval, sval, prec, rounding)' + return_mpc)
+
+_mpf.__sub__ = binary_op('__sub__',
+    'val = mpf_sub(sval, tval, prec, rounding)' + return_mpf,
+    'val = mpf_sub(sval, from_int(other), prec, rounding)' + return_mpf,
+    'val = mpc_sub((sval, fzero), tval, prec, rounding)' + return_mpc)
+
+_mpf.__mul__ = binary_op('__mul__',
+    'val = mpf_mul(sval, tval, prec, rounding)' + return_mpf,
+    'val = mpf_mul_int(sval, other, prec, rounding)' + return_mpf,
+    'val = mpc_mul_mpf(tval, sval, prec, rounding)' + return_mpc)
+
+_mpf.__div__ = binary_op('__div__',
+    'val = mpf_div(sval, tval, prec, rounding)' + return_mpf,
+    'val = mpf_div(sval, from_int(other), prec, rounding)' + return_mpf,
+    'val = mpc_mpf_div(sval, tval, prec, rounding)' + return_mpc)
+
+_mpf.__mod__ = binary_op('__mod__',
+    'val = mpf_mod(sval, tval, prec, rounding)' + return_mpf,
+    'val = mpf_mod(sval, from_int(other), prec, rounding)' + return_mpf,
+    'raise NotImplementedError("complex modulo")')
+
+_mpf.__pow__ = binary_op('__pow__',
+    mpf_pow_same,
+    'val = mpf_pow_int(sval, other, prec, rounding)' + return_mpf,
+    'val = mpc_pow((sval, fzero), tval, prec, rounding)' + return_mpc)
+
+_mpf.__radd__ = _mpf.__add__
+_mpf.__rmul__ = _mpf.__mul__
+_mpf.__truediv__ = _mpf.__div__
+_mpf.__rtruediv__ = _mpf.__rdiv__
+
+
+class _constant(_mpf):
+    """Represents a mathematical constant with dynamic precision.
+    When printed or used in an arithmetic operation, a constant
+    is converted to a regular mpf at the working precision. A
+    regular mpf can also be obtained using the operation +x."""
+
+    def __new__(cls, func, name, docname=''):
+        a = object.__new__(cls)
+        a.name = name
+        a.func = func
+        a.__doc__ = getattr(function_docs, docname, '')
+        return a
+
+    def __call__(self, prec=None, dps=None, rounding=None):
+        prec2, rounding2 = self.context._prec_rounding
+        if not prec: prec = prec2
+        if not rounding: rounding = rounding2
+        if dps: prec = dps_to_prec(dps)
+        return self.context.make_mpf(self.func(prec, rounding))
+
+    @property
+    def _mpf_(self):
+        prec, rounding = self.context._prec_rounding
+        return self.func(prec, rounding)
+
+    def __repr__(self):
+        return "<%s: %s~>" % (self.name, self.context.nstr(self(dps=15)))
+
+
+class _mpc(mpnumeric):
+    """
+    An mpc represents a complex number using a pair of mpf:s (one
+    for the real part and another for the imaginary part.) The mpc
+    class behaves fairly similarly to Python's complex type.
+    """
+
+    __slots__ = ['_mpc_']
+
+    def __new__(cls, real=0, imag=0):
+        s = object.__new__(cls)
+        if isinstance(real, complex_types):
+            real, imag = real.real, real.imag
+        elif hasattr(real, '_mpc_'):
+            s._mpc_ = real._mpc_
+            return s
+        real = cls.context.mpf(real)
+        imag = cls.context.mpf(imag)
+        s._mpc_ = (real._mpf_, imag._mpf_)
+        return s
+
+    real = property(lambda self: self.context.make_mpf(self._mpc_[0]))
+    imag = property(lambda self: self.context.make_mpf(self._mpc_[1]))
+
+    def __getstate__(self):
+        return to_pickable(self._mpc_[0]), to_pickable(self._mpc_[1])
+
+    def __setstate__(self, val):
+        self._mpc_ = from_pickable(val[0]), from_pickable(val[1])
+
+    def __repr__(s):
+        if s.context.pretty:
+            return str(s)
+        r = repr(s.real)[4:-1]
+        i = repr(s.imag)[4:-1]
+        return "%s(real=%s, imag=%s)" % (type(s).__name__, r, i)
+
+    def __str__(s):
+        return "(%s)" % mpc_to_str(s._mpc_, s.context._str_digits)
+
+    def __complex__(s):
+        return mpc_to_complex(s._mpc_, rnd=s.context._prec_rounding[1])
+
+    def __pos__(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpc_ = mpc_pos(s._mpc_, prec, rounding)
+        return v
+
+    def __abs__(s):
+        prec, rounding = s.context._prec_rounding
+        v = new(s.context.mpf)
+        v._mpf_ = mpc_abs(s._mpc_, prec, rounding)
+        return v
+
+    def __neg__(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpc_ = mpc_neg(s._mpc_, prec, rounding)
+        return v
+
+    def conjugate(s):
+        cls, new, (prec, rounding) = s._ctxdata
+        v = new(cls)
+        v._mpc_ = mpc_conjugate(s._mpc_, prec, rounding)
+        return v
+
+    def __nonzero__(s):
+        return mpc_is_nonzero(s._mpc_)
+
+    __bool__ = __nonzero__
+
+    def __hash__(s):
+        return mpc_hash(s._mpc_)
+
+    @classmethod
+    def mpc_convert_lhs(cls, x):
+        try:
+            y = cls.context.convert(x)
+            return y
+        except TypeError:
+            return NotImplemented
+
+    def __eq__(s, t):
+        if not hasattr(t, '_mpc_'):
+            if isinstance(t, str):
+                return False
+            t = s.mpc_convert_lhs(t)
+            if t is NotImplemented:
+                return t
+        return s.real == t.real and s.imag == t.imag
+
+    def __ne__(s, t):
+        b = s.__eq__(t)
+        if b is NotImplemented:
+            return b
+        return not b
+
+    def _compare(*args):
+        raise TypeError("no ordering relation is defined for complex numbers")
+
+    __gt__ = _compare
+    __le__ = _compare
+    __gt__ = _compare
+    __ge__ = _compare
+
+    def __add__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if not hasattr(t, '_mpc_'):
+            t = s.mpc_convert_lhs(t)
+            if t is NotImplemented:
+                return t
+            if hasattr(t, '_mpf_'):
+                v = new(cls)
+                v._mpc_ = mpc_add_mpf(s._mpc_, t._mpf_, prec, rounding)
+                return v
+        v = new(cls)
+        v._mpc_ = mpc_add(s._mpc_, t._mpc_, prec, rounding)
+        return v
+
+    def __sub__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if not hasattr(t, '_mpc_'):
+            t = s.mpc_convert_lhs(t)
+            if t is NotImplemented:
+                return t
+            if hasattr(t, '_mpf_'):
+                v = new(cls)
+                v._mpc_ = mpc_sub_mpf(s._mpc_, t._mpf_, prec, rounding)
+                return v
+        v = new(cls)
+        v._mpc_ = mpc_sub(s._mpc_, t._mpc_, prec, rounding)
+        return v
+
+    def __mul__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if not hasattr(t, '_mpc_'):
+            if isinstance(t, int_types):
+                v = new(cls)
+                v._mpc_ = mpc_mul_int(s._mpc_, t, prec, rounding)
+                return v
+            t = s.mpc_convert_lhs(t)
+            if t is NotImplemented:
+                return t
+            if hasattr(t, '_mpf_'):
+                v = new(cls)
+                v._mpc_ = mpc_mul_mpf(s._mpc_, t._mpf_, prec, rounding)
+                return v
+            t = s.mpc_convert_lhs(t)
+        v = new(cls)
+        v._mpc_ = mpc_mul(s._mpc_, t._mpc_, prec, rounding)
+        return v
+
+    def __div__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if not hasattr(t, '_mpc_'):
+            t = s.mpc_convert_lhs(t)
+            if t is NotImplemented:
+                return t
+            if hasattr(t, '_mpf_'):
+                v = new(cls)
+                v._mpc_ = mpc_div_mpf(s._mpc_, t._mpf_, prec, rounding)
+                return v
+        v = new(cls)
+        v._mpc_ = mpc_div(s._mpc_, t._mpc_, prec, rounding)
+        return v
+
+    def __pow__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if isinstance(t, int_types):
+            v = new(cls)
+            v._mpc_ = mpc_pow_int(s._mpc_, t, prec, rounding)
+            return v
+        t = s.mpc_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        v = new(cls)
+        if hasattr(t, '_mpf_'):
+            v._mpc_ = mpc_pow_mpf(s._mpc_, t._mpf_, prec, rounding)
+        else:
+            v._mpc_ = mpc_pow(s._mpc_, t._mpc_, prec, rounding)
+        return v
+
+    __radd__ = __add__
+
+    def __rsub__(s, t):
+        t = s.mpc_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t - s
+
+    def __rmul__(s, t):
+        cls, new, (prec, rounding) = s._ctxdata
+        if isinstance(t, int_types):
+            v = new(cls)
+            v._mpc_ = mpc_mul_int(s._mpc_, t, prec, rounding)
+            return v
+        t = s.mpc_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t * s
+
+    def __rdiv__(s, t):
+        t = s.mpc_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t / s
+
+    def __rpow__(s, t):
+        t = s.mpc_convert_lhs(t)
+        if t is NotImplemented:
+            return t
+        return t ** s
+
+    __truediv__ = __div__
+    __rtruediv__ = __rdiv__
+
+    def ae(s, t, rel_eps=None, abs_eps=None):
+        return s.context.almosteq(s, t, rel_eps, abs_eps)
+
+
+complex_types = (complex, _mpc)
+
+
+class PythonMPContext(object):
+
+    def __init__(ctx):
+        ctx._prec_rounding = [53, round_nearest]
+        ctx.mpf = type('mpf', (_mpf,), {})
+        ctx.mpc = type('mpc', (_mpc,), {})
+        ctx.mpf._ctxdata = [ctx.mpf, new, ctx._prec_rounding]
+        ctx.mpc._ctxdata = [ctx.mpc, new, ctx._prec_rounding]
+        ctx.mpf.context = ctx
+        ctx.mpc.context = ctx
+        ctx.constant = type('constant', (_constant,), {})
+        ctx.constant._ctxdata = [ctx.mpf, new, ctx._prec_rounding]
+        ctx.constant.context = ctx
+
+    def make_mpf(ctx, v):
+        a = new(ctx.mpf)
+        a._mpf_ = v
+        return a
+
+    def make_mpc(ctx, v):
+        a = new(ctx.mpc)
+        a._mpc_ = v
+        return a
+
+    def default(ctx):
+        ctx._prec = ctx._prec_rounding[0] = 53
+        ctx._dps = 15
+        ctx.trap_complex = False
+
+    def _set_prec(ctx, n):
+        ctx._prec = ctx._prec_rounding[0] = max(1, int(n))
+        ctx._dps = prec_to_dps(n)
+
+    def _set_dps(ctx, n):
+        ctx._prec = ctx._prec_rounding[0] = dps_to_prec(n)
+        ctx._dps = max(1, int(n))
+
+    prec = property(lambda ctx: ctx._prec, _set_prec)
+    dps = property(lambda ctx: ctx._dps, _set_dps)
+
+    def convert(ctx, x, strings=True):
+        """
+        Converts *x* to an ``mpf`` or ``mpc``. If *x* is of type ``mpf``,
+        ``mpc``, ``int``, ``float``, ``complex``, the conversion
+        will be performed losslessly.
+
+        If *x* is a string, the result will be rounded to the present
+        working precision. Strings representing fractions or complex
+        numbers are permitted.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> mpmathify(3.5)
+            mpf('3.5')
+            >>> mpmathify('2.1')
+            mpf('2.1000000000000001')
+            >>> mpmathify('3/4')
+            mpf('0.75')
+            >>> mpmathify('2+3j')
+            mpc(real='2.0', imag='3.0')
+
+        """
+        if type(x) in ctx.types: return x
+        if isinstance(x, int_types): return ctx.make_mpf(from_int(x))
+        if isinstance(x, float): return ctx.make_mpf(from_float(x))
+        if isinstance(x, complex):
+            return ctx.make_mpc((from_float(x.real), from_float(x.imag)))
+        if type(x).__module__ == 'numpy': return ctx.npconvert(x)
+        if isinstance(x, numbers.Rational): # e.g. Fraction
+            try: x = rational.mpq(int(x.numerator), int(x.denominator))
+            except: pass
+        prec, rounding = ctx._prec_rounding
+        if isinstance(x, rational.mpq):
+            p, q = x._mpq_
+            return ctx.make_mpf(from_rational(p, q, prec))
+        if strings and isinstance(x, basestring):
+            try:
+                _mpf_ = from_str(x, prec, rounding)
+                return ctx.make_mpf(_mpf_)
+            except ValueError:
+                pass
+        if hasattr(x, '_mpf_'): return ctx.make_mpf(x._mpf_)
+        if hasattr(x, '_mpc_'): return ctx.make_mpc(x._mpc_)
+        if hasattr(x, '_mpmath_'):
+            return ctx.convert(x._mpmath_(prec, rounding))
+        if type(x).__module__ == 'decimal':
+            try: return ctx.make_mpf(from_Decimal(x, prec, rounding))
+            except: pass
+        return ctx._convert_fallback(x, strings)
+
+    def npconvert(ctx, x):
+        """
+        Converts *x* to an ``mpf`` or ``mpc``. *x* should be a numpy
+        scalar.
+        """
+        import numpy as np
+        if isinstance(x, np.integer): return ctx.make_mpf(from_int(int(x)))
+        if isinstance(x, np.floating): return ctx.make_mpf(from_npfloat(x))
+        if isinstance(x, np.complexfloating):
+            return ctx.make_mpc((from_npfloat(x.real), from_npfloat(x.imag)))
+        raise TypeError("cannot create mpf from " + repr(x))
+
+    def isnan(ctx, x):
+        """
+        Return *True* if *x* is a NaN (not-a-number), or for a complex
+        number, whether either the real or complex part is NaN;
+        otherwise return *False*::
+
+            >>> from mpmath import *
+            >>> isnan(3.14)
+            False
+            >>> isnan(nan)
+            True
+            >>> isnan(mpc(3.14,2.72))
+            False
+            >>> isnan(mpc(3.14,nan))
+            True
+
+        """
+        if hasattr(x, "_mpf_"):
+            return x._mpf_ == fnan
+        if hasattr(x, "_mpc_"):
+            return fnan in x._mpc_
+        if isinstance(x, int_types) or isinstance(x, rational.mpq):
+            return False
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'):
+            return ctx.isnan(x)
+        raise TypeError("isnan() needs a number as input")
+
+    def isinf(ctx, x):
+        """
+        Return *True* if the absolute value of *x* is infinite;
+        otherwise return *False*::
+
+            >>> from mpmath import *
+            >>> isinf(inf)
+            True
+            >>> isinf(-inf)
+            True
+            >>> isinf(3)
+            False
+            >>> isinf(3+4j)
+            False
+            >>> isinf(mpc(3,inf))
+            True
+            >>> isinf(mpc(inf,3))
+            True
+
+        """
+        if hasattr(x, "_mpf_"):
+            return x._mpf_ in (finf, fninf)
+        if hasattr(x, "_mpc_"):
+            re, im = x._mpc_
+            return re in (finf, fninf) or im in (finf, fninf)
+        if isinstance(x, int_types) or isinstance(x, rational.mpq):
+            return False
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'):
+            return ctx.isinf(x)
+        raise TypeError("isinf() needs a number as input")
+
+    def isnormal(ctx, x):
+        """
+        Determine whether *x* is "normal" in the sense of floating-point
+        representation; that is, return *False* if *x* is zero, an
+        infinity or NaN; otherwise return *True*. By extension, a
+        complex number *x* is considered "normal" if its magnitude is
+        normal::
+
+            >>> from mpmath import *
+            >>> isnormal(3)
+            True
+            >>> isnormal(0)
+            False
+            >>> isnormal(inf); isnormal(-inf); isnormal(nan)
+            False
+            False
+            False
+            >>> isnormal(0+0j)
+            False
+            >>> isnormal(0+3j)
+            True
+            >>> isnormal(mpc(2,nan))
+            False
+        """
+        if hasattr(x, "_mpf_"):
+            return bool(x._mpf_[1])
+        if hasattr(x, "_mpc_"):
+            re, im = x._mpc_
+            re_normal = bool(re[1])
+            im_normal = bool(im[1])
+            if re == fzero: return im_normal
+            if im == fzero: return re_normal
+            return re_normal and im_normal
+        if isinstance(x, int_types) or isinstance(x, rational.mpq):
+            return bool(x)
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'):
+            return ctx.isnormal(x)
+        raise TypeError("isnormal() needs a number as input")
+
+    def isint(ctx, x, gaussian=False):
+        """
+        Return *True* if *x* is integer-valued; otherwise return
+        *False*::
+
+            >>> from mpmath import *
+            >>> isint(3)
+            True
+            >>> isint(mpf(3))
+            True
+            >>> isint(3.2)
+            False
+            >>> isint(inf)
+            False
+
+        Optionally, Gaussian integers can be checked for::
+
+            >>> isint(3+0j)
+            True
+            >>> isint(3+2j)
+            False
+            >>> isint(3+2j, gaussian=True)
+            True
+
+        """
+        if isinstance(x, int_types):
+            return True
+        if hasattr(x, "_mpf_"):
+            sign, man, exp, bc = xval = x._mpf_
+            return bool((man and exp >= 0) or xval == fzero)
+        if hasattr(x, "_mpc_"):
+            re, im = x._mpc_
+            rsign, rman, rexp, rbc = re
+            isign, iman, iexp, ibc = im
+            re_isint = (rman and rexp >= 0) or re == fzero
+            if gaussian:
+                im_isint = (iman and iexp >= 0) or im == fzero
+                return re_isint and im_isint
+            return re_isint and im == fzero
+        if isinstance(x, rational.mpq):
+            p, q = x._mpq_
+            return p % q == 0
+        x = ctx.convert(x)
+        if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'):
+            return ctx.isint(x, gaussian)
+        raise TypeError("isint() needs a number as input")
+
+    def fsum(ctx, terms, absolute=False, squared=False):
+        """
+        Calculates a sum containing a finite number of terms (for infinite
+        series, see :func:`~mpmath.nsum`). The terms will be converted to
+        mpmath numbers. For len(terms) > 2, this function is generally
+        faster and produces more accurate results than the builtin
+        Python function :func:`sum`.
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> fsum([1, 2, 0.5, 7])
+            mpf('10.5')
+
+        With squared=True each term is squared, and with absolute=True
+        the absolute value of each term is used.
+        """
+        prec, rnd = ctx._prec_rounding
+        real = []
+        imag = []
+        for term in terms:
+            reval = imval = 0
+            if hasattr(term, "_mpf_"):
+                reval = term._mpf_
+            elif hasattr(term, "_mpc_"):
+                reval, imval = term._mpc_
+            else:
+                term = ctx.convert(term)
+                if hasattr(term, "_mpf_"):
+                    reval = term._mpf_
+                elif hasattr(term, "_mpc_"):
+                    reval, imval = term._mpc_
+                else:
+                    raise NotImplementedError
+            if imval:
+                if squared:
+                    if absolute:
+                        real.append(mpf_mul(reval,reval))
+                        real.append(mpf_mul(imval,imval))
+                    else:
+                        reval, imval = mpc_pow_int((reval,imval),2,prec+10)
+                        real.append(reval)
+                        imag.append(imval)
+                elif absolute:
+                    real.append(mpc_abs((reval,imval), prec))
+                else:
+                    real.append(reval)
+                    imag.append(imval)
+            else:
+                if squared:
+                    reval = mpf_mul(reval, reval)
+                elif absolute:
+                    reval = mpf_abs(reval)
+                real.append(reval)
+        s = mpf_sum(real, prec, rnd, absolute)
+        if imag:
+            s = ctx.make_mpc((s, mpf_sum(imag, prec, rnd)))
+        else:
+            s = ctx.make_mpf(s)
+        return s
+
+    def fdot(ctx, A, B=None, conjugate=False):
+        r"""
+        Computes the dot product of the iterables `A` and `B`,
+
+        .. math ::
+
+            \sum_{k=0} A_k B_k.
+
+        Alternatively, :func:`~mpmath.fdot` accepts a single iterable of pairs.
+        In other words, ``fdot(A,B)`` and ``fdot(zip(A,B))`` are equivalent.
+        The elements are automatically converted to mpmath numbers.
+
+        With ``conjugate=True``, the elements in the second vector
+        will be conjugated:
+
+        .. math ::
+
+            \sum_{k=0} A_k \overline{B_k}
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> A = [2, 1.5, 3]
+            >>> B = [1, -1, 2]
+            >>> fdot(A, B)
+            mpf('6.5')
+            >>> list(zip(A, B))
+            [(2, 1), (1.5, -1), (3, 2)]
+            >>> fdot(_)
+            mpf('6.5')
+            >>> A = [2, 1.5, 3j]
+            >>> B = [1+j, 3, -1-j]
+            >>> fdot(A, B)
+            mpc(real='9.5', imag='-1.0')
+            >>> fdot(A, B, conjugate=True)
+            mpc(real='3.5', imag='-5.0')
+
+        """
+        if B is not None:
+            A = zip(A, B)
+        prec, rnd = ctx._prec_rounding
+        real = []
+        imag = []
+        hasattr_ = hasattr
+        types = (ctx.mpf, ctx.mpc)
+        for a, b in A:
+            if type(a) not in types: a = ctx.convert(a)
+            if type(b) not in types: b = ctx.convert(b)
+            a_real = hasattr_(a, "_mpf_")
+            b_real = hasattr_(b, "_mpf_")
+            if a_real and b_real:
+                real.append(mpf_mul(a._mpf_, b._mpf_))
+                continue
+            a_complex = hasattr_(a, "_mpc_")
+            b_complex = hasattr_(b, "_mpc_")
+            if a_real and b_complex:
+                aval = a._mpf_
+                bre, bim = b._mpc_
+                if conjugate:
+                    bim = mpf_neg(bim)
+                real.append(mpf_mul(aval, bre))
+                imag.append(mpf_mul(aval, bim))
+            elif b_real and a_complex:
+                are, aim = a._mpc_
+                bval = b._mpf_
+                real.append(mpf_mul(are, bval))
+                imag.append(mpf_mul(aim, bval))
+            elif a_complex and b_complex:
+                #re, im = mpc_mul(a._mpc_, b._mpc_, prec+20)
+                are, aim = a._mpc_
+                bre, bim = b._mpc_
+                if conjugate:
+                    bim = mpf_neg(bim)
+                real.append(mpf_mul(are, bre))
+                real.append(mpf_neg(mpf_mul(aim, bim)))
+                imag.append(mpf_mul(are, bim))
+                imag.append(mpf_mul(aim, bre))
+            else:
+                raise NotImplementedError
+        s = mpf_sum(real, prec, rnd)
+        if imag:
+            s = ctx.make_mpc((s, mpf_sum(imag, prec, rnd)))
+        else:
+            s = ctx.make_mpf(s)
+        return s
+
+    def _wrap_libmp_function(ctx, mpf_f, mpc_f=None, mpi_f=None, doc="<no doc>"):
+        """
+        Given a low-level mpf_ function, and optionally similar functions
+        for mpc_ and mpi_, defines the function as a context method.
+
+        It is assumed that the return type is the same as that of
+        the input; the exception is that propagation from mpf to mpc is possible
+        by raising ComplexResult.
+
+        """
+        def f(x, **kwargs):
+            if type(x) not in ctx.types:
+                x = ctx.convert(x)
+            prec, rounding = ctx._prec_rounding
+            if kwargs:
+                prec = kwargs.get('prec', prec)
+                if 'dps' in kwargs:
+                    prec = dps_to_prec(kwargs['dps'])
+                rounding = kwargs.get('rounding', rounding)
+            if hasattr(x, '_mpf_'):
+                try:
+                    return ctx.make_mpf(mpf_f(x._mpf_, prec, rounding))
+                except ComplexResult:
+                    # Handle propagation to complex
+                    if ctx.trap_complex:
+                        raise
+                    return ctx.make_mpc(mpc_f((x._mpf_, fzero), prec, rounding))
+            elif hasattr(x, '_mpc_'):
+                return ctx.make_mpc(mpc_f(x._mpc_, prec, rounding))
+            raise NotImplementedError("%s of a %s" % (name, type(x)))
+        name = mpf_f.__name__[4:]
+        f.__doc__ = function_docs.__dict__.get(name, "Computes the %s of x" % doc)
+        return f
+
+    # Called by SpecialFunctions.__init__()
+    @classmethod
+    def _wrap_specfun(cls, name, f, wrap):
+        if wrap:
+            def f_wrapped(ctx, *args, **kwargs):
+                convert = ctx.convert
+                args = [convert(a) for a in args]
+                prec = ctx.prec
+                try:
+                    ctx.prec += 10
+                    retval = f(ctx, *args, **kwargs)
+                finally:
+                    ctx.prec = prec
+                return +retval
+        else:
+            f_wrapped = f
+        f_wrapped.__doc__ = function_docs.__dict__.get(name, f.__doc__)
+        setattr(cls, name, f_wrapped)
+
+    def _convert_param(ctx, x):
+        if hasattr(x, "_mpc_"):
+            v, im = x._mpc_
+            if im != fzero:
+                return x, 'C'
+        elif hasattr(x, "_mpf_"):
+            v = x._mpf_
+        else:
+            if type(x) in int_types:
+                return int(x), 'Z'
+            p = None
+            if isinstance(x, tuple):
+                p, q = x
+            elif hasattr(x, '_mpq_'):
+                p, q = x._mpq_
+            elif isinstance(x, basestring) and '/' in x:
+                p, q = x.split('/')
+                p = int(p)
+                q = int(q)
+            if p is not None:
+                if not p % q:
+                    return p // q, 'Z'
+                return ctx.mpq(p,q), 'Q'
+            x = ctx.convert(x)
+            if hasattr(x, "_mpc_"):
+                v, im = x._mpc_
+                if im != fzero:
+                    return x, 'C'
+            elif hasattr(x, "_mpf_"):
+                v = x._mpf_
+            else:
+                return x, 'U'
+        sign, man, exp, bc = v
+        if man:
+            if exp >= -4:
+                if sign:
+                    man = -man
+                if exp >= 0:
+                    return int(man) << exp, 'Z'
+                if exp >= -4:
+                    p, q = int(man), (1<<(-exp))
+                    return ctx.mpq(p,q), 'Q'
+            x = ctx.make_mpf(v)
+            return x, 'R'
+        elif not exp:
+            return 0, 'Z'
+        else:
+            return x, 'U'
+
+    def _mpf_mag(ctx, x):
+        sign, man, exp, bc = x
+        if man:
+            return exp+bc
+        if x == fzero:
+            return ctx.ninf
+        if x == finf or x == fninf:
+            return ctx.inf
+        return ctx.nan
+
+    def mag(ctx, x):
+        """
+        Quick logarithmic magnitude estimate of a number. Returns an
+        integer or infinity `m` such that `|x| <= 2^m`. It is not
+        guaranteed that `m` is an optimal bound, but it will never
+        be too large by more than 2 (and probably not more than 1).
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.pretty = True
+            >>> mag(10), mag(10.0), mag(mpf(10)), int(ceil(log(10,2)))
+            (4, 4, 4, 4)
+            >>> mag(10j), mag(10+10j)
+            (4, 5)
+            >>> mag(0.01), int(ceil(log(0.01,2)))
+            (-6, -6)
+            >>> mag(0), mag(inf), mag(-inf), mag(nan)
+            (-inf, +inf, +inf, nan)
+
+        """
+        if hasattr(x, "_mpf_"):
+            return ctx._mpf_mag(x._mpf_)
+        elif hasattr(x, "_mpc_"):
+            r, i = x._mpc_
+            if r == fzero:
+                return ctx._mpf_mag(i)
+            if i == fzero:
+                return ctx._mpf_mag(r)
+            return 1+max(ctx._mpf_mag(r), ctx._mpf_mag(i))
+        elif isinstance(x, int_types):
+            if x:
+                return bitcount(abs(x))
+            return ctx.ninf
+        elif isinstance(x, rational.mpq):
+            p, q = x._mpq_
+            if p:
+                return 1 + bitcount(abs(p)) - bitcount(q)
+            return ctx.ninf
+        else:
+            x = ctx.convert(x)
+            if hasattr(x, "_mpf_") or hasattr(x, "_mpc_"):
+                return ctx.mag(x)
+            else:
+                raise TypeError("requires an mpf/mpc")
+
+
+# Register with "numbers" ABC
+#     We do not subclass, hence we do not use the @abstractmethod checks. While
+#     this is less invasive it may turn out that we do not actually support
+#     parts of the expected interfaces.  See
+#     http://docs.python.org/2/library/numbers.html for list of abstract
+#     methods.
+try:
+    import numbers
+    numbers.Complex.register(_mpc)
+    numbers.Real.register(_mpf)
+except ImportError:
+    pass

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/identification.py

@@ -0,0 +1,844 @@
+"""
+Implements the PSLQ algorithm for integer relation detection,
+and derivative algorithms for constant recognition.
+"""
+
+from .libmp.backend import xrange
+from .libmp import int_types, sqrt_fixed
+
+# round to nearest integer (can be done more elegantly...)
+def round_fixed(x, prec):
+    return ((x + (1<<(prec-1))) >> prec) << prec
+
+class IdentificationMethods(object):
+    pass
+
+
+def pslq(ctx, x, tol=None, maxcoeff=1000, maxsteps=100, verbose=False):
+    r"""
+    Given a vector of real numbers `x = [x_0, x_1, ..., x_n]`, ``pslq(x)``
+    uses the PSLQ algorithm to find a list of integers
+    `[c_0, c_1, ..., c_n]` such that
+
+    .. math ::
+
+        |c_1 x_1 + c_2 x_2 + ... + c_n x_n| < \mathrm{tol}
+
+    and such that `\max |c_k| < \mathrm{maxcoeff}`. If no such vector
+    exists, :func:`~mpmath.pslq` returns ``None``. The tolerance defaults to
+    3/4 of the working precision.
+
+    **Examples**
+
+    Find rational approximations for `\pi`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> pslq([-1, pi], tol=0.01)
+        [22, 7]
+        >>> pslq([-1, pi], tol=0.001)
+        [355, 113]
+        >>> mpf(22)/7; mpf(355)/113; +pi
+        3.14285714285714
+        3.14159292035398
+        3.14159265358979
+
+    Pi is not a rational number with denominator less than 1000::
+
+        >>> pslq([-1, pi])
+        >>>
+
+    To within the standard precision, it can however be approximated
+    by at least one rational number with denominator less than `10^{12}`::
+
+        >>> p, q = pslq([-1, pi], maxcoeff=10**12)
+        >>> print(p); print(q)
+        238410049439
+        75888275702
+        >>> mpf(p)/q
+        3.14159265358979
+
+    The PSLQ algorithm can be applied to long vectors. For example,
+    we can investigate the rational (in)dependence of integer square
+    roots::
+
+        >>> mp.dps = 30
+        >>> pslq([sqrt(n) for n in range(2, 5+1)])
+        >>>
+        >>> pslq([sqrt(n) for n in range(2, 6+1)])
+        >>>
+        >>> pslq([sqrt(n) for n in range(2, 8+1)])
+        [2, 0, 0, 0, 0, 0, -1]
+
+    **Machin formulas**
+
+    A famous formula for `\pi` is Machin's,
+
+    .. math ::
+
+        \frac{\pi}{4} = 4 \operatorname{acot} 5 - \operatorname{acot} 239
+
+    There are actually infinitely many formulas of this type. Two
+    others are
+
+    .. math ::
+
+        \frac{\pi}{4} = \operatorname{acot} 1
+
+        \frac{\pi}{4} = 12 \operatorname{acot} 49 + 32 \operatorname{acot} 57
+            + 5 \operatorname{acot} 239 + 12 \operatorname{acot} 110443
+
+    We can easily verify the formulas using the PSLQ algorithm::
+
+        >>> mp.dps = 30
+        >>> pslq([pi/4, acot(1)])
+        [1, -1]
+        >>> pslq([pi/4, acot(5), acot(239)])
+        [1, -4, 1]
+        >>> pslq([pi/4, acot(49), acot(57), acot(239), acot(110443)])
+        [1, -12, -32, 5, -12]
+
+    We could try to generate a custom Machin-like formula by running
+    the PSLQ algorithm with a few inverse cotangent values, for example
+    acot(2), acot(3) ... acot(10). Unfortunately, there is a linear
+    dependence among these values, resulting in only that dependence
+    being detected, with a zero coefficient for `\pi`::
+
+        >>> pslq([pi] + [acot(n) for n in range(2,11)])
+        [0, 1, -1, 0, 0, 0, -1, 0, 0, 0]
+
+    We get better luck by removing linearly dependent terms::
+
+        >>> pslq([pi] + [acot(n) for n in range(2,11) if n not in (3, 5)])
+        [1, -8, 0, 0, 4, 0, 0, 0]
+
+    In other words, we found the following formula::
+
+        >>> 8*acot(2) - 4*acot(7)
+        3.14159265358979323846264338328
+        >>> +pi
+        3.14159265358979323846264338328
+
+    **Algorithm**
+
+    This is a fairly direct translation to Python of the pseudocode given by
+    David Bailey, "The PSLQ Integer Relation Algorithm":
+    http://www.cecm.sfu.ca/organics/papers/bailey/paper/html/node3.html
+
+    The present implementation uses fixed-point instead of floating-point
+    arithmetic, since this is significantly (about 7x) faster.
+    """
+
+    n = len(x)
+    if n < 2:
+        raise ValueError("n cannot be less than 2")
+
+    # At too low precision, the algorithm becomes meaningless
+    prec = ctx.prec
+    if prec < 53:
+        raise ValueError("prec cannot be less than 53")
+
+    if verbose and prec // max(2,n) < 5:
+        print("Warning: precision for PSLQ may be too low")
+
+    target = int(prec * 0.75)
+
+    if tol is None:
+        tol = ctx.mpf(2)**(-target)
+    else:
+        tol = ctx.convert(tol)
+
+    extra = 60
+    prec += extra
+
+    if verbose:
+        print("PSLQ using prec %i and tol %s" % (prec, ctx.nstr(tol)))
+
+    tol = ctx.to_fixed(tol, prec)
+    assert tol
+
+    # Convert to fixed-point numbers. The dummy None is added so we can
+    # use 1-based indexing. (This just allows us to be consistent with
+    # Bailey's indexing. The algorithm is 100 lines long, so debugging
+    # a single wrong index can be painful.)
+    x = [None] + [ctx.to_fixed(ctx.mpf(xk), prec) for xk in x]
+
+    # Sanity check on magnitudes
+    minx = min(abs(xx) for xx in x[1:])
+    if not minx:
+        raise ValueError("PSLQ requires a vector of nonzero numbers")
+    if minx < tol//100:
+        if verbose:
+            print("STOPPING: (one number is too small)")
+        return None
+
+    g = sqrt_fixed((4<<prec)//3, prec)
+    A = {}
+    B = {}
+    H = {}
+    # Initialization
+    # step 1
+    for i in xrange(1, n+1):
+        for j in xrange(1, n+1):
+            A[i,j] = B[i,j] = (i==j) << prec
+            H[i,j] = 0
+    # step 2
+    s = [None] + [0] * n
+    for k in xrange(1, n+1):
+        t = 0
+        for j in xrange(k, n+1):
+            t += (x[j]**2 >> prec)
+        s[k] = sqrt_fixed(t, prec)
+    t = s[1]
+    y = x[:]
+    for k in xrange(1, n+1):
+        y[k] = (x[k] << prec) // t
+        s[k] = (s[k] << prec) // t
+    # step 3
+    for i in xrange(1, n+1):
+        for j in xrange(i+1, n):
+            H[i,j] = 0
+        if i <= n-1:
+            if s[i]:
+                H[i,i] = (s[i+1] << prec) // s[i]
+            else:
+                H[i,i] = 0
+        for j in range(1, i):
+            sjj1 = s[j]*s[j+1]
+            if sjj1:
+                H[i,j] = ((-y[i]*y[j])<<prec)//sjj1
+            else:
+                H[i,j] = 0
+    # step 4
+    for i in xrange(2, n+1):
+        for j in xrange(i-1, 0, -1):
+            #t = floor(H[i,j]/H[j,j] + 0.5)
+            if H[j,j]:
+                t = round_fixed((H[i,j] << prec)//H[j,j], prec)
+            else:
+                #t = 0
+                continue
+            y[j] = y[j] + (t*y[i] >> prec)
+            for k in xrange(1, j+1):
+                H[i,k] = H[i,k] - (t*H[j,k] >> prec)
+            for k in xrange(1, n+1):
+                A[i,k] = A[i,k] - (t*A[j,k] >> prec)
+                B[k,j] = B[k,j] + (t*B[k,i] >> prec)
+    # Main algorithm
+    for REP in range(maxsteps):
+        # Step 1
+        m = -1
+        szmax = -1
+        for i in range(1, n):
+            h = H[i,i]
+            sz = (g**i * abs(h)) >> (prec*(i-1))
+            if sz > szmax:
+                m = i
+                szmax = sz
+        # Step 2
+        y[m], y[m+1] = y[m+1], y[m]
+        for i in xrange(1,n+1): H[m,i], H[m+1,i] = H[m+1,i], H[m,i]
+        for i in xrange(1,n+1): A[m,i], A[m+1,i] = A[m+1,i], A[m,i]
+        for i in xrange(1,n+1): B[i,m], B[i,m+1] = B[i,m+1], B[i,m]
+        # Step 3
+        if m <= n - 2:
+            t0 = sqrt_fixed((H[m,m]**2 + H[m,m+1]**2)>>prec, prec)
+            # A zero element probably indicates that the precision has
+            # been exhausted. XXX: this could be spurious, due to
+            # using fixed-point arithmetic
+            if not t0:
+                break
+            t1 = (H[m,m] << prec) // t0
+            t2 = (H[m,m+1] << prec) // t0
+            for i in xrange(m, n+1):
+                t3 = H[i,m]
+                t4 = H[i,m+1]
+                H[i,m] = (t1*t3+t2*t4) >> prec
+                H[i,m+1] = (-t2*t3+t1*t4) >> prec
+        # Step 4
+        for i in xrange(m+1, n+1):
+            for j in xrange(min(i-1, m+1), 0, -1):
+                try:
+                    t = round_fixed((H[i,j] << prec)//H[j,j], prec)
+                # Precision probably exhausted
+                except ZeroDivisionError:
+                    break
+                y[j] = y[j] + ((t*y[i]) >> prec)
+                for k in xrange(1, j+1):
+                    H[i,k] = H[i,k] - (t*H[j,k] >> prec)
+                for k in xrange(1, n+1):
+                    A[i,k] = A[i,k] - (t*A[j,k] >> prec)
+                    B[k,j] = B[k,j] + (t*B[k,i] >> prec)
+        # Until a relation is found, the error typically decreases
+        # slowly (e.g. a factor 1-10) with each step TODO: we could
+        # compare err from two successive iterations. If there is a
+        # large drop (several orders of magnitude), that indicates a
+        # "high quality" relation was detected. Reporting this to
+        # the user somehow might be useful.
+        best_err = maxcoeff<<prec
+        for i in xrange(1, n+1):
+            err = abs(y[i])
+            # Maybe we are done?
+            if err < tol:
+                # We are done if the coefficients are acceptable
+                vec = [int(round_fixed(B[j,i], prec) >> prec) for j in \
+                range(1,n+1)]
+                if max(abs(v) for v in vec) < maxcoeff:
+                    if verbose:
+                        print("FOUND relation at iter %i/%i, error: %s" % \
+                            (REP, maxsteps, ctx.nstr(err / ctx.mpf(2)**prec, 1)))
+                    return vec
+            best_err = min(err, best_err)
+        # Calculate a lower bound for the norm. We could do this
+        # more exactly (using the Euclidean norm) but there is probably
+        # no practical benefit.
+        recnorm = max(abs(h) for h in H.values())
+        if recnorm:
+            norm = ((1 << (2*prec)) // recnorm) >> prec
+            norm //= 100
+        else:
+            norm = ctx.inf
+        if verbose:
+            print("%i/%i:  Error: %8s   Norm: %s" % \
+                (REP, maxsteps, ctx.nstr(best_err / ctx.mpf(2)**prec, 1), norm))
+        if norm >= maxcoeff:
+            break
+    if verbose:
+        print("CANCELLING after step %i/%i." % (REP, maxsteps))
+        print("Could not find an integer relation. Norm bound: %s" % norm)
+    return None
+
+def findpoly(ctx, x, n=1, **kwargs):
+    r"""
+    ``findpoly(x, n)`` returns the coefficients of an integer
+    polynomial `P` of degree at most `n` such that `P(x) \approx 0`.
+    If no polynomial having `x` as a root can be found,
+    :func:`~mpmath.findpoly` returns ``None``.
+
+    :func:`~mpmath.findpoly` works by successively calling :func:`~mpmath.pslq` with
+    the vectors `[1, x]`, `[1, x, x^2]`, `[1, x, x^2, x^3]`, ...,
+    `[1, x, x^2, .., x^n]` as input. Keyword arguments given to
+    :func:`~mpmath.findpoly` are forwarded verbatim to :func:`~mpmath.pslq`. In
+    particular, you can specify a tolerance for `P(x)` with ``tol``
+    and a maximum permitted coefficient size with ``maxcoeff``.
+
+    For large values of `n`, it is recommended to run :func:`~mpmath.findpoly`
+    at high precision; preferably 50 digits or more.
+
+    **Examples**
+
+    By default (degree `n = 1`), :func:`~mpmath.findpoly` simply finds a linear
+    polynomial with a rational root::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> findpoly(0.7)
+        [-10, 7]
+
+    The generated coefficient list is valid input to ``polyval`` and
+    ``polyroots``::
+
+        >>> nprint(polyval(findpoly(phi, 2), phi), 1)
+        -2.0e-16
+        >>> for r in polyroots(findpoly(phi, 2)):
+        ...     print(r)
+        ...
+        -0.618033988749895
+        1.61803398874989
+
+    Numbers of the form `m + n \sqrt p` for integers `(m, n, p)` are
+    solutions to quadratic equations. As we find here, `1+\sqrt 2`
+    is a root of the polynomial `x^2 - 2x - 1`::
+
+        >>> findpoly(1+sqrt(2), 2)
+        [1, -2, -1]
+        >>> findroot(lambda x: x**2 - 2*x - 1, 1)
+        2.4142135623731
+
+    Despite only containing square roots, the following number results
+    in a polynomial of degree 4::
+
+        >>> findpoly(sqrt(2)+sqrt(3), 4)
+        [1, 0, -10, 0, 1]
+
+    In fact, `x^4 - 10x^2 + 1` is the *minimal polynomial* of
+    `r = \sqrt 2 + \sqrt 3`, meaning that a rational polynomial of
+    lower degree having `r` as a root does not exist. Given sufficient
+    precision, :func:`~mpmath.findpoly` will usually find the correct
+    minimal polynomial of a given algebraic number.
+
+    **Non-algebraic numbers**
+
+    If :func:`~mpmath.findpoly` fails to find a polynomial with given
+    coefficient size and tolerance constraints, that means no such
+    polynomial exists.
+
+    We can verify that `\pi` is not an algebraic number of degree 3 with
+    coefficients less than 1000::
+
+        >>> mp.dps = 15
+        >>> findpoly(pi, 3)
+        >>>
+
+    It is always possible to find an algebraic approximation of a number
+    using one (or several) of the following methods:
+
+        1. Increasing the permitted degree
+        2. Allowing larger coefficients
+        3. Reducing the tolerance
+
+    One example of each method is shown below::
+
+        >>> mp.dps = 15
+        >>> findpoly(pi, 4)
+        [95, -545, 863, -183, -298]
+        >>> findpoly(pi, 3, maxcoeff=10000)
+        [836, -1734, -2658, -457]
+        >>> findpoly(pi, 3, tol=1e-7)
+        [-4, 22, -29, -2]
+
+    It is unknown whether Euler's constant is transcendental (or even
+    irrational). We can use :func:`~mpmath.findpoly` to check that if is
+    an algebraic number, its minimal polynomial must have degree
+    at least 7 and a coefficient of magnitude at least 1000000::
+
+        >>> mp.dps = 200
+        >>> findpoly(euler, 6, maxcoeff=10**6, tol=1e-100, maxsteps=1000)
+        >>>
+
+    Note that the high precision and strict tolerance is necessary
+    for such high-degree runs, since otherwise unwanted low-accuracy
+    approximations will be detected. It may also be necessary to set
+    maxsteps high to prevent a premature exit (before the coefficient
+    bound has been reached). Running with ``verbose=True`` to get an
+    idea what is happening can be useful.
+    """
+    x = ctx.mpf(x)
+    if n < 1:
+        raise ValueError("n cannot be less than 1")
+    if x == 0:
+        return [1, 0]
+    xs = [ctx.mpf(1)]
+    for i in range(1,n+1):
+        xs.append(x**i)
+        a = ctx.pslq(xs, **kwargs)
+        if a is not None:
+            return a[::-1]
+
+def fracgcd(p, q):
+    x, y = p, q
+    while y:
+        x, y = y, x % y
+    if x != 1:
+        p //= x
+        q //= x
+    if q == 1:
+        return p
+    return p, q
+
+def pslqstring(r, constants):
+    q = r[0]
+    r = r[1:]
+    s = []
+    for i in range(len(r)):
+        p = r[i]
+        if p:
+            z = fracgcd(-p,q)
+            cs = constants[i][1]
+            if cs == '1':
+                cs = ''
+            else:
+                cs = '*' + cs
+            if isinstance(z, int_types):
+                if z > 0: term = str(z) + cs
+                else:     term = ("(%s)" % z) + cs
+            else:
+                term = ("(%s/%s)" % z) + cs
+            s.append(term)
+    s = ' + '.join(s)
+    if '+' in s or '*' in s:
+        s = '(' + s + ')'
+    return s or '0'
+
+def prodstring(r, constants):
+    q = r[0]
+    r = r[1:]
+    num = []
+    den = []
+    for i in range(len(r)):
+        p = r[i]
+        if p:
+            z = fracgcd(-p,q)
+            cs = constants[i][1]
+            if isinstance(z, int_types):
+                if abs(z) == 1: t = cs
+                else:           t = '%s**%s' % (cs, abs(z))
+                ([num,den][z<0]).append(t)
+            else:
+                t = '%s**(%s/%s)' % (cs, abs(z[0]), z[1])
+                ([num,den][z[0]<0]).append(t)
+    num = '*'.join(num)
+    den = '*'.join(den)
+    if num and den: return "(%s)/(%s)" % (num, den)
+    if num: return num
+    if den: return "1/(%s)" % den
+
+def quadraticstring(ctx,t,a,b,c):
+    if c < 0:
+        a,b,c = -a,-b,-c
+    u1 = (-b+ctx.sqrt(b**2-4*a*c))/(2*c)
+    u2 = (-b-ctx.sqrt(b**2-4*a*c))/(2*c)
+    if abs(u1-t) < abs(u2-t):
+        if b:  s = '((%s+sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c)
+        else:  s = '(sqrt(%s)/%s)' % (-4*a*c,2*c)
+    else:
+        if b:  s = '((%s-sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c)
+        else:  s = '(-sqrt(%s)/%s)' % (-4*a*c,2*c)
+    return s
+
+# Transformation y = f(x,c), with inverse function x = f(y,c)
+# The third entry indicates whether the transformation is
+# redundant when c = 1
+transforms = [
+  (lambda ctx,x,c: x*c, '$y/$c', 0),
+  (lambda ctx,x,c: x/c, '$c*$y', 1),
+  (lambda ctx,x,c: c/x, '$c/$y', 0),
+  (lambda ctx,x,c: (x*c)**2, 'sqrt($y)/$c', 0),
+  (lambda ctx,x,c: (x/c)**2, '$c*sqrt($y)', 1),
+  (lambda ctx,x,c: (c/x)**2, '$c/sqrt($y)', 0),
+  (lambda ctx,x,c: c*x**2, 'sqrt($y)/sqrt($c)', 1),
+  (lambda ctx,x,c: x**2/c, 'sqrt($c)*sqrt($y)', 1),
+  (lambda ctx,x,c: c/x**2, 'sqrt($c)/sqrt($y)', 1),
+  (lambda ctx,x,c: ctx.sqrt(x*c), '$y**2/$c', 0),
+  (lambda ctx,x,c: ctx.sqrt(x/c), '$c*$y**2', 1),
+  (lambda ctx,x,c: ctx.sqrt(c/x), '$c/$y**2', 0),
+  (lambda ctx,x,c: c*ctx.sqrt(x), '$y**2/$c**2', 1),
+  (lambda ctx,x,c: ctx.sqrt(x)/c, '$c**2*$y**2', 1),
+  (lambda ctx,x,c: c/ctx.sqrt(x), '$c**2/$y**2', 1),
+  (lambda ctx,x,c: ctx.exp(x*c), 'log($y)/$c', 0),
+  (lambda ctx,x,c: ctx.exp(x/c), '$c*log($y)', 1),
+  (lambda ctx,x,c: ctx.exp(c/x), '$c/log($y)', 0),
+  (lambda ctx,x,c: c*ctx.exp(x), 'log($y/$c)', 1),
+  (lambda ctx,x,c: ctx.exp(x)/c, 'log($c*$y)', 1),
+  (lambda ctx,x,c: c/ctx.exp(x), 'log($c/$y)', 0),
+  (lambda ctx,x,c: ctx.ln(x*c), 'exp($y)/$c', 0),
+  (lambda ctx,x,c: ctx.ln(x/c), '$c*exp($y)', 1),
+  (lambda ctx,x,c: ctx.ln(c/x), '$c/exp($y)', 0),
+  (lambda ctx,x,c: c*ctx.ln(x), 'exp($y/$c)', 1),
+  (lambda ctx,x,c: ctx.ln(x)/c, 'exp($c*$y)', 1),
+  (lambda ctx,x,c: c/ctx.ln(x), 'exp($c/$y)', 0),
+]
+
+def identify(ctx, x, constants=[], tol=None, maxcoeff=1000, full=False,
+    verbose=False):
+    r"""
+    Given a real number `x`, ``identify(x)`` attempts to find an exact
+    formula for `x`. This formula is returned as a string. If no match
+    is found, ``None`` is returned. With ``full=True``, a list of
+    matching formulas is returned.
+
+    As a simple example, :func:`~mpmath.identify` will find an algebraic
+    formula for the golden ratio::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> identify(phi)
+        '((1+sqrt(5))/2)'
+
+    :func:`~mpmath.identify` can identify simple algebraic numbers and simple
+    combinations of given base constants, as well as certain basic
+    transformations thereof. More specifically, :func:`~mpmath.identify`
+    looks for the following:
+
+        1. Fractions
+        2. Quadratic algebraic numbers
+        3. Rational linear combinations of the base constants
+        4. Any of the above after first transforming `x` into `f(x)` where
+           `f(x)` is `1/x`, `\sqrt x`, `x^2`, `\log x` or `\exp x`, either
+           directly or with `x` or `f(x)` multiplied or divided by one of
+           the base constants
+        5. Products of fractional powers of the base constants and
+           small integers
+
+    Base constants can be given as a list of strings representing mpmath
+    expressions (:func:`~mpmath.identify` will ``eval`` the strings to numerical
+    values and use the original strings for the output), or as a dict of
+    formula:value pairs.
+
+    In order not to produce spurious results, :func:`~mpmath.identify` should
+    be used with high precision; preferably 50 digits or more.
+
+    **Examples**
+
+    Simple identifications can be performed safely at standard
+    precision. Here the default recognition of rational, algebraic,
+    and exp/log of algebraic numbers is demonstrated::
+
+        >>> mp.dps = 15
+        >>> identify(0.22222222222222222)
+        '(2/9)'
+        >>> identify(1.9662210973805663)
+        'sqrt(((24+sqrt(48))/8))'
+        >>> identify(4.1132503787829275)
+        'exp((sqrt(8)/2))'
+        >>> identify(0.881373587019543)
+        'log(((2+sqrt(8))/2))'
+
+    By default, :func:`~mpmath.identify` does not recognize `\pi`. At standard
+    precision it finds a not too useful approximation. At slightly
+    increased precision, this approximation is no longer accurate
+    enough and :func:`~mpmath.identify` more correctly returns ``None``::
+
+        >>> identify(pi)
+        '(2**(176/117)*3**(20/117)*5**(35/39))/(7**(92/117))'
+        >>> mp.dps = 30
+        >>> identify(pi)
+        >>>
+
+    Numbers such as `\pi`, and simple combinations of user-defined
+    constants, can be identified if they are provided explicitly::
+
+        >>> identify(3*pi-2*e, ['pi', 'e'])
+        '(3*pi + (-2)*e)'
+
+    Here is an example using a dict of constants. Note that the
+    constants need not be "atomic"; :func:`~mpmath.identify` can just
+    as well express the given number in terms of expressions
+    given by formulas::
+
+        >>> identify(pi+e, {'a':pi+2, 'b':2*e})
+        '((-2) + 1*a + (1/2)*b)'
+
+    Next, we attempt some identifications with a set of base constants.
+    It is necessary to increase the precision a bit.
+
+        >>> mp.dps = 50
+        >>> base = ['sqrt(2)','pi','log(2)']
+        >>> identify(0.25, base)
+        '(1/4)'
+        >>> identify(3*pi + 2*sqrt(2) + 5*log(2)/7, base)
+        '(2*sqrt(2) + 3*pi + (5/7)*log(2))'
+        >>> identify(exp(pi+2), base)
+        'exp((2 + 1*pi))'
+        >>> identify(1/(3+sqrt(2)), base)
+        '((3/7) + (-1/7)*sqrt(2))'
+        >>> identify(sqrt(2)/(3*pi+4), base)
+        'sqrt(2)/(4 + 3*pi)'
+        >>> identify(5**(mpf(1)/3)*pi*log(2)**2, base)
+        '5**(1/3)*pi*log(2)**2'
+
+    An example of an erroneous solution being found when too low
+    precision is used::
+
+        >>> mp.dps = 15
+        >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)'])
+        '((11/25) + (-158/75)*pi + (76/75)*e + (44/15)*sqrt(2))'
+        >>> mp.dps = 50
+        >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)'])
+        '1/(3*pi + (-4)*e + 2*sqrt(2))'
+
+    **Finding approximate solutions**
+
+    The tolerance ``tol`` defaults to 3/4 of the working precision.
+    Lowering the tolerance is useful for finding approximate matches.
+    We can for example try to generate approximations for pi::
+
+        >>> mp.dps = 15
+        >>> identify(pi, tol=1e-2)
+        '(22/7)'
+        >>> identify(pi, tol=1e-3)
+        '(355/113)'
+        >>> identify(pi, tol=1e-10)
+        '(5**(339/269))/(2**(64/269)*3**(13/269)*7**(92/269))'
+
+    With ``full=True``, and by supplying a few base constants,
+    ``identify`` can generate almost endless lists of approximations
+    for any number (the output below has been truncated to show only
+    the first few)::
+
+        >>> for p in identify(pi, ['e', 'catalan'], tol=1e-5, full=True):
+        ...     print(p)
+        ...  # doctest: +ELLIPSIS
+        e/log((6 + (-4/3)*e))
+        (3**3*5*e*catalan**2)/(2*7**2)
+        sqrt(((-13) + 1*e + 22*catalan))
+        log(((-6) + 24*e + 4*catalan)/e)
+        exp(catalan*((-1/5) + (8/15)*e))
+        catalan*(6 + (-6)*e + 15*catalan)
+        sqrt((5 + 26*e + (-3)*catalan))/e
+        e*sqrt(((-27) + 2*e + 25*catalan))
+        log(((-1) + (-11)*e + 59*catalan))
+        ((3/20) + (21/20)*e + (3/20)*catalan)
+        ...
+
+    The numerical values are roughly as close to `\pi` as permitted by the
+    specified tolerance:
+
+        >>> e/log(6-4*e/3)
+        3.14157719846001
+        >>> 135*e*catalan**2/98
+        3.14166950419369
+        >>> sqrt(e-13+22*catalan)
+        3.14158000062992
+        >>> log(24*e-6+4*catalan)-1
+        3.14158791577159
+
+    **Symbolic processing**
+
+    The output formula can be evaluated as a Python expression.
+    Note however that if fractions (like '2/3') are present in
+    the formula, Python's :func:`~mpmath.eval()` may erroneously perform
+    integer division. Note also that the output is not necessarily
+    in the algebraically simplest form::
+
+        >>> identify(sqrt(2))
+        '(sqrt(8)/2)'
+
+    As a solution to both problems, consider using SymPy's
+    :func:`~mpmath.sympify` to convert the formula into a symbolic expression.
+    SymPy can be used to pretty-print or further simplify the formula
+    symbolically::
+
+        >>> from sympy import sympify # doctest: +SKIP
+        >>> sympify(identify(sqrt(2))) # doctest: +SKIP
+        2**(1/2)
+
+    Sometimes :func:`~mpmath.identify` can simplify an expression further than
+    a symbolic algorithm::
+
+        >>> from sympy import simplify # doctest: +SKIP
+        >>> x = sympify('-1/(-3/2+(1/2)*5**(1/2))*(3/2-1/2*5**(1/2))**(1/2)') # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        (3/2 - 5**(1/2)/2)**(-1/2)
+        >>> x = simplify(x) # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        2/(6 - 2*5**(1/2))**(1/2)
+        >>> mp.dps = 30 # doctest: +SKIP
+        >>> x = sympify(identify(x.evalf(30))) # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        1/2 + 5**(1/2)/2
+
+    (In fact, this functionality is available directly in SymPy as the
+    function :func:`~mpmath.nsimplify`, which is essentially a wrapper for
+    :func:`~mpmath.identify`.)
+
+    **Miscellaneous issues and limitations**
+
+    The input `x` must be a real number. All base constants must be
+    positive real numbers and must not be rationals or rational linear
+    combinations of each other.
+
+    The worst-case computation time grows quickly with the number of
+    base constants. Already with 3 or 4 base constants,
+    :func:`~mpmath.identify` may require several seconds to finish. To search
+    for relations among a large number of constants, you should
+    consider using :func:`~mpmath.pslq` directly.
+
+    The extended transformations are applied to x, not the constants
+    separately. As a result, ``identify`` will for example be able to
+    recognize ``exp(2*pi+3)`` with ``pi`` given as a base constant, but
+    not ``2*exp(pi)+3``. It will be able to recognize the latter if
+    ``exp(pi)`` is given explicitly as a base constant.
+
+    """
+
+    solutions = []
+
+    def addsolution(s):
+        if verbose: print("Found: ", s)
+        solutions.append(s)
+
+    x = ctx.mpf(x)
+
+    # Further along, x will be assumed positive
+    if x == 0:
+        if full: return ['0']
+        else:    return '0'
+    if x < 0:
+        sol = ctx.identify(-x, constants, tol, maxcoeff, full, verbose)
+        if sol is None:
+            return sol
+        if full:
+            return ["-(%s)"%s for s in sol]
+        else:
+            return "-(%s)" % sol
+
+    if tol:
+        tol = ctx.mpf(tol)
+    else:
+        tol = ctx.eps**0.7
+    M = maxcoeff
+
+    if constants:
+        if isinstance(constants, dict):
+            constants = [(ctx.mpf(v), name) for (name, v) in sorted(constants.items())]
+        else:
+            namespace = dict((name, getattr(ctx,name)) for name in dir(ctx))
+            constants = [(eval(p, namespace), p) for p in constants]
+    else:
+        constants = []
+
+    # We always want to find at least rational terms
+    if 1 not in [value for (name, value) in constants]:
+        constants = [(ctx.mpf(1), '1')] + constants
+
+    # PSLQ with simple algebraic and functional transformations
+    for ft, ftn, red in transforms:
+        for c, cn in constants:
+            if red and cn == '1':
+                continue
+            t = ft(ctx,x,c)
+            # Prevent exponential transforms from wreaking havoc
+            if abs(t) > M**2 or abs(t) < tol:
+                continue
+            # Linear combination of base constants
+            r = ctx.pslq([t] + [a[0] for a in constants], tol, M)
+            s = None
+            if r is not None and max(abs(uw) for uw in r) <= M and r[0]:
+                s = pslqstring(r, constants)
+            # Quadratic algebraic numbers
+            else:
+                q = ctx.pslq([ctx.one, t, t**2], tol, M)
+                if q is not None and len(q) == 3 and q[2]:
+                    aa, bb, cc = q
+                    if max(abs(aa),abs(bb),abs(cc)) <= M:
+                        s = quadraticstring(ctx,t,aa,bb,cc)
+            if s:
+                if cn == '1' and ('/$c' in ftn):
+                    s = ftn.replace('$y', s).replace('/$c', '')
+                else:
+                    s = ftn.replace('$y', s).replace('$c', cn)
+                addsolution(s)
+                if not full: return solutions[0]
+
+            if verbose:
+                print(".")
+
+    # Check for a direct multiplicative formula
+    if x != 1:
+        # Allow fractional powers of fractions
+        ilogs = [2,3,5,7]
+        # Watch out for existing fractional powers of fractions
+        logs = []
+        for a, s in constants:
+            if not sum(bool(ctx.findpoly(ctx.ln(a)/ctx.ln(i),1)) for i in ilogs):
+                logs.append((ctx.ln(a), s))
+        logs = [(ctx.ln(i),str(i)) for i in ilogs] + logs
+        r = ctx.pslq([ctx.ln(x)] + [a[0] for a in logs], tol, M)
+        if r is not None and max(abs(uw) for uw in r) <= M and r[0]:
+            addsolution(prodstring(r, logs))
+            if not full: return solutions[0]
+
+    if full:
+        return sorted(solutions, key=len)
+    else:
+        return None
+
+IdentificationMethods.pslq = pslq
+IdentificationMethods.findpoly = findpoly
+IdentificationMethods.identify = identify
+
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/rational.py

@@ -0,0 +1,240 @@
+import operator
+import sys
+from .libmp import int_types, mpf_hash, bitcount, from_man_exp, HASH_MODULUS
+
+new = object.__new__
+
+def create_reduced(p, q, _cache={}):
+    key = p, q
+    if key in _cache:
+        return _cache[key]
+    x, y = p, q
+    while y:
+        x, y = y, x % y
+    if x != 1:
+        p //= x
+        q //= x
+    v = new(mpq)
+    v._mpq_ = p, q
+    # Speedup integers, half-integers and other small fractions
+    if q <= 4 and abs(key[0]) < 100:
+        _cache[key] = v
+    return v
+
+class mpq(object):
+    """
+    Exact rational type, currently only intended for internal use.
+    """
+
+    __slots__ = ["_mpq_"]
+
+    def __new__(cls, p, q=1):
+        if type(p) is tuple:
+            p, q = p
+        elif hasattr(p, '_mpq_'):
+            p, q = p._mpq_
+        return create_reduced(p, q)
+
+    def __repr__(s):
+        return "mpq(%s,%s)" % s._mpq_
+
+    def __str__(s):
+        return "(%s/%s)" % s._mpq_
+
+    def __int__(s):
+        a, b = s._mpq_
+        return a // b
+
+    def __nonzero__(s):
+        return bool(s._mpq_[0])
+
+    __bool__ = __nonzero__
+
+    def __hash__(s):
+        a, b = s._mpq_
+        if sys.version_info >= (3, 2):
+            inverse = pow(b, HASH_MODULUS-2, HASH_MODULUS)
+            if not inverse:
+                h = sys.hash_info.inf
+            else:
+                h = (abs(a) * inverse) % HASH_MODULUS
+            if a < 0: h = -h
+            if h == -1: h = -2
+            return h
+        else:
+            if b == 1:
+                return hash(a)
+            # Power of two: mpf compatible hash
+            if not (b & (b-1)):
+                return mpf_hash(from_man_exp(a, 1-bitcount(b)))
+            return hash((a,b))
+
+    def __eq__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            return s._mpq_ == t._mpq_
+        if ttype in int_types:
+            a, b = s._mpq_
+            if b != 1:
+                return False
+            return a == t
+        return NotImplemented
+
+    def __ne__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            return s._mpq_ != t._mpq_
+        if ttype in int_types:
+            a, b = s._mpq_
+            if b != 1:
+                return True
+            return a != t
+        return NotImplemented
+
+    def _cmp(s, t, op):
+        ttype = type(t)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return op(a, t*b)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return op(a*d, b*c)
+        return NotImplementedError
+
+    def __lt__(s, t): return s._cmp(t, operator.lt)
+    def __le__(s, t): return s._cmp(t, operator.le)
+    def __gt__(s, t): return s._cmp(t, operator.gt)
+    def __ge__(s, t): return s._cmp(t, operator.ge)
+
+    def __abs__(s):
+        a, b = s._mpq_
+        if a >= 0:
+            return s
+        v = new(mpq)
+        v._mpq_ = -a, b
+        return v
+
+    def __neg__(s):
+        a, b = s._mpq_
+        v = new(mpq)
+        v._mpq_ = -a, b
+        return v
+
+    def __pos__(s):
+        return s
+
+    def __add__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d+b*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = a+b*t, b
+            return v
+        return NotImplemented
+
+    __radd__ = __add__
+
+    def __sub__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d-b*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = a-b*t, b
+            return v
+        return NotImplemented
+
+    def __rsub__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(b*c-a*d, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = b*t-a, b
+            return v
+        return NotImplemented
+
+    def __mul__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(a*t, b)
+        return NotImplemented
+
+    __rmul__ = __mul__
+
+    def __div__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d, b*c)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(a, b*t)
+        return NotImplemented
+
+    def __rdiv__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(b*c, a*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(b*t, a)
+        return NotImplemented
+
+    def __pow__(s, t):
+        ttype = type(t)
+        if ttype in int_types:
+            a, b = s._mpq_
+            if t:
+                if t < 0:
+                    a, b, t = b, a, -t
+                v = new(mpq)
+                v._mpq_ = a**t, b**t
+                return v
+            raise ZeroDivisionError
+        return NotImplemented
+
+
+mpq_1 = mpq((1,1))
+mpq_0 = mpq((0,1))
+mpq_1_2 = mpq((1,2))
+mpq_3_2 = mpq((3,2))
+mpq_1_4 = mpq((1,4))
+mpq_1_16 = mpq((1,16))
+mpq_3_16 = mpq((3,16))
+mpq_5_2 = mpq((5,2))
+mpq_3_4 = mpq((3,4))
+mpq_7_4 = mpq((7,4))
+mpq_5_4 = mpq((5,4))
+
+
+# Register with "numbers" ABC
+#     We do not subclass, hence we do not use the @abstractmethod checks. While
+#     this is less invasive it may turn out that we do not actually support
+#     parts of the expected interfaces.  See
+#     http://docs.python.org/2/library/numbers.html for list of abstract
+#     methods.
+try:
+    import numbers
+    numbers.Rational.register(mpq)
+except ImportError:
+    pass

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/usertools.py

@@ -0,0 +1,93 @@
+
+def monitor(f, input='print', output='print'):
+    """
+    Returns a wrapped copy of *f* that monitors evaluation by calling
+    *input* with every input (*args*, *kwargs*) passed to *f* and
+    *output* with every value returned from *f*. The default action
+    (specify using the special string value ``'print'``) is to print
+    inputs and outputs to stdout, along with the total evaluation
+    count::
+
+        >>> from mpmath import *
+        >>> mp.dps = 5; mp.pretty = False
+        >>> diff(monitor(exp), 1)   # diff will eval f(x-h) and f(x+h)
+        in  0 (mpf('0.99999999906867742538452148'),) {}
+        out 0 mpf('2.7182818259274480055282064')
+        in  1 (mpf('1.0000000009313225746154785'),) {}
+        out 1 mpf('2.7182818309906424675501024')
+        mpf('2.7182808')
+
+    To disable either the input or the output handler, you may
+    pass *None* as argument.
+
+    Custom input and output handlers may be used e.g. to store
+    results for later analysis::
+
+        >>> mp.dps = 15
+        >>> input = []
+        >>> output = []
+        >>> findroot(monitor(sin, input.append, output.append), 3.0)
+        mpf('3.1415926535897932')
+        >>> len(input)  # Count number of evaluations
+        9
+        >>> print(input[3]); print(output[3])
+        ((mpf('3.1415076583334066'),), {})
+        8.49952562843408e-5
+        >>> print(input[4]); print(output[4])
+        ((mpf('3.1415928201669122'),), {})
+        -1.66577118985331e-7
+
+    """
+    if not input:
+        input = lambda v: None
+    elif input == 'print':
+        incount = [0]
+        def input(value):
+            args, kwargs = value
+            print("in  %s %r %r" % (incount[0], args, kwargs))
+            incount[0] += 1
+    if not output:
+        output = lambda v: None
+    elif output == 'print':
+        outcount = [0]
+        def output(value):
+            print("out %s %r" % (outcount[0], value))
+            outcount[0] += 1
+    def f_monitored(*args, **kwargs):
+        input((args, kwargs))
+        v = f(*args, **kwargs)
+        output(v)
+        return v
+    return f_monitored
+
+def timing(f, *args, **kwargs):
+    """
+    Returns time elapsed for evaluating ``f()``. Optionally arguments
+    may be passed to time the execution of ``f(*args, **kwargs)``.
+
+    If the first call is very quick, ``f`` is called
+    repeatedly and the best time is returned.
+    """
+    once = kwargs.get('once')
+    if 'once' in kwargs:
+        del kwargs['once']
+    if args or kwargs:
+        if len(args) == 1 and not kwargs:
+            arg = args[0]
+            g = lambda: f(arg)
+        else:
+            g = lambda: f(*args, **kwargs)
+    else:
+        g = f
+    from timeit import default_timer as clock
+    t1=clock(); v=g(); t2=clock(); t=t2-t1
+    if t > 0.05 or once:
+        return t
+    for i in range(3):
+        t1=clock();
+        # Evaluate multiple times because the timer function
+        # has a significant overhead
+        g();g();g();g();g();g();g();g();g();g()
+        t2=clock()
+        t=min(t,(t2-t1)/10)
+    return t

tuning-competition-baseline/.venv/lib/python3.11/site-packages/mpmath/visualization.py

@@ -0,0 +1,313 @@
+"""
+Plotting (requires matplotlib)
+"""
+
+from colorsys import hsv_to_rgb, hls_to_rgb
+from .libmp import NoConvergence
+from .libmp.backend import xrange
+
+class VisualizationMethods(object):
+    plot_ignore = (ValueError, ArithmeticError, ZeroDivisionError, NoConvergence)
+
+def plot(ctx, f, xlim=[-5,5], ylim=None, points=200, file=None, dpi=None,
+    singularities=[], axes=None):
+    r"""
+    Shows a simple 2D plot of a function `f(x)` or list of functions
+    `[f_0(x), f_1(x), \ldots, f_n(x)]` over a given interval
+    specified by *xlim*. Some examples::
+
+        plot(lambda x: exp(x)*li(x), [1, 4])
+        plot([cos, sin], [-4, 4])
+        plot([fresnels, fresnelc], [-4, 4])
+        plot([sqrt, cbrt], [-4, 4])
+        plot(lambda t: zeta(0.5+t*j), [-20, 20])
+        plot([floor, ceil, abs, sign], [-5, 5])
+
+    Points where the function raises a numerical exception or
+    returns an infinite value are removed from the graph.
+    Singularities can also be excluded explicitly
+    as follows (useful for removing erroneous vertical lines)::
+
+        plot(cot, ylim=[-5, 5])   # bad
+        plot(cot, ylim=[-5, 5], singularities=[-pi, 0, pi])  # good
+
+    For parts where the function assumes complex values, the
+    real part is plotted with dashes and the imaginary part
+    is plotted with dots.
+
+    .. note :: This function requires matplotlib (pylab).
+    """
+    if file:
+        axes = None
+    fig = None
+    if not axes:
+        import pylab
+        fig = pylab.figure()
+        axes = fig.add_subplot(111)
+    if not isinstance(f, (tuple, list)):
+        f = [f]
+    a, b = xlim
+    colors = ['b', 'r', 'g', 'm', 'k']
+    for n, func in enumerate(f):
+        x = ctx.arange(a, b, (b-a)/float(points))
+        segments = []
+        segment = []
+        in_complex = False
+        for i in xrange(len(x)):
+            try:
+                if i != 0:
+                    for sing in singularities:
+                        if x[i-1] <= sing and x[i] >= sing:
+                            raise ValueError
+                v = func(x[i])
+                if ctx.isnan(v) or abs(v) > 1e300:
+                    raise ValueError
+                if hasattr(v, "imag") and v.imag:
+                    re = float(v.real)
+                    im = float(v.imag)
+                    if not in_complex:
+                        in_complex = True
+                        segments.append(segment)
+                        segment = []
+                    segment.append((float(x[i]), re, im))
+                else:
+                    if in_complex:
+                        in_complex = False
+                        segments.append(segment)
+                        segment = []
+                    if hasattr(v, "real"):
+                        v = v.real
+                    segment.append((float(x[i]), v))
+            except ctx.plot_ignore:
+                if segment:
+                    segments.append(segment)
+                segment = []
+        if segment:
+            segments.append(segment)
+        for segment in segments:
+            x = [s[0] for s in segment]
+            y = [s[1] for s in segment]
+            if not x:
+                continue
+            c = colors[n % len(colors)]
+            if len(segment[0]) == 3:
+                z = [s[2] for s in segment]
+                axes.plot(x, y, '--'+c, linewidth=3)
+                axes.plot(x, z, ':'+c, linewidth=3)
+            else:
+                axes.plot(x, y, c, linewidth=3)
+    axes.set_xlim([float(_) for _ in xlim])
+    if ylim:
+        axes.set_ylim([float(_) for _ in ylim])
+    axes.set_xlabel('x')
+    axes.set_ylabel('f(x)')
+    axes.grid(True)
+    if fig:
+        if file:
+            pylab.savefig(file, dpi=dpi)
+        else:
+            pylab.show()
+
+def default_color_function(ctx, z):
+    if ctx.isinf(z):
+        return (1.0, 1.0, 1.0)
+    if ctx.isnan(z):
+        return (0.5, 0.5, 0.5)
+    pi = 3.1415926535898
+    a = (float(ctx.arg(z)) + ctx.pi) / (2*ctx.pi)
+    a = (a + 0.5) % 1.0
+    b = 1.0 - float(1/(1.0+abs(z)**0.3))
+    return hls_to_rgb(a, b, 0.8)
+
+blue_orange_colors = [
+  (-1.0,  (0.0, 0.0, 0.0)),
+  (-0.95, (0.1, 0.2, 0.5)),   # dark blue
+  (-0.5,  (0.0, 0.5, 1.0)),   # blueish
+  (-0.05, (0.4, 0.8, 0.8)),   # cyanish
+  ( 0.0,  (1.0, 1.0, 1.0)),
+  ( 0.05, (1.0, 0.9, 0.3)),   # yellowish
+  ( 0.5,  (0.9, 0.5, 0.0)),   # orangeish
+  ( 0.95, (0.7, 0.1, 0.0)),   # redish
+  ( 1.0,  (0.0, 0.0, 0.0)),
+  ( 2.0,  (0.0, 0.0, 0.0)),
+]
+
+def phase_color_function(ctx, z):
+    if ctx.isinf(z):
+        return (1.0, 1.0, 1.0)
+    if ctx.isnan(z):
+        return (0.5, 0.5, 0.5)
+    pi = 3.1415926535898
+    w = float(ctx.arg(z)) / pi
+    w = max(min(w, 1.0), -1.0)
+    for i in range(1,len(blue_orange_colors)):
+        if blue_orange_colors[i][0] > w:
+            a, (ra, ga, ba) = blue_orange_colors[i-1]
+            b, (rb, gb, bb) = blue_orange_colors[i]
+            s = (w-a) / (b-a)
+            return ra+(rb-ra)*s, ga+(gb-ga)*s, ba+(bb-ba)*s
+
+def cplot(ctx, f, re=[-5,5], im=[-5,5], points=2000, color=None,
+    verbose=False, file=None, dpi=None, axes=None):
+    """
+    Plots the given complex-valued function *f* over a rectangular part
+    of the complex plane specified by the pairs of intervals *re* and *im*.
+    For example::
+
+        cplot(lambda z: z, [-2, 2], [-10, 10])
+        cplot(exp)
+        cplot(zeta, [0, 1], [0, 50])
+
+    By default, the complex argument (phase) is shown as color (hue) and
+    the magnitude is show as brightness. You can also supply a
+    custom color function (*color*). This function should take a
+    complex number as input and return an RGB 3-tuple containing
+    floats in the range 0.0-1.0.
+
+    Alternatively, you can select a builtin color function by passing
+    a string as *color*:
+
+      * "default" - default color scheme
+      * "phase" - a color scheme that only renders the phase of the function,
+         with white for positive reals, black for negative reals, gold in the
+         upper half plane, and blue in the lower half plane.
+
+    To obtain a sharp image, the number of points may need to be
+    increased to 100,000 or thereabout. Since evaluating the
+    function that many times is likely to be slow, the 'verbose'
+    option is useful to display progress.
+
+    .. note :: This function requires matplotlib (pylab).
+    """
+    if color is None or color == "default":
+        color = ctx.default_color_function
+    if color == "phase":
+        color = ctx.phase_color_function
+    import pylab
+    if file:
+        axes = None
+    fig = None
+    if not axes:
+        fig = pylab.figure()
+        axes = fig.add_subplot(111)
+    rea, reb = re
+    ima, imb = im
+    dre = reb - rea
+    dim = imb - ima
+    M = int(ctx.sqrt(points*dre/dim)+1)
+    N = int(ctx.sqrt(points*dim/dre)+1)
+    x = pylab.linspace(rea, reb, M)
+    y = pylab.linspace(ima, imb, N)
+    # Note: we have to be careful to get the right rotation.
+    # Test with these plots:
+    #   cplot(lambda z: z if z.real < 0 else 0)
+    #   cplot(lambda z: z if z.imag < 0 else 0)
+    w = pylab.zeros((N, M, 3))
+    for n in xrange(N):
+        for m in xrange(M):
+            z = ctx.mpc(x[m], y[n])
+            try:
+                v = color(f(z))
+            except ctx.plot_ignore:
+                v = (0.5, 0.5, 0.5)
+            w[n,m] = v
+        if verbose:
+            print(str(n) + ' of ' + str(N))
+    rea, reb, ima, imb = [float(_) for _ in [rea, reb, ima, imb]]
+    axes.imshow(w, extent=(rea, reb, ima, imb), origin='lower')
+    axes.set_xlabel('Re(z)')
+    axes.set_ylabel('Im(z)')
+    if fig:
+        if file:
+            pylab.savefig(file, dpi=dpi)
+        else:
+            pylab.show()
+
+def splot(ctx, f, u=[-5,5], v=[-5,5], points=100, keep_aspect=True, \
+          wireframe=False, file=None, dpi=None, axes=None):
+    """
+    Plots the surface defined by `f`.
+
+    If `f` returns a single component, then this plots the surface
+    defined by `z = f(x,y)` over the rectangular domain with
+    `x = u` and `y = v`.
+
+    If `f` returns three components, then this plots the parametric
+    surface `x, y, z = f(u,v)` over the pairs of intervals `u` and `v`.
+
+    For example, to plot a simple function::
+
+        >>> from mpmath import *
+        >>> f = lambda x, y: sin(x+y)*cos(y)
+        >>> splot(f, [-pi,pi], [-pi,pi])    # doctest: +SKIP
+
+    Plotting a donut::
+
+        >>> r, R = 1, 2.5
+        >>> f = lambda u, v: [r*cos(u), (R+r*sin(u))*cos(v), (R+r*sin(u))*sin(v)]
+        >>> splot(f, [0, 2*pi], [0, 2*pi])    # doctest: +SKIP
+
+    .. note :: This function requires matplotlib (pylab) 0.98.5.3 or higher.
+    """
+    import pylab
+    import mpl_toolkits.mplot3d as mplot3d
+    if file:
+        axes = None
+    fig = None
+    if not axes:
+        fig = pylab.figure()
+        axes = mplot3d.axes3d.Axes3D(fig)
+    ua, ub = u
+    va, vb = v
+    du = ub - ua
+    dv = vb - va
+    if not isinstance(points, (list, tuple)):
+        points = [points, points]
+    M, N = points
+    u = pylab.linspace(ua, ub, M)
+    v = pylab.linspace(va, vb, N)
+    x, y, z = [pylab.zeros((M, N)) for i in xrange(3)]
+    xab, yab, zab = [[0, 0] for i in xrange(3)]
+    for n in xrange(N):
+        for m in xrange(M):
+            fdata = f(ctx.convert(u[m]), ctx.convert(v[n]))
+            try:
+                x[m,n], y[m,n], z[m,n] = fdata
+            except TypeError:
+                x[m,n], y[m,n], z[m,n] = u[m], v[n], fdata
+            for c, cab in [(x[m,n], xab), (y[m,n], yab), (z[m,n], zab)]:
+                if c < cab[0]:
+                    cab[0] = c
+                if c > cab[1]:
+                    cab[1] = c
+    if wireframe:
+        axes.plot_wireframe(x, y, z, rstride=4, cstride=4)
+    else:
+        axes.plot_surface(x, y, z, rstride=4, cstride=4)
+    axes.set_xlabel('x')
+    axes.set_ylabel('y')
+    axes.set_zlabel('z')
+    if keep_aspect:
+        dx, dy, dz = [cab[1] - cab[0] for cab in [xab, yab, zab]]
+        maxd = max(dx, dy, dz)
+        if dx < maxd:
+            delta = maxd - dx
+            axes.set_xlim3d(xab[0] - delta / 2.0, xab[1] + delta / 2.0)
+        if dy < maxd:
+            delta = maxd - dy
+            axes.set_ylim3d(yab[0] - delta / 2.0, yab[1] + delta / 2.0)
+        if dz < maxd:
+            delta = maxd - dz
+            axes.set_zlim3d(zab[0] - delta / 2.0, zab[1] + delta / 2.0)
+    if fig:
+        if file:
+            pylab.savefig(file, dpi=dpi)
+        else:
+            pylab.show()
+
+
+VisualizationMethods.plot = plot
+VisualizationMethods.default_color_function = default_color_function
+VisualizationMethods.phase_color_function = phase_color_function
+VisualizationMethods.cplot = cplot
+VisualizationMethods.splot = splot

tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/locations/__init__.py

@@ -0,0 +1,456 @@
+import functools
+import logging
+import os
+import pathlib
+import sys
+import sysconfig
+from typing import Any, Dict, Generator, Optional, Tuple
+
+from pip._internal.models.scheme import SCHEME_KEYS, Scheme
+from pip._internal.utils.compat import WINDOWS
+from pip._internal.utils.deprecation import deprecated
+from pip._internal.utils.virtualenv import running_under_virtualenv
+
+from . import _sysconfig
+from .base import (
+    USER_CACHE_DIR,
+    get_major_minor_version,
+    get_src_prefix,
+    is_osx_framework,
+    site_packages,
+    user_site,
+)
+
+__all__ = [
+    "USER_CACHE_DIR",
+    "get_bin_prefix",
+    "get_bin_user",
+    "get_major_minor_version",
+    "get_platlib",
+    "get_purelib",
+    "get_scheme",
+    "get_src_prefix",
+    "site_packages",
+    "user_site",
+]
+
+
+logger = logging.getLogger(__name__)
+
+
+_PLATLIBDIR: str = getattr(sys, "platlibdir", "lib")
+
+_USE_SYSCONFIG_DEFAULT = sys.version_info >= (3, 10)
+
+
+def _should_use_sysconfig() -> bool:
+    """This function determines the value of _USE_SYSCONFIG.
+
+    By default, pip uses sysconfig on Python 3.10+.
+    But Python distributors can override this decision by setting:
+        sysconfig._PIP_USE_SYSCONFIG = True / False
+    Rationale in https://github.com/pypa/pip/issues/10647
+
+    This is a function for testability, but should be constant during any one
+    run.
+    """
+    return bool(getattr(sysconfig, "_PIP_USE_SYSCONFIG", _USE_SYSCONFIG_DEFAULT))
+
+
+_USE_SYSCONFIG = _should_use_sysconfig()
+
+if not _USE_SYSCONFIG:
+    # Import distutils lazily to avoid deprecation warnings,
+    # but import it soon enough that it is in memory and available during
+    # a pip reinstall.
+    from . import _distutils
+
+# Be noisy about incompatibilities if this platforms "should" be using
+# sysconfig, but is explicitly opting out and using distutils instead.
+if _USE_SYSCONFIG_DEFAULT and not _USE_SYSCONFIG:
+    _MISMATCH_LEVEL = logging.WARNING
+else:
+    _MISMATCH_LEVEL = logging.DEBUG
+
+
+def _looks_like_bpo_44860() -> bool:
+    """The resolution to bpo-44860 will change this incorrect platlib.
+
+    See <https://bugs.python.org/issue44860>.
+    """
+    from distutils.command.install import INSTALL_SCHEMES
+
+    try:
+        unix_user_platlib = INSTALL_SCHEMES["unix_user"]["platlib"]
+    except KeyError:
+        return False
+    return unix_user_platlib == "$usersite"
+
+
+def _looks_like_red_hat_patched_platlib_purelib(scheme: Dict[str, str]) -> bool:
+    platlib = scheme["platlib"]
+    if "/$platlibdir/" in platlib:
+        platlib = platlib.replace("/$platlibdir/", f"/{_PLATLIBDIR}/")
+    if "/lib64/" not in platlib:
+        return False
+    unpatched = platlib.replace("/lib64/", "/lib/")
+    return unpatched.replace("$platbase/", "$base/") == scheme["purelib"]
+
+
+@functools.lru_cache(maxsize=None)
+def _looks_like_red_hat_lib() -> bool:
+    """Red Hat patches platlib in unix_prefix and unix_home, but not purelib.
+
+    This is the only way I can see to tell a Red Hat-patched Python.
+    """
+    from distutils.command.install import INSTALL_SCHEMES
+
+    return all(
+        k in INSTALL_SCHEMES
+        and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k])
+        for k in ("unix_prefix", "unix_home")
+    )
+
+
+@functools.lru_cache(maxsize=None)
+def _looks_like_debian_scheme() -> bool:
+    """Debian adds two additional schemes."""
+    from distutils.command.install import INSTALL_SCHEMES
+
+    return "deb_system" in INSTALL_SCHEMES and "unix_local" in INSTALL_SCHEMES
+
+
+@functools.lru_cache(maxsize=None)
+def _looks_like_red_hat_scheme() -> bool:
+    """Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.
+
+    Red Hat's ``00251-change-user-install-location.patch`` changes the install
+    command's ``prefix`` and ``exec_prefix`` to append ``"/local"``. This is
+    (fortunately?) done quite unconditionally, so we create a default command
+    object without any configuration to detect this.
+    """
+    from distutils.command.install import install
+    from distutils.dist import Distribution
+
+    cmd: Any = install(Distribution())
+    cmd.finalize_options()
+    return (
+        cmd.exec_prefix == f"{os.path.normpath(sys.exec_prefix)}/local"
+        and cmd.prefix == f"{os.path.normpath(sys.prefix)}/local"
+    )
+
+
+@functools.lru_cache(maxsize=None)
+def _looks_like_slackware_scheme() -> bool:
+    """Slackware patches sysconfig but fails to patch distutils and site.
+
+    Slackware changes sysconfig's user scheme to use ``"lib64"`` for the lib
+    path, but does not do the same to the site module.
+    """
+    if user_site is None:  # User-site not available.
+        return False
+    try:
+        paths = sysconfig.get_paths(scheme="posix_user", expand=False)
+    except KeyError:  # User-site not available.
+        return False
+    return "/lib64/" in paths["purelib"] and "/lib64/" not in user_site
+
+
+@functools.lru_cache(maxsize=None)
+def _looks_like_msys2_mingw_scheme() -> bool:
+    """MSYS2 patches distutils and sysconfig to use a UNIX-like scheme.
+
+    However, MSYS2 incorrectly patches sysconfig ``nt`` scheme. The fix is
+    likely going to be included in their 3.10 release, so we ignore the warning.
+    See msys2/MINGW-packages#9319.
+
+    MSYS2 MINGW's patch uses lowercase ``"lib"`` instead of the usual uppercase,
+    and is missing the final ``"site-packages"``.
+    """
+    paths = sysconfig.get_paths("nt", expand=False)
+    return all(
+        "Lib" not in p and "lib" in p and not p.endswith("site-packages")
+        for p in (paths[key] for key in ("platlib", "purelib"))
+    )
+
+
+def _fix_abiflags(parts: Tuple[str]) -> Generator[str, None, None]:
+    ldversion = sysconfig.get_config_var("LDVERSION")
+    abiflags = getattr(sys, "abiflags", None)
+
+    # LDVERSION does not end with sys.abiflags. Just return the path unchanged.
+    if not ldversion or not abiflags or not ldversion.endswith(abiflags):
+        yield from parts
+        return
+
+    # Strip sys.abiflags from LDVERSION-based path components.
+    for part in parts:
+        if part.endswith(ldversion):
+            part = part[: (0 - len(abiflags))]
+        yield part
+
+
+@functools.lru_cache(maxsize=None)
+def _warn_mismatched(old: pathlib.Path, new: pathlib.Path, *, key: str) -> None:
+    issue_url = "https://github.com/pypa/pip/issues/10151"
+    message = (
+        "Value for %s does not match. Please report this to <%s>"
+        "\ndistutils: %s"
+        "\nsysconfig: %s"
+    )
+    logger.log(_MISMATCH_LEVEL, message, key, issue_url, old, new)
+
+
+def _warn_if_mismatch(old: pathlib.Path, new: pathlib.Path, *, key: str) -> bool:
+    if old == new:
+        return False
+    _warn_mismatched(old, new, key=key)
+    return True
+
+
+@functools.lru_cache(maxsize=None)
+def _log_context(
+    *,
+    user: bool = False,
+    home: Optional[str] = None,
+    root: Optional[str] = None,
+    prefix: Optional[str] = None,
+) -> None:
+    parts = [
+        "Additional context:",
+        "user = %r",
+        "home = %r",
+        "root = %r",
+        "prefix = %r",
+    ]
+
+    logger.log(_MISMATCH_LEVEL, "\n".join(parts), user, home, root, prefix)
+
+
+def get_scheme(
+    dist_name: str,
+    user: bool = False,
+    home: Optional[str] = None,
+    root: Optional[str] = None,
+    isolated: bool = False,
+    prefix: Optional[str] = None,
+) -> Scheme:
+    new = _sysconfig.get_scheme(
+        dist_name,
+        user=user,
+        home=home,
+        root=root,
+        isolated=isolated,
+        prefix=prefix,
+    )
+    if _USE_SYSCONFIG:
+        return new
+
+    old = _distutils.get_scheme(
+        dist_name,
+        user=user,
+        home=home,
+        root=root,
+        isolated=isolated,
+        prefix=prefix,
+    )
+
+    warning_contexts = []
+    for k in SCHEME_KEYS:
+        old_v = pathlib.Path(getattr(old, k))
+        new_v = pathlib.Path(getattr(new, k))
+
+        if old_v == new_v:
+            continue
+
+        # distutils incorrectly put PyPy packages under ``site-packages/python``
+        # in the ``posix_home`` scheme, but PyPy devs said they expect the
+        # directory name to be ``pypy`` instead. So we treat this as a bug fix
+        # and not warn about it. See bpo-43307 and python/cpython#24628.
+        skip_pypy_special_case = (
+            sys.implementation.name == "pypy"
+            and home is not None
+            and k in ("platlib", "purelib")
+            and old_v.parent == new_v.parent
+            and old_v.name.startswith("python")
+            and new_v.name.startswith("pypy")
+        )
+        if skip_pypy_special_case:
+            continue
+
+        # sysconfig's ``osx_framework_user`` does not include ``pythonX.Y`` in
+        # the ``include`` value, but distutils's ``headers`` does. We'll let
+        # CPython decide whether this is a bug or feature. See bpo-43948.
+        skip_osx_framework_user_special_case = (
+            user
+            and is_osx_framework()
+            and k == "headers"
+            and old_v.parent.parent == new_v.parent
+            and old_v.parent.name.startswith("python")
+        )
+        if skip_osx_framework_user_special_case:
+            continue
+
+        # On Red Hat and derived Linux distributions, distutils is patched to
+        # use "lib64" instead of "lib" for platlib.
+        if k == "platlib" and _looks_like_red_hat_lib():
+            continue
+
+        # On Python 3.9+, sysconfig's posix_user scheme sets platlib against
+        # sys.platlibdir, but distutils's unix_user incorrectly coninutes
+        # using the same $usersite for both platlib and purelib. This creates a
+        # mismatch when sys.platlibdir is not "lib".
+        skip_bpo_44860 = (
+            user
+            and k == "platlib"
+            and not WINDOWS
+            and sys.version_info >= (3, 9)
+            and _PLATLIBDIR != "lib"
+            and _looks_like_bpo_44860()
+        )
+        if skip_bpo_44860:
+            continue
+
+        # Slackware incorrectly patches posix_user to use lib64 instead of lib,
+        # but not usersite to match the location.
+        skip_slackware_user_scheme = (
+            user
+            and k in ("platlib", "purelib")
+            and not WINDOWS
+            and _looks_like_slackware_scheme()
+        )
+        if skip_slackware_user_scheme:
+            continue
+
+        # Both Debian and Red Hat patch Python to place the system site under
+        # /usr/local instead of /usr. Debian also places lib in dist-packages
+        # instead of site-packages, but the /usr/local check should cover it.
+        skip_linux_system_special_case = (
+            not (user or home or prefix or running_under_virtualenv())
+            and old_v.parts[1:3] == ("usr", "local")
+            and len(new_v.parts) > 1
+            and new_v.parts[1] == "usr"
+            and (len(new_v.parts) < 3 or new_v.parts[2] != "local")
+            and (_looks_like_red_hat_scheme() or _looks_like_debian_scheme())
+        )
+        if skip_linux_system_special_case:
+            continue
+
+        # MSYS2 MINGW's sysconfig patch does not include the "site-packages"
+        # part of the path. This is incorrect and will be fixed in MSYS.
+        skip_msys2_mingw_bug = (
+            WINDOWS and k in ("platlib", "purelib") and _looks_like_msys2_mingw_scheme()
+        )
+        if skip_msys2_mingw_bug:
+            continue
+
+        # CPython's POSIX install script invokes pip (via ensurepip) against the
+        # interpreter located in the source tree, not the install site. This
+        # triggers special logic in sysconfig that's not present in distutils.
+        # https://github.com/python/cpython/blob/8c21941ddaf/Lib/sysconfig.py#L178-L194
+        skip_cpython_build = (
+            sysconfig.is_python_build(check_home=True)
+            and not WINDOWS
+            and k in ("headers", "include", "platinclude")
+        )
+        if skip_cpython_build:
+            continue
+
+        warning_contexts.append((old_v, new_v, f"scheme.{k}"))
+
+    if not warning_contexts:
+        return old
+
+    # Check if this path mismatch is caused by distutils config files. Those
+    # files will no longer work once we switch to sysconfig, so this raises a
+    # deprecation message for them.
+    default_old = _distutils.distutils_scheme(
+        dist_name,
+        user,
+        home,
+        root,
+        isolated,
+        prefix,
+        ignore_config_files=True,
+    )
+    if any(default_old[k] != getattr(old, k) for k in SCHEME_KEYS):
+        deprecated(
+            reason=(
+                "Configuring installation scheme with distutils config files "
+                "is deprecated and will no longer work in the near future. If you "
+                "are using a Homebrew or Linuxbrew Python, please see discussion "
+                "at https://github.com/Homebrew/homebrew-core/issues/76621"
+            ),
+            replacement=None,
+            gone_in=None,
+        )
+        return old
+
+    # Post warnings about this mismatch so user can report them back.
+    for old_v, new_v, key in warning_contexts:
+        _warn_mismatched(old_v, new_v, key=key)
+    _log_context(user=user, home=home, root=root, prefix=prefix)
+
+    return old
+
+
+def get_bin_prefix() -> str:
+    new = _sysconfig.get_bin_prefix()
+    if _USE_SYSCONFIG:
+        return new
+
+    old = _distutils.get_bin_prefix()
+    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="bin_prefix"):
+        _log_context()
+    return old
+
+
+def get_bin_user() -> str:
+    return _sysconfig.get_scheme("", user=True).scripts
+
+
+def _looks_like_deb_system_dist_packages(value: str) -> bool:
+    """Check if the value is Debian's APT-controlled dist-packages.
+
+    Debian's ``distutils.sysconfig.get_python_lib()`` implementation returns the
+    default package path controlled by APT, but does not patch ``sysconfig`` to
+    do the same. This is similar to the bug worked around in ``get_scheme()``,
+    but here the default is ``deb_system`` instead of ``unix_local``. Ultimately
+    we can't do anything about this Debian bug, and this detection allows us to
+    skip the warning when needed.
+    """
+    if not _looks_like_debian_scheme():
+        return False
+    if value == "/usr/lib/python3/dist-packages":
+        return True
+    return False
+
+
+def get_purelib() -> str:
+    """Return the default pure-Python lib location."""
+    new = _sysconfig.get_purelib()
+    if _USE_SYSCONFIG:
+        return new
+
+    old = _distutils.get_purelib()
+    if _looks_like_deb_system_dist_packages(old):
+        return old
+    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="purelib"):
+        _log_context()
+    return old
+
+
+def get_platlib() -> str:
+    """Return the default platform-shared lib location."""
+    new = _sysconfig.get_platlib()
+    if _USE_SYSCONFIG:
+        return new
+
+    from . import _distutils
+
+    old = _distutils.get_platlib()
+    if _looks_like_deb_system_dist_packages(old):
+        return old
+    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="platlib"):
+        _log_context()
+    return old

tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/locations/_sysconfig.py

@@ -0,0 +1,214 @@
+import logging
+import os
+import sys
+import sysconfig
+import typing
+
+from pip._internal.exceptions import InvalidSchemeCombination, UserInstallationInvalid
+from pip._internal.models.scheme import SCHEME_KEYS, Scheme
+from pip._internal.utils.virtualenv import running_under_virtualenv
+
+from .base import change_root, get_major_minor_version, is_osx_framework
+
+logger = logging.getLogger(__name__)
+
+
+# Notes on _infer_* functions.
+# Unfortunately ``get_default_scheme()`` didn't exist before 3.10, so there's no
+# way to ask things like "what is the '_prefix' scheme on this platform". These
+# functions try to answer that with some heuristics while accounting for ad-hoc
+# platforms not covered by CPython's default sysconfig implementation. If the
+# ad-hoc implementation does not fully implement sysconfig, we'll fall back to
+# a POSIX scheme.
+
+_AVAILABLE_SCHEMES = set(sysconfig.get_scheme_names())
+
+_PREFERRED_SCHEME_API = getattr(sysconfig, "get_preferred_scheme", None)
+
+
+def _should_use_osx_framework_prefix() -> bool:
+    """Check for Apple's ``osx_framework_library`` scheme.
+
+    Python distributed by Apple's Command Line Tools has this special scheme
+    that's used when:
+
+    * This is a framework build.
+    * We are installing into the system prefix.
+
+    This does not account for ``pip install --prefix`` (also means we're not
+    installing to the system prefix), which should use ``posix_prefix``, but
+    logic here means ``_infer_prefix()`` outputs ``osx_framework_library``. But
+    since ``prefix`` is not available for ``sysconfig.get_default_scheme()``,
+    which is the stdlib replacement for ``_infer_prefix()``, presumably Apple
+    wouldn't be able to magically switch between ``osx_framework_library`` and
+    ``posix_prefix``. ``_infer_prefix()`` returning ``osx_framework_library``
+    means its behavior is consistent whether we use the stdlib implementation
+    or our own, and we deal with this special case in ``get_scheme()`` instead.
+    """
+    return (
+        "osx_framework_library" in _AVAILABLE_SCHEMES
+        and not running_under_virtualenv()
+        and is_osx_framework()
+    )
+
+
+def _infer_prefix() -> str:
+    """Try to find a prefix scheme for the current platform.
+
+    This tries:
+
+    * A special ``osx_framework_library`` for Python distributed by Apple's
+      Command Line Tools, when not running in a virtual environment.
+    * Implementation + OS, used by PyPy on Windows (``pypy_nt``).
+    * Implementation without OS, used by PyPy on POSIX (``pypy``).
+    * OS + "prefix", used by CPython on POSIX (``posix_prefix``).
+    * Just the OS name, used by CPython on Windows (``nt``).
+
+    If none of the above works, fall back to ``posix_prefix``.
+    """
+    if _PREFERRED_SCHEME_API:
+        return _PREFERRED_SCHEME_API("prefix")
+    if _should_use_osx_framework_prefix():
+        return "osx_framework_library"
+    implementation_suffixed = f"{sys.implementation.name}_{os.name}"
+    if implementation_suffixed in _AVAILABLE_SCHEMES:
+        return implementation_suffixed
+    if sys.implementation.name in _AVAILABLE_SCHEMES:
+        return sys.implementation.name
+    suffixed = f"{os.name}_prefix"
+    if suffixed in _AVAILABLE_SCHEMES:
+        return suffixed
+    if os.name in _AVAILABLE_SCHEMES:  # On Windows, prefx is just called "nt".
+        return os.name
+    return "posix_prefix"
+
+
+def _infer_user() -> str:
+    """Try to find a user scheme for the current platform."""
+    if _PREFERRED_SCHEME_API:
+        return _PREFERRED_SCHEME_API("user")
+    if is_osx_framework() and not running_under_virtualenv():
+        suffixed = "osx_framework_user"
+    else:
+        suffixed = f"{os.name}_user"
+    if suffixed in _AVAILABLE_SCHEMES:
+        return suffixed
+    if "posix_user" not in _AVAILABLE_SCHEMES:  # User scheme unavailable.
+        raise UserInstallationInvalid()
+    return "posix_user"
+
+
+def _infer_home() -> str:
+    """Try to find a home for the current platform."""
+    if _PREFERRED_SCHEME_API:
+        return _PREFERRED_SCHEME_API("home")
+    suffixed = f"{os.name}_home"
+    if suffixed in _AVAILABLE_SCHEMES:
+        return suffixed
+    return "posix_home"
+
+
+# Update these keys if the user sets a custom home.
+_HOME_KEYS = [
+    "installed_base",
+    "base",
+    "installed_platbase",
+    "platbase",
+    "prefix",
+    "exec_prefix",
+]
+if sysconfig.get_config_var("userbase") is not None:
+    _HOME_KEYS.append("userbase")
+
+
+def get_scheme(
+    dist_name: str,
+    user: bool = False,
+    home: typing.Optional[str] = None,
+    root: typing.Optional[str] = None,
+    isolated: bool = False,
+    prefix: typing.Optional[str] = None,
+) -> Scheme:
+    """
+    Get the "scheme" corresponding to the input parameters.
+
+    :param dist_name: the name of the package to retrieve the scheme for, used
+        in the headers scheme path
+    :param user: indicates to use the "user" scheme
+    :param home: indicates to use the "home" scheme
+    :param root: root under which other directories are re-based
+    :param isolated: ignored, but kept for distutils compatibility (where
+        this controls whether the user-site pydistutils.cfg is honored)
+    :param prefix: indicates to use the "prefix" scheme and provides the
+        base directory for the same
+    """
+    if user and prefix:
+        raise InvalidSchemeCombination("--user", "--prefix")
+    if home and prefix:
+        raise InvalidSchemeCombination("--home", "--prefix")
+
+    if home is not None:
+        scheme_name = _infer_home()
+    elif user:
+        scheme_name = _infer_user()
+    else:
+        scheme_name = _infer_prefix()
+
+    # Special case: When installing into a custom prefix, use posix_prefix
+    # instead of osx_framework_library. See _should_use_osx_framework_prefix()
+    # docstring for details.
+    if prefix is not None and scheme_name == "osx_framework_library":
+        scheme_name = "posix_prefix"
+
+    if home is not None:
+        variables = {k: home for k in _HOME_KEYS}
+    elif prefix is not None:
+        variables = {k: prefix for k in _HOME_KEYS}
+    else:
+        variables = {}
+
+    paths = sysconfig.get_paths(scheme=scheme_name, vars=variables)
+
+    # Logic here is very arbitrary, we're doing it for compatibility, don't ask.
+    # 1. Pip historically uses a special header path in virtual environments.
+    # 2. If the distribution name is not known, distutils uses 'UNKNOWN'. We
+    #    only do the same when not running in a virtual environment because
+    #    pip's historical header path logic (see point 1) did not do this.
+    if running_under_virtualenv():
+        if user:
+            base = variables.get("userbase", sys.prefix)
+        else:
+            base = variables.get("base", sys.prefix)
+        python_xy = f"python{get_major_minor_version()}"
+        paths["include"] = os.path.join(base, "include", "site", python_xy)
+    elif not dist_name:
+        dist_name = "UNKNOWN"
+
+    scheme = Scheme(
+        platlib=paths["platlib"],
+        purelib=paths["purelib"],
+        headers=os.path.join(paths["include"], dist_name),
+        scripts=paths["scripts"],
+        data=paths["data"],
+    )
+    if root is not None:
+        converted_keys = {}
+        for key in SCHEME_KEYS:
+            converted_keys[key] = change_root(root, getattr(scheme, key))
+        scheme = Scheme(**converted_keys)
+    return scheme
+
+
+def get_bin_prefix() -> str:
+    # Forcing to use /usr/local/bin for standard macOS framework installs.
+    if sys.platform[:6] == "darwin" and sys.prefix[:16] == "/System/Library/":
+        return "/usr/local/bin"
+    return sysconfig.get_paths()["scripts"]
+
+
+def get_purelib() -> str:
+    return sysconfig.get_paths()["purelib"]
+
+
+def get_platlib() -> str:
+    return sysconfig.get_paths()["platlib"]

tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/network/__init__.py

@@ -0,0 +1,2 @@
+"""Contains purely network-related utilities.
+"""

tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/network/session.py

@@ -0,0 +1,522 @@
+"""PipSession and supporting code, containing all pip-specific
+network request configuration and behavior.
+"""
+
+import email.utils
+import functools
+import io
+import ipaddress
+import json
+import logging
+import mimetypes
+import os
+import platform
+import shutil
+import subprocess
+import sys
+import urllib.parse
+import warnings
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generator,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+)
+
+from pip._vendor import requests, urllib3
+from pip._vendor.cachecontrol import CacheControlAdapter as _BaseCacheControlAdapter
+from pip._vendor.requests.adapters import DEFAULT_POOLBLOCK, BaseAdapter
+from pip._vendor.requests.adapters import HTTPAdapter as _BaseHTTPAdapter
+from pip._vendor.requests.models import PreparedRequest, Response
+from pip._vendor.requests.structures import CaseInsensitiveDict
+from pip._vendor.urllib3.connectionpool import ConnectionPool
+from pip._vendor.urllib3.exceptions import InsecureRequestWarning
+
+from pip import __version__
+from pip._internal.metadata import get_default_environment
+from pip._internal.models.link import Link
+from pip._internal.network.auth import MultiDomainBasicAuth
+from pip._internal.network.cache import SafeFileCache
+
+# Import ssl from compat so the initial import occurs in only one place.
+from pip._internal.utils.compat import has_tls
+from pip._internal.utils.glibc import libc_ver
+from pip._internal.utils.misc import build_url_from_netloc, parse_netloc
+from pip._internal.utils.urls import url_to_path
+
+if TYPE_CHECKING:
+    from ssl import SSLContext
+
+    from pip._vendor.urllib3.poolmanager import PoolManager
+
+
+logger = logging.getLogger(__name__)
+
+SecureOrigin = Tuple[str, str, Optional[Union[int, str]]]
+
+
+# Ignore warning raised when using --trusted-host.
+warnings.filterwarnings("ignore", category=InsecureRequestWarning)
+
+
+SECURE_ORIGINS: List[SecureOrigin] = [
+    # protocol, hostname, port
+    # Taken from Chrome's list of secure origins (See: http://bit.ly/1qrySKC)
+    ("https", "*", "*"),
+    ("*", "localhost", "*"),
+    ("*", "127.0.0.0/8", "*"),
+    ("*", "::1/128", "*"),
+    ("file", "*", None),
+    # ssh is always secure.
+    ("ssh", "*", "*"),
+]
+
+
+# These are environment variables present when running under various
+# CI systems.  For each variable, some CI systems that use the variable
+# are indicated.  The collection was chosen so that for each of a number
+# of popular systems, at least one of the environment variables is used.
+# This list is used to provide some indication of and lower bound for
+# CI traffic to PyPI.  Thus, it is okay if the list is not comprehensive.
+# For more background, see: https://github.com/pypa/pip/issues/5499
+CI_ENVIRONMENT_VARIABLES = (
+    # Azure Pipelines
+    "BUILD_BUILDID",
+    # Jenkins
+    "BUILD_ID",
+    # AppVeyor, CircleCI, Codeship, Gitlab CI, Shippable, Travis CI
+    "CI",
+    # Explicit environment variable.
+    "PIP_IS_CI",
+)
+
+
+def looks_like_ci() -> bool:
+    """
+    Return whether it looks like pip is running under CI.
+    """
+    # We don't use the method of checking for a tty (e.g. using isatty())
+    # because some CI systems mimic a tty (e.g. Travis CI).  Thus that
+    # method doesn't provide definitive information in either direction.
+    return any(name in os.environ for name in CI_ENVIRONMENT_VARIABLES)
+
+
+@functools.lru_cache(maxsize=1)
+def user_agent() -> str:
+    """
+    Return a string representing the user agent.
+    """
+    data: Dict[str, Any] = {
+        "installer": {"name": "pip", "version": __version__},
+        "python": platform.python_version(),
+        "implementation": {
+            "name": platform.python_implementation(),
+        },
+    }
+
+    if data["implementation"]["name"] == "CPython":
+        data["implementation"]["version"] = platform.python_version()
+    elif data["implementation"]["name"] == "PyPy":
+        pypy_version_info = sys.pypy_version_info  # type: ignore
+        if pypy_version_info.releaselevel == "final":
+            pypy_version_info = pypy_version_info[:3]
+        data["implementation"]["version"] = ".".join(
+            [str(x) for x in pypy_version_info]
+        )
+    elif data["implementation"]["name"] == "Jython":
+        # Complete Guess
+        data["implementation"]["version"] = platform.python_version()
+    elif data["implementation"]["name"] == "IronPython":
+        # Complete Guess
+        data["implementation"]["version"] = platform.python_version()
+
+    if sys.platform.startswith("linux"):
+        from pip._vendor import distro
+
+        linux_distribution = distro.name(), distro.version(), distro.codename()
+        distro_infos: Dict[str, Any] = dict(
+            filter(
+                lambda x: x[1],
+                zip(["name", "version", "id"], linux_distribution),
+            )
+        )
+        libc = dict(
+            filter(
+                lambda x: x[1],
+                zip(["lib", "version"], libc_ver()),
+            )
+        )
+        if libc:
+            distro_infos["libc"] = libc
+        if distro_infos:
+            data["distro"] = distro_infos
+
+    if sys.platform.startswith("darwin") and platform.mac_ver()[0]:
+        data["distro"] = {"name": "macOS", "version": platform.mac_ver()[0]}
+
+    if platform.system():
+        data.setdefault("system", {})["name"] = platform.system()
+
+    if platform.release():
+        data.setdefault("system", {})["release"] = platform.release()
+
+    if platform.machine():
+        data["cpu"] = platform.machine()
+
+    if has_tls():
+        import _ssl as ssl
+
+        data["openssl_version"] = ssl.OPENSSL_VERSION
+
+    setuptools_dist = get_default_environment().get_distribution("setuptools")
+    if setuptools_dist is not None:
+        data["setuptools_version"] = str(setuptools_dist.version)
+
+    if shutil.which("rustc") is not None:
+        # If for any reason `rustc --version` fails, silently ignore it
+        try:
+            rustc_output = subprocess.check_output(
+                ["rustc", "--version"], stderr=subprocess.STDOUT, timeout=0.5
+            )
+        except Exception:
+            pass
+        else:
+            if rustc_output.startswith(b"rustc "):
+                # The format of `rustc --version` is:
+                # `b'rustc 1.52.1 (9bc8c42bb 2021-05-09)\n'`
+                # We extract just the middle (1.52.1) part
+                data["rustc_version"] = rustc_output.split(b" ")[1].decode()
+
+    # Use None rather than False so as not to give the impression that
+    # pip knows it is not being run under CI.  Rather, it is a null or
+    # inconclusive result.  Also, we include some value rather than no
+    # value to make it easier to know that the check has been run.
+    data["ci"] = True if looks_like_ci() else None
+
+    user_data = os.environ.get("PIP_USER_AGENT_USER_DATA")
+    if user_data is not None:
+        data["user_data"] = user_data
+
+    return "{data[installer][name]}/{data[installer][version]} {json}".format(
+        data=data,
+        json=json.dumps(data, separators=(",", ":"), sort_keys=True),
+    )
+
+
+class LocalFSAdapter(BaseAdapter):
+    def send(
+        self,
+        request: PreparedRequest,
+        stream: bool = False,
+        timeout: Optional[Union[float, Tuple[float, float]]] = None,
+        verify: Union[bool, str] = True,
+        cert: Optional[Union[str, Tuple[str, str]]] = None,
+        proxies: Optional[Mapping[str, str]] = None,
+    ) -> Response:
+        pathname = url_to_path(request.url)
+
+        resp = Response()
+        resp.status_code = 200
+        resp.url = request.url
+
+        try:
+            stats = os.stat(pathname)
+        except OSError as exc:
+            # format the exception raised as a io.BytesIO object,
+            # to return a better error message:
+            resp.status_code = 404
+            resp.reason = type(exc).__name__
+            resp.raw = io.BytesIO(f"{resp.reason}: {exc}".encode())
+        else:
+            modified = email.utils.formatdate(stats.st_mtime, usegmt=True)
+            content_type = mimetypes.guess_type(pathname)[0] or "text/plain"
+            resp.headers = CaseInsensitiveDict(
+                {
+                    "Content-Type": content_type,
+                    "Content-Length": stats.st_size,
+                    "Last-Modified": modified,
+                }
+            )
+
+            resp.raw = open(pathname, "rb")
+            resp.close = resp.raw.close
+
+        return resp
+
+    def close(self) -> None:
+        pass
+
+
+class _SSLContextAdapterMixin:
+    """Mixin to add the ``ssl_context`` constructor argument to HTTP adapters.
+
+    The additional argument is forwarded directly to the pool manager. This allows us
+    to dynamically decide what SSL store to use at runtime, which is used to implement
+    the optional ``truststore`` backend.
+    """
+
+    def __init__(
+        self,
+        *,
+        ssl_context: Optional["SSLContext"] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._ssl_context = ssl_context
+        super().__init__(**kwargs)
+
+    def init_poolmanager(
+        self,
+        connections: int,
+        maxsize: int,
+        block: bool = DEFAULT_POOLBLOCK,
+        **pool_kwargs: Any,
+    ) -> "PoolManager":
+        if self._ssl_context is not None:
+            pool_kwargs.setdefault("ssl_context", self._ssl_context)
+        return super().init_poolmanager(  # type: ignore[misc]
+            connections=connections,
+            maxsize=maxsize,
+            block=block,
+            **pool_kwargs,
+        )
+
+
+class HTTPAdapter(_SSLContextAdapterMixin, _BaseHTTPAdapter):
+    pass
+
+
+class CacheControlAdapter(_SSLContextAdapterMixin, _BaseCacheControlAdapter):
+    pass
+
+
+class InsecureHTTPAdapter(HTTPAdapter):
+    def cert_verify(
+        self,
+        conn: ConnectionPool,
+        url: str,
+        verify: Union[bool, str],
+        cert: Optional[Union[str, Tuple[str, str]]],
+    ) -> None:
+        super().cert_verify(conn=conn, url=url, verify=False, cert=cert)
+
+
+class InsecureCacheControlAdapter(CacheControlAdapter):
+    def cert_verify(
+        self,
+        conn: ConnectionPool,
+        url: str,
+        verify: Union[bool, str],
+        cert: Optional[Union[str, Tuple[str, str]]],
+    ) -> None:
+        super().cert_verify(conn=conn, url=url, verify=False, cert=cert)
+
+
+class PipSession(requests.Session):
+    timeout: Optional[int] = None
+
+    def __init__(
+        self,
+        *args: Any,
+        retries: int = 0,
+        cache: Optional[str] = None,
+        trusted_hosts: Sequence[str] = (),
+        index_urls: Optional[List[str]] = None,
+        ssl_context: Optional["SSLContext"] = None,
+        **kwargs: Any,
+    ) -> None:
+        """
+        :param trusted_hosts: Domains not to emit warnings for when not using
+            HTTPS.
+        """
+        super().__init__(*args, **kwargs)
+
+        # Namespace the attribute with "pip_" just in case to prevent
+        # possible conflicts with the base class.
+        self.pip_trusted_origins: List[Tuple[str, Optional[int]]] = []
+
+        # Attach our User Agent to the request
+        self.headers["User-Agent"] = user_agent()
+
+        # Attach our Authentication handler to the session
+        self.auth = MultiDomainBasicAuth(index_urls=index_urls)
+
+        # Create our urllib3.Retry instance which will allow us to customize
+        # how we handle retries.
+        retries = urllib3.Retry(
+            # Set the total number of retries that a particular request can
+            # have.
+            total=retries,
+            # A 503 error from PyPI typically means that the Fastly -> Origin
+            # connection got interrupted in some way. A 503 error in general
+            # is typically considered a transient error so we'll go ahead and
+            # retry it.
+            # A 500 may indicate transient error in Amazon S3
+            # A 502 may be a transient error from a CDN like CloudFlare or CloudFront
+            # A 520 or 527 - may indicate transient error in CloudFlare
+            status_forcelist=[500, 502, 503, 520, 527],
+            # Add a small amount of back off between failed requests in
+            # order to prevent hammering the service.
+            backoff_factor=0.25,
+        )  # type: ignore
+
+        # Our Insecure HTTPAdapter disables HTTPS validation. It does not
+        # support caching so we'll use it for all http:// URLs.
+        # If caching is disabled, we will also use it for
+        # https:// hosts that we've marked as ignoring
+        # TLS errors for (trusted-hosts).
+        insecure_adapter = InsecureHTTPAdapter(max_retries=retries)
+
+        # We want to _only_ cache responses on securely fetched origins or when
+        # the host is specified as trusted. We do this because
+        # we can't validate the response of an insecurely/untrusted fetched
+        # origin, and we don't want someone to be able to poison the cache and
+        # require manual eviction from the cache to fix it.
+        if cache:
+            secure_adapter = CacheControlAdapter(
+                cache=SafeFileCache(cache),
+                max_retries=retries,
+                ssl_context=ssl_context,
+            )
+            self._trusted_host_adapter = InsecureCacheControlAdapter(
+                cache=SafeFileCache(cache),
+                max_retries=retries,
+            )
+        else:
+            secure_adapter = HTTPAdapter(max_retries=retries, ssl_context=ssl_context)
+            self._trusted_host_adapter = insecure_adapter
+
+        self.mount("https://", secure_adapter)
+        self.mount("http://", insecure_adapter)
+
+        # Enable file:// urls
+        self.mount("file://", LocalFSAdapter())
+
+        for host in trusted_hosts:
+            self.add_trusted_host(host, suppress_logging=True)
+
+    def update_index_urls(self, new_index_urls: List[str]) -> None:
+        """
+        :param new_index_urls: New index urls to update the authentication
+            handler with.
+        """
+        self.auth.index_urls = new_index_urls
+
+    def add_trusted_host(
+        self, host: str, source: Optional[str] = None, suppress_logging: bool = False
+    ) -> None:
+        """
+        :param host: It is okay to provide a host that has previously been
+            added.
+        :param source: An optional source string, for logging where the host
+            string came from.
+        """
+        if not suppress_logging:
+            msg = f"adding trusted host: {host!r}"
+            if source is not None:
+                msg += f" (from {source})"
+            logger.info(msg)
+
+        parsed_host, parsed_port = parse_netloc(host)
+        if parsed_host is None:
+            raise ValueError(f"Trusted host URL must include a host part: {host!r}")
+        if (parsed_host, parsed_port) not in self.pip_trusted_origins:
+            self.pip_trusted_origins.append((parsed_host, parsed_port))
+
+        self.mount(
+            build_url_from_netloc(host, scheme="http") + "/", self._trusted_host_adapter
+        )
+        self.mount(build_url_from_netloc(host) + "/", self._trusted_host_adapter)
+        if not parsed_port:
+            self.mount(
+                build_url_from_netloc(host, scheme="http") + ":",
+                self._trusted_host_adapter,
+            )
+            # Mount wildcard ports for the same host.
+            self.mount(build_url_from_netloc(host) + ":", self._trusted_host_adapter)
+
+    def iter_secure_origins(self) -> Generator[SecureOrigin, None, None]:
+        yield from SECURE_ORIGINS
+        for host, port in self.pip_trusted_origins:
+            yield ("*", host, "*" if port is None else port)
+
+    def is_secure_origin(self, location: Link) -> bool:
+        # Determine if this url used a secure transport mechanism
+        parsed = urllib.parse.urlparse(str(location))
+        origin_protocol, origin_host, origin_port = (
+            parsed.scheme,
+            parsed.hostname,
+            parsed.port,
+        )
+
+        # The protocol to use to see if the protocol matches.
+        # Don't count the repository type as part of the protocol: in
+        # cases such as "git+ssh", only use "ssh". (I.e., Only verify against
+        # the last scheme.)
+        origin_protocol = origin_protocol.rsplit("+", 1)[-1]
+
+        # Determine if our origin is a secure origin by looking through our
+        # hardcoded list of secure origins, as well as any additional ones
+        # configured on this PackageFinder instance.
+        for secure_origin in self.iter_secure_origins():
+            secure_protocol, secure_host, secure_port = secure_origin
+            if origin_protocol != secure_protocol and secure_protocol != "*":
+                continue
+
+            try:
+                addr = ipaddress.ip_address(origin_host or "")
+                network = ipaddress.ip_network(secure_host)
+            except ValueError:
+                # We don't have both a valid address or a valid network, so
+                # we'll check this origin against hostnames.
+                if (
+                    origin_host
+                    and origin_host.lower() != secure_host.lower()
+                    and secure_host != "*"
+                ):
+                    continue
+            else:
+                # We have a valid address and network, so see if the address
+                # is contained within the network.
+                if addr not in network:
+                    continue
+
+            # Check to see if the port matches.
+            if (
+                origin_port != secure_port
+                and secure_port != "*"
+                and secure_port is not None
+            ):
+                continue
+
+            # If we've gotten here, then this origin matches the current
+            # secure origin and we should return True
+            return True
+
+        # If we've gotten to this point, then the origin isn't secure and we
+        # will not accept it as a valid location to search. We will however
+        # log a warning that we are ignoring it.
+        logger.warning(
+            "The repository located at %s is not a trusted or secure host and "
+            "is being ignored. If this repository is available via HTTPS we "
+            "recommend you use HTTPS instead, otherwise you may silence "
+            "this warning and allow it anyway with '--trusted-host %s'.",
+            origin_host,
+            origin_host,
+        )
+
+        return False
+
+    def request(self, method: str, url: str, *args: Any, **kwargs: Any) -> Response:
+        # Allow setting a default timeout on a session
+        kwargs.setdefault("timeout", self.timeout)
+        # Allow setting a default proxies on a session
+        kwargs.setdefault("proxies", self.proxies)
+
+        # Dispatch the actual request
+        return super().request(method, url, *args, **kwargs)

tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/base.py

@@ -0,0 +1,139 @@
+from dataclasses import dataclass
+from typing import FrozenSet, Iterable, Optional, Tuple
+
+from pip._vendor.packaging.specifiers import SpecifierSet
+from pip._vendor.packaging.utils import NormalizedName
+from pip._vendor.packaging.version import Version
+
+from pip._internal.models.link import Link, links_equivalent
+from pip._internal.req.req_install import InstallRequirement
+from pip._internal.utils.hashes import Hashes
+
+CandidateLookup = Tuple[Optional["Candidate"], Optional[InstallRequirement]]
+
+
+def format_name(project: NormalizedName, extras: FrozenSet[NormalizedName]) -> str:
+    if not extras:
+        return project
+    extras_expr = ",".join(sorted(extras))
+    return f"{project}[{extras_expr}]"
+
+
+@dataclass(frozen=True)
+class Constraint:
+    specifier: SpecifierSet
+    hashes: Hashes
+    links: FrozenSet[Link]
+
+    @classmethod
+    def empty(cls) -> "Constraint":
+        return Constraint(SpecifierSet(), Hashes(), frozenset())
+
+    @classmethod
+    def from_ireq(cls, ireq: InstallRequirement) -> "Constraint":
+        links = frozenset([ireq.link]) if ireq.link else frozenset()
+        return Constraint(ireq.specifier, ireq.hashes(trust_internet=False), links)
+
+    def __bool__(self) -> bool:
+        return bool(self.specifier) or bool(self.hashes) or bool(self.links)
+
+    def __and__(self, other: InstallRequirement) -> "Constraint":
+        if not isinstance(other, InstallRequirement):
+            return NotImplemented
+        specifier = self.specifier & other.specifier
+        hashes = self.hashes & other.hashes(trust_internet=False)
+        links = self.links
+        if other.link:
+            links = links.union([other.link])
+        return Constraint(specifier, hashes, links)
+
+    def is_satisfied_by(self, candidate: "Candidate") -> bool:
+        # Reject if there are any mismatched URL constraints on this package.
+        if self.links and not all(_match_link(link, candidate) for link in self.links):
+            return False
+        # We can safely always allow prereleases here since PackageFinder
+        # already implements the prerelease logic, and would have filtered out
+        # prerelease candidates if the user does not expect them.
+        return self.specifier.contains(candidate.version, prereleases=True)
+
+
+class Requirement:
+    @property
+    def project_name(self) -> NormalizedName:
+        """The "project name" of a requirement.
+
+        This is different from ``name`` if this requirement contains extras,
+        in which case ``name`` would contain the ``[...]`` part, while this
+        refers to the name of the project.
+        """
+        raise NotImplementedError("Subclass should override")
+
+    @property
+    def name(self) -> str:
+        """The name identifying this requirement in the resolver.
+
+        This is different from ``project_name`` if this requirement contains
+        extras, where ``project_name`` would not contain the ``[...]`` part.
+        """
+        raise NotImplementedError("Subclass should override")
+
+    def is_satisfied_by(self, candidate: "Candidate") -> bool:
+        return False
+
+    def get_candidate_lookup(self) -> CandidateLookup:
+        raise NotImplementedError("Subclass should override")
+
+    def format_for_error(self) -> str:
+        raise NotImplementedError("Subclass should override")
+
+
+def _match_link(link: Link, candidate: "Candidate") -> bool:
+    if candidate.source_link:
+        return links_equivalent(link, candidate.source_link)
+    return False
+
+
+class Candidate:
+    @property
+    def project_name(self) -> NormalizedName:
+        """The "project name" of the candidate.
+
+        This is different from ``name`` if this candidate contains extras,
+        in which case ``name`` would contain the ``[...]`` part, while this
+        refers to the name of the project.
+        """
+        raise NotImplementedError("Override in subclass")
+
+    @property
+    def name(self) -> str:
+        """The name identifying this candidate in the resolver.
+
+        This is different from ``project_name`` if this candidate contains
+        extras, where ``project_name`` would not contain the ``[...]`` part.
+        """
+        raise NotImplementedError("Override in subclass")
+
+    @property
+    def version(self) -> Version:
+        raise NotImplementedError("Override in subclass")
+
+    @property
+    def is_installed(self) -> bool:
+        raise NotImplementedError("Override in subclass")
+
+    @property
+    def is_editable(self) -> bool:
+        raise NotImplementedError("Override in subclass")
+
+    @property
+    def source_link(self) -> Optional[Link]:
+        raise NotImplementedError("Override in subclass")
+
+    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
+        raise NotImplementedError("Override in subclass")
+
+    def get_install_requirement(self) -> Optional[InstallRequirement]:
+        raise NotImplementedError("Override in subclass")
+
+    def format_for_error(self) -> str:
+        raise NotImplementedError("Subclass should override")