Add files using upload-large-folder tool

env-llmeval/lib/python3.10/site-packages/pathvalidate/__init__.py

@@ -0,0 +1,81 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+from .__version__ import __author__, __copyright__, __email__, __license__, __version__
+from ._base import AbstractSanitizer, AbstractValidator
+from ._common import (
+    ascii_symbols,
+    normalize_platform,
+    replace_ansi_escape,
+    replace_unprintable_char,
+    unprintable_ascii_chars,
+    validate_pathtype,
+    validate_unprintable_char,
+)
+from ._const import Platform
+from ._filename import (
+    FileNameSanitizer,
+    FileNameValidator,
+    is_valid_filename,
+    sanitize_filename,
+    validate_filename,
+)
+from ._filepath import (
+    FilePathSanitizer,
+    FilePathValidator,
+    is_valid_filepath,
+    sanitize_filepath,
+    validate_filepath,
+)
+from ._ltsv import sanitize_ltsv_label, validate_ltsv_label
+from ._symbol import replace_symbol, validate_symbol
+from .error import (
+    ErrorReason,
+    InvalidCharError,
+    InvalidReservedNameError,
+    NullNameError,
+    ReservedNameError,
+    ValidationError,
+    ValidReservedNameError,
+)
+
+
+__all__ = (
+    "__author__",
+    "__copyright__",
+    "__email__",
+    "__license__",
+    "__version__",
+    "AbstractSanitizer",
+    "AbstractValidator",
+    "Platform",
+    "ascii_symbols",
+    "normalize_platform",
+    "replace_ansi_escape",
+    "replace_unprintable_char",
+    "unprintable_ascii_chars",
+    "validate_pathtype",
+    "validate_unprintable_char",
+    "FileNameSanitizer",
+    "FileNameValidator",
+    "is_valid_filename",
+    "sanitize_filename",
+    "validate_filename",
+    "FilePathSanitizer",
+    "FilePathValidator",
+    "is_valid_filepath",
+    "sanitize_filepath",
+    "validate_filepath",
+    "sanitize_ltsv_label",
+    "validate_ltsv_label",
+    "replace_symbol",
+    "validate_symbol",
+    "ErrorReason",
+    "InvalidCharError",
+    "InvalidReservedNameError",
+    "NullNameError",
+    "ReservedNameError",
+    "ValidationError",
+    "ValidReservedNameError",
+)

env-llmeval/lib/python3.10/site-packages/pathvalidate/__version__.py

@@ -0,0 +1,6 @@
+__author__ = "Tsuyoshi Hombashi"
+__copyright__ = f"Copyright 2016, {__author__}"
+__license__ = "MIT License"
+__version__ = "3.2.0"
+__maintainer__ = __author__
+__email__ = "[email protected]"

env-llmeval/lib/python3.10/site-packages/pathvalidate/_base.py

@@ -0,0 +1,237 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+import abc
+import os
+import sys
+from typing import ClassVar, Optional, Sequence, Tuple
+
+from ._common import normalize_platform, unprintable_ascii_chars
+from ._const import DEFAULT_MIN_LEN, Platform
+from ._types import PathType, PlatformType
+from .error import ReservedNameError, ValidationError
+from .handler import NullValueHandler, ReservedNameHandler, ValidationErrorHandler
+
+
+class BaseFile:
+    _INVALID_PATH_CHARS: ClassVar[str] = "".join(unprintable_ascii_chars)
+    _INVALID_FILENAME_CHARS: ClassVar[str] = _INVALID_PATH_CHARS + "/"
+    _INVALID_WIN_PATH_CHARS: ClassVar[str] = _INVALID_PATH_CHARS + ':*?"<>|\t\n\r\x0b\x0c'
+    _INVALID_WIN_FILENAME_CHARS: ClassVar[str] = (
+        _INVALID_FILENAME_CHARS + _INVALID_WIN_PATH_CHARS + "\\"
+    )
+
+    @property
+    def platform(self) -> Platform:
+        return self.__platform
+
+    @property
+    def reserved_keywords(self) -> Tuple[str, ...]:
+        return self._additional_reserved_names
+
+    @property
+    def max_len(self) -> int:
+        return self._max_len
+
+    def __init__(
+        self,
+        max_len: int,
+        fs_encoding: Optional[str],
+        additional_reserved_names: Optional[Sequence[str]] = None,
+        platform_max_len: Optional[int] = None,
+        platform: Optional[PlatformType] = None,
+    ) -> None:
+        if additional_reserved_names is None:
+            additional_reserved_names = tuple()
+        self._additional_reserved_names = tuple(n.upper() for n in additional_reserved_names)
+
+        self.__platform = normalize_platform(platform)
+
+        if platform_max_len is None:
+            platform_max_len = self._get_default_max_path_len()
+
+        if max_len <= 0:
+            self._max_len = platform_max_len
+        else:
+            self._max_len = max_len
+
+        self._max_len = min(self._max_len, platform_max_len)
+
+        if fs_encoding:
+            self._fs_encoding = fs_encoding
+        else:
+            self._fs_encoding = sys.getfilesystemencoding()
+
+    def _is_posix(self) -> bool:
+        return self.platform == Platform.POSIX
+
+    def _is_universal(self) -> bool:
+        return self.platform == Platform.UNIVERSAL
+
+    def _is_linux(self, include_universal: bool = False) -> bool:
+        if include_universal:
+            return self.platform in (Platform.UNIVERSAL, Platform.LINUX)
+
+        return self.platform == Platform.LINUX
+
+    def _is_windows(self, include_universal: bool = False) -> bool:
+        if include_universal:
+            return self.platform in (Platform.UNIVERSAL, Platform.WINDOWS)
+
+        return self.platform == Platform.WINDOWS
+
+    def _is_macos(self, include_universal: bool = False) -> bool:
+        if include_universal:
+            return self.platform in (Platform.UNIVERSAL, Platform.MACOS)
+
+        return self.platform == Platform.MACOS
+
+    def _get_default_max_path_len(self) -> int:
+        if self._is_linux():
+            return 4096
+
+        if self._is_windows():
+            return 260
+
+        if self._is_posix() or self._is_macos():
+            return 1024
+
+        return 260  # universal
+
+
+class AbstractValidator(BaseFile, metaclass=abc.ABCMeta):
+    def __init__(
+        self,
+        max_len: int,
+        fs_encoding: Optional[str],
+        check_reserved: bool,
+        additional_reserved_names: Optional[Sequence[str]] = None,
+        platform_max_len: Optional[int] = None,
+        platform: Optional[PlatformType] = None,
+    ) -> None:
+        self._check_reserved = check_reserved
+
+        super().__init__(
+            max_len,
+            fs_encoding,
+            additional_reserved_names=additional_reserved_names,
+            platform_max_len=platform_max_len,
+            platform=platform,
+        )
+
+    @abc.abstractproperty
+    def min_len(self) -> int:  # pragma: no cover
+        pass
+
+    @abc.abstractmethod
+    def validate(self, value: PathType) -> None:  # pragma: no cover
+        pass
+
+    def is_valid(self, value: PathType) -> bool:
+        try:
+            self.validate(value)
+        except (TypeError, ValidationError):
+            return False
+
+        return True
+
+    def _is_reserved_keyword(self, value: str) -> bool:
+        return value in self.reserved_keywords
+
+
+class AbstractSanitizer(BaseFile, metaclass=abc.ABCMeta):
+    def __init__(
+        self,
+        validator: AbstractValidator,
+        max_len: int,
+        fs_encoding: Optional[str],
+        validate_after_sanitize: bool,
+        null_value_handler: Optional[ValidationErrorHandler] = None,
+        reserved_name_handler: Optional[ValidationErrorHandler] = None,
+        additional_reserved_names: Optional[Sequence[str]] = None,
+        platform_max_len: Optional[int] = None,
+        platform: Optional[PlatformType] = None,
+    ) -> None:
+        super().__init__(
+            max_len=max_len,
+            fs_encoding=fs_encoding,
+            additional_reserved_names=additional_reserved_names,
+            platform_max_len=platform_max_len,
+            platform=platform,
+        )
+
+        if null_value_handler is None:
+            null_value_handler = NullValueHandler.return_null_string
+        self._null_value_handler = null_value_handler
+
+        if reserved_name_handler is None:
+            reserved_name_handler = ReservedNameHandler.add_trailing_underscore
+        self._reserved_name_handler = reserved_name_handler
+
+        self._validate_after_sanitize = validate_after_sanitize
+
+        self._validator = validator
+
+    @abc.abstractmethod
+    def sanitize(self, value: PathType, replacement_text: str = "") -> PathType:  # pragma: no cover
+        pass
+
+
+class BaseValidator(AbstractValidator):
+    @property
+    def min_len(self) -> int:
+        return self._min_len
+
+    def __init__(
+        self,
+        min_len: int,
+        max_len: int,
+        fs_encoding: Optional[str],
+        check_reserved: bool,
+        additional_reserved_names: Optional[Sequence[str]] = None,
+        platform_max_len: Optional[int] = None,
+        platform: Optional[PlatformType] = None,
+    ) -> None:
+        if min_len <= 0:
+            min_len = DEFAULT_MIN_LEN
+        self._min_len = max(min_len, 1)
+
+        super().__init__(
+            max_len=max_len,
+            fs_encoding=fs_encoding,
+            check_reserved=check_reserved,
+            additional_reserved_names=additional_reserved_names,
+            platform_max_len=platform_max_len,
+            platform=platform,
+        )
+
+        self._validate_max_len()
+
+    def _validate_reserved_keywords(self, name: str) -> None:
+        if not self._check_reserved:
+            return
+
+        root_name = self.__extract_root_name(name)
+        base_name = os.path.basename(name).upper()
+
+        if self._is_reserved_keyword(root_name.upper()) or self._is_reserved_keyword(
+            base_name.upper()
+        ):
+            raise ReservedNameError(
+                f"'{root_name}' is a reserved name",
+                reusable_name=False,
+                reserved_name=root_name,
+                platform=self.platform,
+            )
+
+    def _validate_max_len(self) -> None:
+        if self.max_len < 1:
+            raise ValueError("max_len must be greater or equal to one")
+
+        if self.min_len > self.max_len:
+            raise ValueError("min_len must be lower than max_len")
+
+    @staticmethod
+    def __extract_root_name(path: str) -> str:
+        return os.path.splitext(os.path.basename(path))[0]

env-llmeval/lib/python3.10/site-packages/pathvalidate/_const.py

@@ -0,0 +1,40 @@
+import enum
+
+
+DEFAULT_MIN_LEN = 1
+INVALID_CHAR_ERR_MSG_TMPL = "invalids=({invalid}), value={value}"
+
+
+_NTFS_RESERVED_FILE_NAMES = (
+    "$Mft",
+    "$MftMirr",
+    "$LogFile",
+    "$Volume",
+    "$AttrDef",
+    "$Bitmap",
+    "$Boot",
+    "$BadClus",
+    "$Secure",
+    "$Upcase",
+    "$Extend",
+    "$Quota",
+    "$ObjId",
+    "$Reparse",
+)  # Only in root directory
+
+
+@enum.unique
+class Platform(enum.Enum):
+    """
+    Platform specifier enumeration.
+    """
+
+    #: POSIX compatible platform.
+    POSIX = "POSIX"
+
+    #: platform independent. note that absolute paths cannot specify this.
+    UNIVERSAL = "universal"
+
+    LINUX = "Linux"
+    WINDOWS = "Windows"
+    MACOS = "macOS"

env-llmeval/lib/python3.10/site-packages/pathvalidate/_symbol.py

@@ -0,0 +1,92 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+import re
+from typing import Sequence
+
+from ._common import ascii_symbols, to_str, unprintable_ascii_chars
+from .error import InvalidCharError
+
+
+__RE_SYMBOL = re.compile(
+    "[{}]".format(re.escape("".join(ascii_symbols + unprintable_ascii_chars))), re.UNICODE
+)
+
+
+def validate_symbol(text: str) -> None:
+    """
+    Verifying whether symbol(s) included in the ``text`` or not.
+
+    Args:
+        text:
+            Input text to validate.
+
+    Raises:
+        ValidationError (ErrorReason.INVALID_CHARACTER):
+            If symbol(s) included in the ``text``.
+    """
+
+    match_list = __RE_SYMBOL.findall(to_str(text))
+    if match_list:
+        raise InvalidCharError(f"invalid symbols found: {match_list}")
+
+
+def replace_symbol(
+    text: str,
+    replacement_text: str = "",
+    exclude_symbols: Sequence[str] = [],
+    is_replace_consecutive_chars: bool = False,
+    is_strip: bool = False,
+) -> str:
+    """
+    Replace all of the symbols in the ``text``.
+
+    Args:
+        text:
+            Input text.
+        replacement_text:
+            Replacement text.
+        exclude_symbols:
+            Symbols that exclude from the replacement.
+        is_replace_consecutive_chars:
+            If |True|, replace consecutive multiple ``replacement_text`` characters
+            to a single character.
+        is_strip:
+            If |True|, strip ``replacement_text`` from the beginning/end of the replacement text.
+
+    Returns:
+        A replacement string.
+
+    Example:
+
+        :ref:`example-sanitize-symbol`
+    """
+
+    if exclude_symbols:
+        regexp = re.compile(
+            "[{}]".format(
+                re.escape(
+                    "".join(set(ascii_symbols + unprintable_ascii_chars) - set(exclude_symbols))
+                )
+            ),
+            re.UNICODE,
+        )
+    else:
+        regexp = __RE_SYMBOL
+
+    try:
+        new_text = regexp.sub(replacement_text, to_str(text))
+    except TypeError:
+        raise TypeError("text must be a string")
+
+    if not replacement_text:
+        return new_text
+
+    if is_replace_consecutive_chars:
+        new_text = re.sub(f"{re.escape(replacement_text)}+", replacement_text, new_text)
+
+    if is_strip:
+        new_text = new_text.strip(replacement_text)
+
+    return new_text

env-llmeval/lib/python3.10/site-packages/pathvalidate/_types.py

@@ -0,0 +1,8 @@
+from pathlib import Path
+from typing import TypeVar
+
+from ._const import Platform
+
+
+PathType = TypeVar("PathType", str, Path)
+PlatformType = TypeVar("PlatformType", str, Platform)

env-llmeval/lib/python3.10/site-packages/pathvalidate/argparse.py

@@ -0,0 +1,47 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+from argparse import ArgumentTypeError
+
+from ._filename import sanitize_filename, validate_filename
+from ._filepath import sanitize_filepath, validate_filepath
+from .error import ValidationError
+
+
+def validate_filename_arg(value: str) -> str:
+    if not value:
+        return ""
+
+    try:
+        validate_filename(value)
+    except ValidationError as e:
+        raise ArgumentTypeError(e)
+
+    return value
+
+
+def validate_filepath_arg(value: str) -> str:
+    if not value:
+        return ""
+
+    try:
+        validate_filepath(value, platform="auto")
+    except ValidationError as e:
+        raise ArgumentTypeError(e)
+
+    return value
+
+
+def sanitize_filename_arg(value: str) -> str:
+    if not value:
+        return ""
+
+    return sanitize_filename(value)
+
+
+def sanitize_filepath_arg(value: str) -> str:
+    if not value:
+        return ""
+
+    return sanitize_filepath(value, platform="auto")

env-llmeval/lib/python3.10/site-packages/pathvalidate/click.py

@@ -0,0 +1,48 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+import click
+from click.core import Context, Option
+
+from ._filename import sanitize_filename, validate_filename
+from ._filepath import sanitize_filepath, validate_filepath
+from .error import ValidationError
+
+
+def validate_filename_arg(ctx: Context, param: Option, value: str) -> str:
+    if not value:
+        return ""
+
+    try:
+        validate_filename(value)
+    except ValidationError as e:
+        raise click.BadParameter(str(e))
+
+    return value
+
+
+def validate_filepath_arg(ctx: Context, param: Option, value: str) -> str:
+    if not value:
+        return ""
+
+    try:
+        validate_filepath(value)
+    except ValidationError as e:
+        raise click.BadParameter(str(e))
+
+    return value
+
+
+def sanitize_filename_arg(ctx: Context, param: Option, value: str) -> str:
+    if not value:
+        return ""
+
+    return sanitize_filename(value)
+
+
+def sanitize_filepath_arg(ctx: Context, param: Option, value: str) -> str:
+    if not value:
+        return ""
+
+    return sanitize_filepath(value)

env-llmeval/lib/python3.10/site-packages/pathvalidate/error.py

@@ -0,0 +1,253 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+import enum
+from typing import Dict, Optional
+
+from ._const import Platform
+
+
+def _to_error_code(code: int) -> str:
+    return f"PV{code:04d}"
+
+
+class ErrorAttrKey:
+    BYTE_COUNT = "byte_count"
+    DESCRIPTION = "description"
+    FS_ENCODING = "fs_encoding"
+    PLATFORM = "platform"
+    REASON = "reason"
+    RESERVED_NAME = "reserved_name"
+    REUSABLE_NAME = "reusable_name"
+
+
+@enum.unique
+class ErrorReason(enum.Enum):
+    """
+    Validation error reasons.
+    """
+
+    NULL_NAME = (_to_error_code(1001), "NULL_NAME", "the value must not be an empty")
+    RESERVED_NAME = (
+        _to_error_code(1002),
+        "RESERVED_NAME",
+        "found a reserved name by a platform",
+    )
+    INVALID_CHARACTER = (
+        _to_error_code(1100),
+        "INVALID_CHARACTER",
+        "invalid characters found",
+    )
+    INVALID_LENGTH = (
+        _to_error_code(1101),
+        "INVALID_LENGTH",
+        "found an invalid string length",
+    )
+    FOUND_ABS_PATH = (
+        _to_error_code(1200),
+        "FOUND_ABS_PATH",
+        "found an absolute path where must be a relative path",
+    )
+    MALFORMED_ABS_PATH = (
+        _to_error_code(1201),
+        "MALFORMED_ABS_PATH",
+        "found a malformed absolute path",
+    )
+    INVALID_AFTER_SANITIZE = (
+        _to_error_code(2000),
+        "INVALID_AFTER_SANITIZE",
+        "found invalid value after sanitizing",
+    )
+
+    @property
+    def code(self) -> str:
+        """str: Error code."""
+        return self.__code
+
+    @property
+    def name(self) -> str:
+        """str: Error reason name."""
+        return self.__name
+
+    @property
+    def description(self) -> str:
+        """str: Error reason description."""
+        return self.__description
+
+    def __init__(self, code: str, name: str, description: str) -> None:
+        self.__name = name
+        self.__code = code
+        self.__description = description
+
+    def __str__(self) -> str:
+        return f"[{self.__code}] {self.__description}"
+
+
+class ValidationError(ValueError):
+    """
+    Exception class of validation errors.
+    """
+
+    @property
+    def platform(self) -> Optional[Platform]:
+        """
+        :py:class:`~pathvalidate.Platform`: Platform information.
+        """
+        return self.__platform
+
+    @property
+    def reason(self) -> ErrorReason:
+        """
+        :py:class:`~pathvalidate.error.ErrorReason`: The cause of the error.
+        """
+        return self.__reason
+
+    @property
+    def description(self) -> Optional[str]:
+        """Optional[str]: Error description."""
+        return self.__description
+
+    @property
+    def reserved_name(self) -> str:
+        """str: Reserved name."""
+        return self.__reserved_name
+
+    @property
+    def reusable_name(self) -> Optional[bool]:
+        """Optional[bool]: Whether the name is reusable or not."""
+        return self.__reusable_name
+
+    @property
+    def fs_encoding(self) -> Optional[str]:
+        """Optional[str]: File system encoding."""
+        return self.__fs_encoding
+
+    @property
+    def byte_count(self) -> Optional[int]:
+        """Optional[int]: Byte count of the path."""
+        return self.__byte_count
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore
+        if ErrorAttrKey.REASON not in kwargs:
+            raise ValueError(f"{ErrorAttrKey.REASON} must be specified")
+
+        self.__reason: ErrorReason = kwargs.pop(ErrorAttrKey.REASON)
+        self.__byte_count: Optional[int] = kwargs.pop(ErrorAttrKey.BYTE_COUNT, None)
+        self.__platform: Optional[Platform] = kwargs.pop(ErrorAttrKey.PLATFORM, None)
+        self.__description: Optional[str] = kwargs.pop(ErrorAttrKey.DESCRIPTION, None)
+        self.__reserved_name: str = kwargs.pop(ErrorAttrKey.RESERVED_NAME, "")
+        self.__reusable_name: Optional[bool] = kwargs.pop(ErrorAttrKey.REUSABLE_NAME, None)
+        self.__fs_encoding: Optional[str] = kwargs.pop(ErrorAttrKey.FS_ENCODING, None)
+
+        try:
+            super().__init__(*args[0], **kwargs)
+        except IndexError:
+            super().__init__(*args, **kwargs)
+
+    def as_slog(self) -> Dict[str, str]:
+        """Return a dictionary representation of the error.
+
+        Returns:
+            Dict[str, str]: A dictionary representation of the error.
+        """
+
+        slog: Dict[str, str] = {
+            "code": self.reason.code,
+            ErrorAttrKey.DESCRIPTION: self.reason.description,
+        }
+        if self.platform:
+            slog[ErrorAttrKey.PLATFORM] = self.platform.value
+        if self.description:
+            slog[ErrorAttrKey.DESCRIPTION] = self.description
+        if self.__reusable_name is not None:
+            slog[ErrorAttrKey.REUSABLE_NAME] = str(self.__reusable_name)
+        if self.__fs_encoding:
+            slog[ErrorAttrKey.FS_ENCODING] = self.__fs_encoding
+        if self.__byte_count:
+            slog[ErrorAttrKey.BYTE_COUNT] = str(self.__byte_count)
+
+        return slog
+
+    def __str__(self) -> str:
+        item_list = []
+        header = str(self.reason)
+
+        if Exception.__str__(self):
+            item_list.append(Exception.__str__(self))
+
+        if self.platform:
+            item_list.append(f"{ErrorAttrKey.PLATFORM}={self.platform.value}")
+        if self.description:
+            item_list.append(f"{ErrorAttrKey.DESCRIPTION}={self.description}")
+        if self.__reusable_name is not None:
+            item_list.append(f"{ErrorAttrKey.REUSABLE_NAME}={self.reusable_name}")
+        if self.__fs_encoding:
+            item_list.append(f"{ErrorAttrKey.FS_ENCODING}={self.__fs_encoding}")
+        if self.__byte_count is not None:
+            item_list.append(f"{ErrorAttrKey.BYTE_COUNT}={self.__byte_count:,d}")
+
+        if item_list:
+            header += ": "
+
+        return header + ", ".join(item_list).strip()
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+
+class NullNameError(ValidationError):
+    """[Deprecated]
+    Exception raised when a name is empty.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore
+        kwargs[ErrorAttrKey.REASON] = ErrorReason.NULL_NAME
+
+        super().__init__(args, **kwargs)
+
+
+class InvalidCharError(ValidationError):
+    """
+    Exception raised when includes invalid character(s) within a string.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+        kwargs[ErrorAttrKey.REASON] = ErrorReason.INVALID_CHARACTER
+
+        super().__init__(args, **kwargs)
+
+
+class ReservedNameError(ValidationError):
+    """
+    Exception raised when a string matched a reserved name.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+        kwargs[ErrorAttrKey.REASON] = ErrorReason.RESERVED_NAME
+
+        super().__init__(args, **kwargs)
+
+
+class ValidReservedNameError(ReservedNameError):
+    """[Deprecated]
+    Exception raised when a string matched a reserved name.
+    However, it can be used as a name.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+        kwargs[ErrorAttrKey.REUSABLE_NAME] = True
+
+        super().__init__(args, **kwargs)
+
+
+class InvalidReservedNameError(ReservedNameError):
+    """[Deprecated]
+    Exception raised when a string matched a reserved name.
+    Moreover, the reserved name is invalid as a name.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+        kwargs[ErrorAttrKey.REUSABLE_NAME] = False
+
+        super().__init__(args, **kwargs)

env-llmeval/lib/python3.10/site-packages/pathvalidate/handler.py

@@ -0,0 +1,139 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+
+import warnings
+from datetime import datetime
+from typing import Callable
+
+from .error import ValidationError
+
+
+ValidationErrorHandler = Callable[[ValidationError], str]
+
+
+def return_null_string(e: ValidationError) -> str:
+    """Null value handler that always returns an empty string.
+
+    Args:
+        e (ValidationError): A validation error.
+
+    Returns:
+        str: An empty string.
+    """
+
+    warnings.warn(
+        "'return_null_string' is deprecated. Use 'NullValueHandler.return_null_string' instead.",
+        DeprecationWarning,
+    )
+
+    return ""
+
+
+def return_timestamp(e: ValidationError) -> str:
+    """Null value handler that returns a timestamp of when the function was called.
+
+    Args:
+        e (ValidationError): A validation error.
+
+    Returns:
+        str: A timestamp.
+    """
+
+    warnings.warn(
+        "'return_timestamp' is deprecated. Use 'NullValueHandler.reserved_name_handler' instead.",
+        DeprecationWarning,
+    )
+
+    return str(datetime.now().timestamp())
+
+
+def raise_error(e: ValidationError) -> str:
+    """Null value handler that always raises an exception.
+
+    Args:
+        e (ValidationError): A validation error.
+
+    Raises:
+        ValidationError: Always raised.
+    """
+
+    raise e
+
+
+class NullValueHandler:
+    @classmethod
+    def return_null_string(cls, e: ValidationError) -> str:
+        """Null value handler that always returns an empty string.
+
+        Args:
+            e (ValidationError): A validation error.
+
+        Returns:
+            str: An empty string.
+        """
+
+        return ""
+
+    @classmethod
+    def return_timestamp(cls, e: ValidationError) -> str:
+        """Null value handler that returns a timestamp of when the function was called.
+
+        Args:
+            e (ValidationError): A validation error.
+
+        Returns:
+            str: A timestamp.
+        """
+
+        return str(datetime.now().timestamp())
+
+
+class ReservedNameHandler:
+    @classmethod
+    def add_leading_underscore(cls, e: ValidationError) -> str:
+        """Reserved name handler that adds a leading underscore (``"_"``) to the name
+        except for ``"."`` and ``".."``.
+
+        Args:
+            e (ValidationError): A reserved name error.
+
+        Returns:
+            str: The converted name.
+        """
+
+        if e.reserved_name in (".", "..") or e.reusable_name:
+            return e.reserved_name
+
+        return f"_{e.reserved_name}"
+
+    @classmethod
+    def add_trailing_underscore(cls, e: ValidationError) -> str:
+        """Reserved name handler that adds a trailing underscore (``"_"``) to the name
+        except for ``"."`` and ``".."``.
+
+        Args:
+            e (ValidationError): A reserved name error.
+
+        Returns:
+            str: The converted name.
+        """
+
+        if e.reserved_name in (".", "..") or e.reusable_name:
+            return e.reserved_name
+
+        return f"{e.reserved_name}_"
+
+    @classmethod
+    def as_is(cls, e: ValidationError) -> str:
+        """Reserved name handler that returns the name as is.
+
+        Args:
+            e (ValidationError): A reserved name error.
+
+        Returns:
+            str: The name as is.
+        """
+
+        return e.reserved_name

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/Backtrace.h

@@ -0,0 +1,2 @@
+#include <c10/util/Backtrace.h>
+#include <c10/util/Type.h>

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/IListRef.h

@@ -0,0 +1,631 @@
+#pragma once
+
+#include <ATen/core/ivalue_to.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+
+#include <functional>
+#include <initializer_list>
+#include <iterator>
+#include <type_traits>
+
+/*
+ * [Note: IListRef]
+ * Wrapper around different API containers (e.g. boxed and unboxed).
+ *
+ * What is it?
+ * ===========
+ * It is a tagged union of both boxed and unboxed API containers.
+ * Working implementations:
+ *
+ * - `IListRef<at::Tensor>`
+ * - `IListRef<at::OptionalTensorRef>`
+ *
+ * Note that `IListRef` is a view type. Meaning that it won't own the
+ * tensors it holds. It's intended to be used only as argument parameters.
+ * Specifically, where these 2 worlds overlap.
+ *
+ * What is this for?
+ * =================
+ * Historically, PyTorch has maintained 2 different APIs: the unboxed
+ * (called from C++ API and Python eager mode) and boxed APIs (called
+ * from the TorchScript JIT, mobile interpreter, and boxed fallbacks).
+ *
+ * Calling unboxed kernels from the boxed "world" and vice-versa may
+ * result in non-negligible overhead. Lists are one of those types:
+ *
+ * - Boxed world: `c10::List`
+ * - Unboxed world: `c10::ArrayRef`
+ *
+ * In this context, `c10::IListRef` solves this problem by wrapping those
+ * 2 container types, so that we don't need to convert from one to
+ * the other.
+ *
+ * (see https://github.com/pytorch/pytorch/issues/66328)
+ *
+ * What does it do?
+ * ================
+ * This container wraps around the different tagged containers
+ * (currently, only boxed and unboxed), without incurring in extra
+ * overhead for converting from one to another. It does so while
+ * exposing usual container methods, which dispatch to corresponding
+ * implementations.
+ *
+ * While it works with different container types, it introduces
+ * overhead for repeatedly calling member functions (since those will
+ * get dispatched, again). Therefore, you should only use it to iterate
+ * through the list up to one time. If you need to do more complex things,
+ * call `materialize()` first.
+ *
+ * Adding support for a new Tag
+ * ============================
+ * Suppose we want to add a new tag: `Chest`. Here are the steps
+ * we would have to go through:
+ *
+ * 1. Add a line for it in the macro `TORCH_ILISTREF_FORALL_TAGS`.
+ *
+ *   #define TORCH_ILISTREF_FORALL_TAGS(_, ...) \
+ *     ...
+ *     _(Chest, ##__VA_ARGS__)
+ *
+ * 2. Add type aliases, union members, and constructors.
+ *
+ *   template <typename T>
+ *   class IListRef {
+ *     ...
+ *     using chest_type =
+ *       typename detail::IListRefTagImpl<T, IListRefTag::Chest>::list_type;
+ *     ...
+ *     IListRef(...) : tag_(IListRefTag::Chest) {
+ *       ...
+ *     }
+ *     ...
+ *     union Payload {
+ *       ...
+ *       chest_type chest;
+ *       ...
+ *     };
+ *     ...
+ *   };
+ *
+ * 3. Add a default implementation for it (in 'IListRef_inl.h'). It's
+ *    preferable to make the default implementation work for `T = Tensor`
+ *    (both `Unboxed` and `Boxed` do it).
+ *
+ *   template <typename T, typename ListElemT>
+ *   class IListRefTagImplBase<IListRefTag::Chest, T, ListElemT> {
+ *    public:
+ *     using elem_type = ListElemT;
+ *     using list_type = ChestContainer<elem_type>;
+ *
+ *     static const list_type& unwrap(const IListRef<T>& ilist) { ... }
+ *
+ *     static typename list_type::const_iterator& unwrap(
+ *         IListRefIterator<T>& it) { ... }
+ *
+ *     static const typename list_type::const_iterator& unwrap(
+ *         const IListRefIterator<T>& it) { ... }
+ *
+ *     static IListRefConstRef<T> iterator_get(
+ *         const typename list_type::const_iterator& it) { ... }
+ *   }
+ *
+ * 4. Add an specialization for each of the already supported types.
+ *    Finally, for consistency, add them to the tracking list.
+ *    (see [Note: IListRefTagImpl Specializations])
+ *
+ *   template <>
+ *   class IListRefTagImpl<IListRefTag::Chest, at::Tensor>
+ *       : public IListRefTagImplBase<IListRefTag::Chest, at::Tensor> {};
+ *
+ * Adding support for a new Type
+ * =============================
+ * Suppose we want to add support for a new type: `Matrix`.
+ * Here are the steps we would have to go through:
+ *
+ * 1. Add an specialization for each of the existing tags.
+ *    For consistency, add them to the tracking list.
+ *    (see [Note: IListRefTagImpl Specializations])
+ *
+ *   template <>
+ *   class IListRefTagImpl<IListRefTag::Unboxed, Matrix>
+ *       : public IListRefTagImplBase<IListRefTag::Unboxed, Matrix> {};
+ *
+ *   template <>
+ *   class IListRefTagImpl<Matrix, IListRefTag::Boxed>
+ *       : public IListRefTagImplBase<IListRefTag::Boxed, Matrix> {};
+ *
+ * Common Problems
+ * ===============
+ * 1. One of `IListRef(Iterator)` methods are failing to compile.
+ *
+ *     That may be happening because the container type you added
+ *     is not compatible with the code written for that method. If
+ *     that's true, then you might have to transform that code into
+ *     a static method call (see `List::operator[]` method).
+ *
+ * 2. Can't make `IListRefIterator<T>::operator*` return a const-reference.
+ *
+ *    First, keep in mind that we assume that boxed containers will
+ *    have to deal with `IValue` (e.g. `c10::List`). In this context,
+ *    what may be happening is that `IValue` doesn't store internally
+ *    your type `T`. Instead, it constructs a type new `T` everytime
+ *    you try to get `T` for it (see `IListRef<at::OptinalTensorRef>`).
+ */
+
+namespace c10 {
+template <typename T>
+class IListRef;
+
+/*
+ * Applies arbitrary macros to each `IListRefTag`.
+ */
+#define TORCH_ILISTREF_FORALL_TAGS(_, ...) \
+  _(Unboxed, ##__VA_ARGS__)                \
+  _(Boxed, ##__VA_ARGS__)                  \
+  _(Materialized, ##__VA_ARGS__)
+
+/*
+ * Defines a "switch-case" for `TAG`. Inside, it executes `BODY`,
+ * while bringing to scope:
+ *
+ * - `ImplT`: the implementation class for `TAG`
+ * - `this_`: the result of unwrapping `this`
+ */
+#define TORCH_ILISTREF_UNWRAP_CASE(TAG, BODY)                        \
+  case c10::IListRefTag::TAG: {                                      \
+    using ImplT = c10::detail::IListRefTagImpl<IListRefTag::TAG, T>; \
+    auto& this_ = ImplT::unwrap(*this);                              \
+    BODY                                                             \
+  } break;
+
+/*
+ * Dispatches the unwrap call, depending on `TAG`, followed by
+ * the execution of `BODY`. It aborts if `TAG` is not a `IListRefTag`.
+ *
+ * This macro is useful because it allows us to handle different
+ * types (that correspond to different tags) to be implemented
+ * only once. We can do it even when the implementation of the
+ * different tags aren't syntatically the same, by dispatching
+ * it to a function (e.g. `ImplT::<dispatch-function>(this_)`).
+ */
+#define TORCH_ILISTREF_UNWRAP(TAG, BODY)                         \
+  switch (TAG) {                                                 \
+    TORCH_ILISTREF_FORALL_TAGS(TORCH_ILISTREF_UNWRAP_CASE, BODY) \
+    break;                                                       \
+    default:                                                     \
+      TORCH_INTERNAL_ASSERT(false, "invalid IListRef tag.");     \
+  }
+
+enum class IListRefTag {
+#define DEFINE_TAG(tag, ...) tag,
+  TORCH_ILISTREF_FORALL_TAGS(DEFINE_TAG)
+#undef DEFINE_TAG
+      None
+};
+
+namespace detail {
+/*
+ * Type alias that specifies whether we return a reference or a copy of `T`.
+ *
+ * What is this for?
+ * =================
+ * Since values in the boxed world are represented by an `IValue`, we also
+ * depend on whether it can be converted to a const-reference (`Tensor`) or
+ * has to create a new copy of `T` (`OptionalTensorRef`).
+ */
+template <typename T>
+using IListRefConstRef = typename ivalue_to_const_ref_overload_return<T>::type;
+
+/*
+ * Interface that implements key functions for each `IListRefTag` type.
+ *
+ * What is this for?
+ * =================
+ * Given an `IListRef(Iterator)<T>`, some methods have to be implemented
+ * differently for each `TAG`. Therefore, the methods inside this class
+ * are used as dispatch targets for the different `IListRefTag` values.
+ *
+ * You should create an specialization of this class for each possible
+ * combination of `IListRefTag` type (except `None`) and element types
+ * (e.g. `Tensor`).
+ *
+ * What does it do?
+ * ================
+ * 1. defines static methods to be used as dispatch targets by both
+ *    `IListRef<T>` and `IListRefIterator<T>` (see the implementation of
+ *    `IListRefTagImplBase`).
+ *
+ * 2. defines the `elem_type` and `list_type` aliases that will be
+ *    used in the definition of `IListRef<T>`. In general, we should do
+ *    so by inheriting from `IListRefTagImplBase<TAG, T, ListElemT>`.
+ *
+ * [Note: IListRefTagImpl Specialization]
+ * ======================================
+ * For `IListRef(Iterator)<at::Tensor>`:
+ * - <IListRefTag::Unboxed, at::Tensor>
+ * - <IListRefTag::Boxed, at::Tensor>
+ * - <IListRefTag::Materialized, at::Tensor>
+ *
+ * For `IListRef(Iterator)<at::OptionalTensorRef>`:
+ * - <IListRefTag::Unboxed, at::OptionalTensorRef>
+ * - <IListRefTag::Boxed, at::OptionalTensorRef>
+ * - <IListRefTag::Materialized, at::OptionalTensorRef>
+ */
+template <IListRefTag TAG, typename T>
+class IListRefTagImpl {};
+
+/*
+ * Base implementation of `IListRefTagImpl<TAG, T>` methods.
+ *
+ * What is this for?
+ * =================
+ * This should make adding specializations for new types easier. For
+ * example, one should be able to add a new type just by making its
+ * `IListRefTagImpl` specialization inherit from `IListRefTagImplBase`.
+ *
+ * You should create a partial specialization for this class only if
+ * you introduce a new `IListRefTag`. The idea being that there is one
+ * default implementation for each possible value of `IListRefTag`.
+ *
+ * What does it do?
+ * ================
+ * 1. defines `elem_type` as an alias to `ListElemT`.
+ *
+ * 1. defines `list_type` as an alias to the default container type
+ *    that will hold a collection of `elem_type`. The idea being that
+ *    all types tagged as `TAG` will have `list_type` as its container,
+ *    with different `elem_type`.
+ *
+ * 3. defines the default implementation for each of the methods that
+ *    are supposed to be defined on `IListRefTagImpl` specializations.
+ *
+ * 4. inheriting from `IListRefTagImplBase<TAG, T, ListElemT>` also means
+ *    that the payload of the type `IListRef<T>` will be of type `list_type`
+ *    when it is tagged as `TAG`.
+ */
+template <IListRefTag TAG, typename T, typename ListElemT = T>
+class IListRefTagImplBase {};
+
+/*
+ * Materialized container for `IListRef<T>`.
+ *
+ * What is this for?
+ * =================
+ * Container that groups `T` references together. This exchanges the
+ * overhead of every method call from `IListRef<T>` for a dynamic allocation.
+ *
+ * You should use this container instead of `IListRef<T>` if:
+ *
+ *   - You are going to iterate the list more than once
+ *   - You need to repeatedly access arbitrary elements (using `operator[]`)
+ * What does it do?
+
+ * ================
+ * Removes the reference (&) from the type, and wraps it into a
+ * `std::reference_wrapper`. If `IListRefConstRef<T>` is not a
+ * reference type, then it's left unchanged.
+ */
+template <typename T>
+using _MaterializedIListRefElem = typename std::conditional<
+    std::is_reference<T>::value,
+    typename std::reference_wrapper<typename std::remove_reference<T>::type>,
+    T>::type;
+
+template <typename T>
+using MaterializedIListRefElem = _MaterializedIListRefElem<IListRefConstRef<T>>;
+
+template <typename T>
+using MaterializedIListRef = std::vector<MaterializedIListRefElem<T>>;
+
+} // namespace detail
+
+/*
+ * Iterator for `IListRef<T>`.
+ *
+ * What is it?
+ * ===========
+ * Currently, a `std::bidirectional_iterator` that wraps the iterator
+ * types defined for each of the `IListRefTag`.
+ *
+ * One should be able to use it, as if it were the unwrapped
+ * iterators themselves.
+
+ * What does it do?
+ * ================
+ * Similarly to `IListRef<T>`, this is a wrapper class. Specifically, it
+ * wraps each container's `const_iterator` type alias. So, for example,
+ * given that the container for `IListRefTag::Boxed` is `c10::List`, this
+ * iterator will wrap a `c10::List::const_iterator`.
+ *
+ * [Note: MSVC Iterator Debug]
+ * ===========================
+ * MSVC `vector<T>::iterator` implementation (used in the boxed variant)
+ * makes it so this union's destructor, copy-constructor (assignment), and
+ * move-constructor (assignment) are implicitly deleted.
+ *
+ * Therefore, we need to explicitly define them as needed. Follows a list
+ * of places where these are needed and their reason:
+ *
+ *   - `Payload` destructor:
+ *     it is deleted only if the macro `_ITERATOR_DEBUG_LEVEL` is set to 2.
+ *
+ *   - `IListRefIterator` destructor:
+ *     same as above. However, we need to explicitly call the variant
+ *     destructor explicitly.
+ *
+ *   - `IListRefIterator` copy-constructor:
+ *     it is deleted only if the macro `_ITERATOR_DEBUG_LEVEL` is different
+ *     than 0.
+ */
+template <typename T>
+class IListRefIterator {
+ private:
+#define DEFINE_FRIEND_CLASS(TAG, ...)                        \
+  friend class detail::IListRefTagImpl<IListRefTag::TAG, T>; \
+  friend class detail::IListRefTagImplBase<                  \
+      IListRefTag::TAG,                                      \
+      T,                                                     \
+      typename detail::IListRefTagImpl<IListRefTag::TAG, T>::elem_type>;
+  TORCH_ILISTREF_FORALL_TAGS(DEFINE_FRIEND_CLASS)
+#undef DEFINE_FRIEND_CLASS
+
+ public:
+  // C++17 friendly std::iterator implementation
+  using iterator_category = std::bidirectional_iterator_tag;
+  using value_type = T;
+  using difference_type = std::ptrdiff_t;
+  using pointer = T*;
+  using reference = T&;
+
+  using unboxed_iterator_type = typename detail::
+      IListRefTagImpl<IListRefTag::Unboxed, T>::list_type::const_iterator;
+  using boxed_iterator_type = typename detail::
+      IListRefTagImpl<IListRefTag::Boxed, T>::list_type::const_iterator;
+  using materialized_iterator_type =
+      typename detail::MaterializedIListRef<T>::const_iterator;
+
+  IListRefIterator() : tag_(IListRefTag::None) {}
+
+#if defined(_MSC_VER) && _ITERATOR_DEBUG_LEVEL != 0
+  // See [Note: MSVC Iterator Debug]
+  IListRefIterator(const IListRefIterator& iterator)
+      : tag_(iterator.tag_) {
+    switch (tag_) {
+      case IListRefTag::Boxed:
+        payload_.boxed_iterator = iterator.payload_.boxed_iterator;
+        break;
+      case IListRefTag::Unboxed:
+        payload_.unboxed_iterator = iterator.payload_.unboxed_iterator;
+        break;
+      case IListRefTag::Materialized:
+        payload_.materialized_iterator = iterator.payload_.materialized_iterator;
+        break;
+      default:
+        TORCH_INTERNAL_ASSERT(false, "invalid IListRef tag.");
+    }
+  }
+#endif
+
+#if defined(_MSC_VER) && _ITERATOR_DEBUG_LEVEL == 2
+  // See [Note: MSVC Iterator Debug]
+  ~IListRefIterator() noexcept(false) {
+    switch (tag_) {
+      case IListRefTag::Boxed:
+        payload_.boxed_iterator.~boxed_iterator_type();
+        break;
+      case IListRefTag::Unboxed:
+        payload_.unboxed_iterator.~unboxed_iterator_type();
+        break;
+      case IListRefTag::Materialized:
+        payload_.materialized_iterator.~materialized_iterator_type();
+        break;
+      default:
+        TORCH_INTERNAL_ASSERT(false, "invalid IListRef tag.");
+    }
+  }
+#endif
+
+  IListRefIterator(boxed_iterator_type boxed) : tag_(IListRefTag::Boxed) {
+    payload_.boxed_iterator = boxed;
+  }
+
+  IListRefIterator(unboxed_iterator_type unboxed) : tag_(IListRefTag::Unboxed) {
+    payload_.unboxed_iterator = unboxed;
+  }
+
+  IListRefIterator(materialized_iterator_type materialized) : tag_(IListRefTag::Materialized) {
+    payload_.materialized_iterator = materialized;
+  }
+
+  detail::IListRefConstRef<T> operator*() const {
+    TORCH_ILISTREF_UNWRAP(tag_, { return ImplT::iterator_get(this_); });
+  }
+
+  IListRefIterator& operator++() {
+    TORCH_ILISTREF_UNWRAP(tag_, { ++this_; });
+    return *this;
+  }
+
+  IListRefIterator operator++(int) {
+    auto old = *this;
+    TORCH_ILISTREF_UNWRAP(tag_, { ++this_; });
+    return old;
+  }
+
+  IListRefIterator& operator--() {
+    TORCH_ILISTREF_UNWRAP(tag_, { --this_; });
+    return *this;
+  }
+
+  IListRefIterator operator--(int) {
+    auto old = *this;
+    TORCH_ILISTREF_UNWRAP(tag_, { --this_; });
+    return old;
+  }
+
+  bool operator==(const IListRefIterator& rhs) const {
+    if (tag_ != rhs.tag_) {
+      return false;
+    }
+    TORCH_ILISTREF_UNWRAP(tag_, {
+      auto& rhs_it = ImplT::unwrap(rhs);
+      return this_ == rhs_it;
+    });
+  }
+
+  bool operator!=(const IListRefIterator& rhs) const {
+    return !(*this == rhs);
+  }
+
+ private:
+  union Payload {
+    boxed_iterator_type boxed_iterator;
+    unboxed_iterator_type unboxed_iterator;
+    materialized_iterator_type materialized_iterator;
+    void* _init_ptr;
+    Payload() : _init_ptr(nullptr) {}
+#if defined(_MSC_VER)
+    // See [Note: MSVC Iterator Debug]
+    ~Payload() {}
+#endif
+  };
+
+  Payload payload_;
+  IListRefTag tag_;
+};
+
+/*
+ * See [Note: IListRef]
+ */
+template <typename T>
+class IListRef {
+ private:
+#define DEFINE_FRIEND_CLASS(TAG, ...)                        \
+  friend class detail::IListRefTagImpl<IListRefTag::TAG, T>; \
+  friend class detail::IListRefTagImplBase<                  \
+      IListRefTag::TAG,                                      \
+      T,                                                     \
+      typename detail::IListRefTagImpl<IListRefTag::TAG, T>::elem_type>;
+  TORCH_ILISTREF_FORALL_TAGS(DEFINE_FRIEND_CLASS)
+#undef DEFINE_FRIEND_CLASS
+
+ public:
+  using unboxed_type =
+      typename detail::IListRefTagImpl<IListRefTag::Unboxed, T>::list_type;
+  using boxed_type =
+      typename detail::IListRefTagImpl<IListRefTag::Boxed, T>::list_type;
+  using materialized_type =
+      typename detail::MaterializedIListRef<T>;
+
+  using iterator = IListRefIterator<T>;
+  using const_iterator = IListRefIterator<T>;
+  using reverse_iterator = std::reverse_iterator<iterator>;
+  using value_type = typename iterator::value_type;
+
+  IListRef() : tag_(IListRefTag::None) {}
+
+  IListRef(const boxed_type& boxed) : tag_(IListRefTag::Boxed) {
+    payload_.boxed = &boxed;
+  }
+
+  IListRef(const unboxed_type& unboxed) : tag_(IListRefTag::Unboxed) {
+    payload_.unboxed = unboxed;
+  }
+
+  IListRef(const std::initializer_list<T>& list) : tag_(IListRefTag::Unboxed) {
+    payload_.unboxed = at::ArrayRef<T>(list);
+  }
+
+  template <
+      typename... UnboxedConstructorArgs,
+      typename = std::enable_if_t<
+          std::is_constructible<unboxed_type, UnboxedConstructorArgs...>::value>>
+  IListRef(UnboxedConstructorArgs&&... args) : tag_(IListRefTag::Unboxed) {
+    payload_.unboxed = unboxed_type(std::forward<UnboxedConstructorArgs>(args)...);
+  }
+
+  IListRef(const materialized_type& materialized) : tag_(IListRefTag::Materialized) {
+    payload_.materialized = &materialized;
+  }
+
+  size_t size() const {
+    TORCH_ILISTREF_UNWRAP(tag_, { return this_.size(); });
+  }
+
+  bool empty() const {
+    return size() == 0;
+  }
+
+  iterator begin() const {
+    TORCH_ILISTREF_UNWRAP(tag_, { return this_.begin(); });
+  }
+
+  iterator end() const {
+    TORCH_ILISTREF_UNWRAP(tag_, { return this_.end(); });
+  }
+
+  detail::IListRefConstRef<T> front() const {
+    TORCH_ILISTREF_UNWRAP(tag_, { return ImplT::front(this_); });
+  }
+
+  /*
+   * Materializes the `IListRef` into a `std::vector`.
+   *
+   * This should be used when one wishes to either:
+   *
+   *   - iterate over the list more than once: each `IListRefIterator`
+   *     member function call has to go through a switch, introducing
+   *     non-negligible overhead
+   *
+   *   - randomly access an arbitrary element using `operator[]`:
+   *     same reason as above
+   */
+  detail::MaterializedIListRef<T> materialize() const {
+    if (isMaterialized()) {
+      return toMaterialized();
+    }
+
+    detail::MaterializedIListRef<T> materialized;
+    materialized.reserve(size());
+    for (const auto& t : *this) {
+      materialized.emplace_back(t);
+    }
+    return materialized;
+  }
+
+#define DEFINE_CHECK(TAG, ...)    \
+  bool is##TAG() const {          \
+    return tag_ == IListRefTag::TAG; \
+  }
+  TORCH_ILISTREF_FORALL_TAGS(DEFINE_CHECK);
+#undef DEFINE_CHECK
+
+  bool isNone() const {
+    return tag_ == IListRefTag::None;
+  }
+
+#define DEFINE_CASTING(TAG, ...)                                          \
+  const typename detail::IListRefTagImpl<IListRefTag::TAG, T>::list_type& \
+      to##TAG() const {                                                   \
+    TORCH_INTERNAL_ASSERT(is##TAG());                                     \
+    return detail::IListRefTagImpl<IListRefTag::TAG, T>::unwrap(*this);   \
+  }
+  TORCH_ILISTREF_FORALL_TAGS(DEFINE_CASTING);
+#undef DEFINE_CASTING
+
+ private:
+  union Payload {
+    const boxed_type* boxed;
+    unboxed_type unboxed;
+    const materialized_type* materialized;
+    Payload() : boxed(nullptr) {}
+  };
+
+  Payload payload_;
+  IListRefTag tag_;
+};
+
+} // namespace c10
+
+#include <ATen/core/IListRef_inl.h>

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/LegacyTypeDispatch.h

@@ -0,0 +1,111 @@
+#pragma once
+
+// The legacy mechanism for dispatching operators in ATen is a Type
+// object, which is essentially a giant virtual dispatch table
+// for every operation we support dynamically dispatching over.
+//
+// This has been deprecated in favor of ATenDispatch, and in the future,
+// c10 dispatcher.
+// TODO: Clean up what remains here
+
+#include <c10/core/impl/LocalDispatchKeySet.h>
+
+namespace at {
+
+// A RAII, thread local (!) guard that will disable dispatch to variable
+// handler.
+//
+// NOTE [ Treating Variables as non-Variables in type dispatch ]
+//
+// What exactly does AutoDispatchBelowAutograd do?  The short answer is, it causes
+// dispatches on ATen functions to go to the non-variable implementation,
+// bypassing autograd handling (and also profiling and tracing).
+//
+// To understand why this guard exists, it's helpful to understand the history
+// behind how Variable was implemented.  Previously, Variables were implemented
+// as a wrapper on Tensors; so the act of processing a Variable involved
+// unwrapping the underlying Tensor, and then calling the underlying base
+// operation on /that/ operation
+//
+// However, after the Variable/Tensor merge, there is no concept of unwrapping
+// a tensor anymore.  If you just call the operation on the same variable
+// again inside your VariableType handler, you'll dispatch back to
+// VariableType, which is not what we want.
+//
+// The solution to the above problem is to add `at::AutoDispatchBelowAutograd`, which
+// when enabled will cause `legacyTensorType()` and `getType()` to always return
+// non-Variable type, even if the tensor being called on is a variable.
+
+/* Note [AutoDispatchBelowAutograd]
+ * AutoDispatchBelowAutograd is **INTERNAL ONLY** that it should be used
+ * for kernel implementations and customized C++ kernels.
+ * If you are looking for a guard to run workload in inference mode, please use
+ * c10::InferenceMode RAII which is user facing API.
+ * In the past AutoDispatchBelowAutograd(or its old version AutoNonVariableTypeMode)
+ * was used in the user code for inference-only workload, this was under risk of
+ * producing wrong results silently in some edge cases. For example:
+ * ```
+ *  torch::Tensor s = torch::ones({1, 2, 3}).set_requires_grad(true);
+ *  torch::Tensor out = s * s;
+ *  {
+ *    at::AutoDispatchBelowAutograd guard;
+ *    s.add_(1);  // Skips version bump on `s`.
+ *  }
+ *  // WRONG GRADIENT! s.grad() are now computed using `s` value after the
+ *  // inplace update.
+ *  out.backward(torch::ones_like(out));
+ * ```
+ * Users should use `c10::InferenceMode` here so that it'll properly throw an
+ * error saying "one of the variables needed for gradient computation has be modified."
+ */
+struct TORCH_API AutoDispatchBelowAutograd {
+  AutoDispatchBelowAutograd() :
+    autograd_guard_(c10::autograd_dispatch_keyset) {
+  }
+
+  // disable all autograd dispatch keys
+  c10::impl::ExcludeDispatchKeyGuard autograd_guard_;
+};
+
+// TODO: AutoNonVariableTypeMode should be removed in release 1.10.
+struct TORCH_API AutoNonVariableTypeMode {
+  AutoNonVariableTypeMode(bool enabled = true) :
+    autograd_guard_(c10::autograd_dispatch_keyset) {
+    TORCH_WARN_ONCE("AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. "
+        "For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, "
+        "If you are looking for a user facing API to enable running your inference-only "
+        "workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code "
+        "is under risk of producing silent wrong result in some edge cases. "
+        "See Note [AutoDispatchBelowAutograd] for more details.");
+    TORCH_INTERNAL_ASSERT(enabled);
+  }
+
+  // disable all autograd dispatch keys
+  c10::impl::ExcludeDispatchKeyGuard autograd_guard_;
+};
+
+struct TORCH_API AutoDispatchSkipFunctionalize {
+  AutoDispatchSkipFunctionalize() :
+    dispatch_key_guard_(c10::DispatchKeySet(c10::DispatchKey::Functionalize)) {
+  }
+  c10::impl::ExcludeDispatchKeyGuard dispatch_key_guard_;
+};
+
+/* Note [AutoDispatchBelowADInplaceOrView]
+ * AutoDispatchBelowADInplaceOrView is equivalent to AutoNonVariableTypeMode
+ * before we split inplace & view ops out of VariableType kernel.
+ * Note this guard is used in VariableType kernels for functional ops
+ * as well as ADInplaceOrView kernels for inplace/view ops to enforce the
+ * Invariant:
+ *   Once you are in VariableType/ADInplaceOrView kernel for an op,
+ *   you never go back to a kernel on same dispatch key until
+ *   you finish the current op.
+ */
+struct TORCH_API AutoDispatchBelowADInplaceOrView {
+  AutoDispatchBelowADInplaceOrView() :
+    dispatch_key_guard_(c10::autograd_dispatch_keyset_with_ADInplaceOrView) {
+  }
+  // disable Autograd & ADInplaceOrView dispatch keys
+  c10::impl::ExcludeDispatchKeyGuard dispatch_key_guard_;
+};
+} // namespace at

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/QuantizerBase.h

@@ -0,0 +1,83 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/core/QScheme.h>
+#include <c10/util/intrusive_ptr.h>
+
+namespace at {
+
+class Tensor;
+struct QTensorImpl;
+struct Quantizer;
+using ConstQuantizerPtr = const c10::intrusive_ptr<Quantizer>&;
+using QuantizerPtr = c10::intrusive_ptr<Quantizer>;
+
+/**
+ * Quantizer is the class for storing all the information
+ * that's necessary to perform quantize and dequantize
+ * operation.
+ *
+ * We might have different types of quantization schemes and this is
+ * the base class for all quantizers.
+ *
+ * QTensorImpl will hold a pointer to Quantizer so that we can support
+ * different quantization schemes on Tensor.
+ *
+ * For example, the most common quantization scheme, Affine Quantization,
+ * requires scale and zero_point as parameters, we'll store scale and zero_point
+ * inside the instance and we can use it to quantize a float Tensor or
+ * dequantize a quantized Tensor.
+ *
+ * When you add new types of leaf Quantizer class, please also
+ * make sure to add a corresponding QScheme enum since
+ * they should have one to one mapping.
+ *
+ * Note about intrusive_ptr:
+ * Quantized Tensor holds an intrusive_ptr to Quantizer, and multiple Tensor can
+ * share the same Quantizer. Quantizer should be immutable.
+ */
+struct TORCH_API Quantizer : public c10::intrusive_ptr_target {
+  const ScalarType scalar_type_;
+  explicit Quantizer(ScalarType scalar_type) : scalar_type_(scalar_type) {}
+  ~Quantizer() override;
+
+  // Copied from torch/csrc/jit/ir/scope.h
+  QuantizerPtr intrusive_from_this() {
+    c10::raw::intrusive_ptr::incref(this); // we are creating a new pointer
+                                           // from a raw `this` pointer
+                                           // so we need to bump the refcount
+                                           // to account for this ownership
+    return c10::intrusive_ptr<Quantizer>::reclaim(this);
+  }
+
+  /**
+   * Each concrete Quantizer type should have a unique QScheme type.
+   */
+  virtual QScheme qscheme() const = 0;
+
+  ScalarType scalar_type() const {
+    return scalar_type_;
+  }
+
+  /**
+   * quantize a float Tensor into a quantized Tensor.
+   */
+  virtual Tensor quantize(const Tensor& t) = 0;
+
+  /**
+   * dequantize a quantized Tensor into a float Tensor.
+   */
+  virtual Tensor dequantize(const Tensor& t) = 0;
+
+  /**
+   * dequantize a quantized Tensor into a float Tensor, out= variant
+   */
+  virtual Tensor& dequantize_out(Tensor& out, const Tensor& t) = 0;
+
+  /**
+   * Compare against `other` for equality.
+   */
+  virtual bool equalTo(QuantizerPtr other) const = 0;
+};
+
+} // namespace at

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/VariableHooksInterface.h

@@ -0,0 +1,75 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <ATen/core/Tensor.h>
+
+// A little explanation about why this file exists at all.  We have
+// a few methods on Tensor class which require access to reified access to
+// AutogradMeta.  In open source, this isn't a big deal: we just access
+// torch/csrc/autograd/variable.h from aten/src/ATen/core/Tensor.cpp and
+// we can put the definitions inline.  This is because everything gets balled
+// into a single dynamic library in the end.
+//
+// However, inside our Facebook internal version of our build system, we
+// have a split between aten and torch/csrc.  So we cannot simply just
+// cross this boundary.  "Now wait," you might say, "Why don't we just
+// merge the libraries inside Facebook".  Well, the problem is that there
+// are some downstream applications which are at binary size limit, and
+// incorporating all of the extra code from libtorch would push them
+// over (admarket/adreview/service:adreviewservice, see also
+// https://github.com/pytorch/pytorch/pull/29299)  So if you want to do that,
+// we have to fix all of the services like this.
+//
+// I didn't want to block eliminating Tensor-Variable on this work, so I
+// had to introduce another dynamic dispatch to get to the variable
+// implementations (which live in torch/csrc/autograd/variable.cpp, FYI).
+//
+// I also considered using our existing dynamic dispatch mechanism, c10
+// dispatcher, to do this.  However, (1) some of the functions on Tensor
+// have weird signatures that are not supported by autograd, and (2)
+// see this bug https://github.com/pytorch/pytorch/issues/30102
+
+namespace torch { namespace autograd {
+
+struct Node;
+
+}} // namespace torch::autograd
+
+namespace at {
+namespace impl {
+
+struct TORCH_API VariableHooksInterface {
+  virtual ~VariableHooksInterface() = default;
+  virtual TensorBase tensor_data(const TensorBase&) const = 0;
+  virtual TensorBase variable_data(const TensorBase&) const = 0;
+  virtual const std::shared_ptr<torch::autograd::Node>& grad_fn(const TensorBase&) const = 0;
+  virtual unsigned _register_hook(
+      const TensorBase&,
+      std::function<TensorBase(const TensorBase&)> hook) const = 0;
+  virtual void remove_hook(const TensorBase&, unsigned pos) const = 0;
+  virtual bool is_view(const TensorBase&) const = 0;
+  virtual const TensorBase& base(const TensorBase&) const = 0;
+  virtual const std::string& name(const TensorBase&) const = 0;
+  virtual bool is_leaf(const TensorBase&) const = 0;
+  virtual int64_t output_nr(const TensorBase&) const = 0;
+  virtual void set_data(const TensorBase&, const TensorBase&) const = 0;
+  virtual TensorBase data(const TensorBase&) const = 0;
+  virtual int64_t _version(const TensorBase&) const = 0;
+  virtual void retain_grad(const TensorBase&) const = 0;
+  virtual bool retains_grad(const TensorBase&) const = 0;
+  virtual void _backward(const Tensor&, TensorList, const c10::optional<Tensor>&, c10::optional<bool>, bool) const = 0;
+  virtual void requires_grad_(const TensorBase&, bool) const = 0;
+  virtual void basic_autograd_not_implemented_fallback(const c10::OperatorHandle& op, c10::DispatchKeySet dispatch_keys, torch::jit::Stack* stack) const = 0;
+};
+
+TORCH_API void SetVariableHooks(VariableHooksInterface* hooks);
+TORCH_API VariableHooksInterface* GetVariableHooks();
+TORCH_API bool HasVariableHooks();
+
+struct TORCH_API VariableHooksRegisterer {
+  explicit VariableHooksRegisterer(VariableHooksInterface* hooks) {
+    SetVariableHooks(hooks);
+  }
+};
+
+}} // namespace at::impl

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/dynamic_type.h

@@ -0,0 +1,239 @@
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <type_traits>
+
+#include <ATen/core/jit_type_base.h>
+#include <c10/util/Optional.h>
+
+namespace c10 {
+
+using DynamicTypeBits = std::uint32_t;
+#define DYNAMIC_TYPE_BIT(x) (1u << x)
+
+constexpr DynamicTypeBits kDynamicCovariantTypeBit = DYNAMIC_TYPE_BIT(31);
+constexpr DynamicTypeBits kDynamicAnyTypeBit = DYNAMIC_TYPE_BIT(30);
+
+constexpr DynamicTypeBits kDynamicNoneTypeBit = DYNAMIC_TYPE_BIT(1);
+constexpr DynamicTypeBits kDynamicIntTypeBit = DYNAMIC_TYPE_BIT(3);
+constexpr DynamicTypeBits kDynamicFloatTypeBit = DYNAMIC_TYPE_BIT(4);
+constexpr DynamicTypeBits kDynamicComplexTypeBit = DYNAMIC_TYPE_BIT(5);
+constexpr DynamicTypeBits kDynamicListTypeBit = DYNAMIC_TYPE_BIT(7);
+constexpr DynamicTypeBits kDynamicTupleTypeBit = DYNAMIC_TYPE_BIT(8);
+constexpr DynamicTypeBits kDynamicClassTypeBit = DYNAMIC_TYPE_BIT(10);
+
+#define FORALL_DYNAMIC_TYPES(_)                                              \
+  _(Tensor, DYNAMIC_TYPE_BIT(0), 1)                                          \
+  _(None, kDynamicNoneTypeBit, 1)                                            \
+  _(Bool, DYNAMIC_TYPE_BIT(2), 1)                                            \
+  _(Int, kDynamicIntTypeBit, 1)                                              \
+  _(Float, kDynamicFloatTypeBit, 1)                                          \
+  _(Complex, kDynamicComplexTypeBit, 1)                                      \
+  _(Number,                                                                  \
+    (kDynamicIntTypeBit | kDynamicFloatTypeBit | kDynamicComplexTypeBit),    \
+    1)                                                                       \
+  _(String, DYNAMIC_TYPE_BIT(6), 1)                                          \
+  _(List, kDynamicListTypeBit, 0)                                            \
+  _(Tuple, (kDynamicTupleTypeBit | kDynamicCovariantTypeBit), 0)             \
+  _(Dict, DYNAMIC_TYPE_BIT(9), 0)                                            \
+  _(Class, kDynamicClassTypeBit, 0)                                          \
+  _(Optional,                                                                \
+    (DYNAMIC_TYPE_BIT(11) | kDynamicNoneTypeBit | kDynamicCovariantTypeBit), \
+    0)                                                                       \
+  _(AnyList, (kDynamicListTypeBit | kDynamicAnyTypeBit), 1)                  \
+  _(AnyTuple,                                                                \
+    (kDynamicTupleTypeBit | kDynamicCovariantTypeBit | kDynamicAnyTypeBit),  \
+    1)                                                                       \
+  _(DeviceObj, DYNAMIC_TYPE_BIT(12), 1)                                      \
+  _(StreamObj, DYNAMIC_TYPE_BIT(13), 1)                                      \
+  _(Capsule, DYNAMIC_TYPE_BIT(14), 1)                                        \
+  _(Generator, DYNAMIC_TYPE_BIT(15), 1)                                      \
+  _(Storage, DYNAMIC_TYPE_BIT(16), 1)                                        \
+  _(Var, DYNAMIC_TYPE_BIT(17), 0)                                            \
+  _(AnyClass, (kDynamicClassTypeBit | kDynamicAnyTypeBit), 1)                \
+  _(QScheme, DYNAMIC_TYPE_BIT(18), 1)                                        \
+  _(Quantizer, DYNAMIC_TYPE_BIT(19), 1)                                      \
+  _(AnyEnum, DYNAMIC_TYPE_BIT(20), 1)                                        \
+  _(RRef, DYNAMIC_TYPE_BIT(21), 0)                                           \
+  _(Future, DYNAMIC_TYPE_BIT(22), 0)                                         \
+  _(Await, DYNAMIC_TYPE_BIT(23), 0)                                          \
+  _(Any, 0xffffffff, 1)
+
+#define FORALL_DYNAMIC_TYPES_FAKE(_) \
+  _(ScalarType, kDynamicIntTypeBit, 1)                                \
+  _(Layout, kDynamicIntTypeBit, 1)                                        \
+  _(SymInt, kDynamicIntTypeBit, 1)                                        \
+  _(MemoryFormat, kDynamicIntTypeBit, 1)
+
+#define FORWARD_DECL_TYPE(NAME, _, __) struct NAME ## Type;
+  FORALL_DYNAMIC_TYPES(FORWARD_DECL_TYPE)
+  FORALL_DYNAMIC_TYPES_FAKE(FORWARD_DECL_TYPE)
+#undef FORWARD_DECL_TYPE
+
+class DynamicType;
+using DynamicTypePtr = std::shared_ptr<DynamicType>;
+
+/**
+ * DynamicType is designed as a low dependency type system for TorchScript. The
+ * existing JIT types are used for both compilation and runtime, which makes
+ * sense for server contexts because we often compile and run the model in
+ * the same process, however this doesn't hold for mobile devices where we
+ * always compiles a model ahead of time, therefore there will be dependencies
+ * which are not needed, but built with mobile runtime causing binary size
+ * bloat, by design. Every basic type like Int, Bool or String will bring their
+ * vtable, typeinfo, constructor, destructor and even more data from their
+ * specializations for STL types to the binary causing a long tail bloat.
+ *
+ * The core problem is about the complexity to implement and maintain a single
+ * type system for both analysis and execution purposes. Although they should
+ * have the exactly same semantics, in practice implement a unified abstraction
+ * adds conceptual and representational overhead for both sides of the world.
+ *
+ * To address the issues, DynamicType implements a minimal subset of JIT types
+ * and uses a generic algorithm to test all subtyping relations. To achieve
+ * this, we assign each dynamic type a single integer tag to represent its
+ * semantics. More specifically, a dynamic type is defined as a set of "control
+ * bits" and "data bits", where control bits describe the special behavior when
+ * testing a type and data bits map to identity of each nominal type. We use bit
+ * operations to perform all the tests.
+ *
+ * For example, a "covariant bit" is a control bit used to describe if a type
+ * is covariant, right now the most used one is tuple type, and in addition to
+ * the control bit, tuple type's data bit is the 8th bit from the LSB. Control
+ * bits start from MSB and data bits start from LSB.
+ *
+ * If two types are equal, then they are subtype of each other, also if the bits
+ * from one type tag is subset of the other tag, it automatically becomes a
+ * subtype of the other. This simplifies the subtyping logic a lot, and over the
+ * long term it is possible to adopt this scheme on the server side as well.
+ * Special cases can be added but they generally should not take too much code
+ * size.
+ *
+ * DynamicType may or may not inherit from c10::Type because it's not the core
+ * requirement of DynamicType to interface with existing JIT types, but we might
+ * want to inherit from c10::Type to reduce the migration cost.
+ */
+class DynamicType : public SharedType {
+  using ClassTypePtr = std::shared_ptr<const c10::ClassType>;
+
+  /**
+   * A implementation detail to support NamedTuple.
+   */
+  struct LabeledDynamicType {
+    c10::optional<std::string> label;
+    DynamicTypePtr ty;
+    explicit LabeledDynamicType(DynamicTypePtr t) : ty(std::move(t)) {}
+
+    bool equals(const LabeledDynamicType& other) const;
+    bool isSubtypeOf(const LabeledDynamicType& other) const;
+  };
+
+ public:
+  // TODO Change Ptr to DynamicTypePtr when all migrations are done.
+  using Ptr = TypePtr;
+  using ElementType = DynamicType;
+  ~DynamicType() override;
+
+  struct Arguments {
+    Arguments() = default;
+    Arguments(c10::ArrayRef<TypePtr>);
+    Arguments(const std::vector<c10::string_view>&, c10::ArrayRef<TypePtr>);
+    std::vector<LabeledDynamicType> elems;
+  };
+
+  enum class Tag : DynamicTypeBits {
+#define DYNAMIC_TYPE_ITEM(NAME, VAL, _) NAME = VAL,
+    FORALL_DYNAMIC_TYPES(DYNAMIC_TYPE_ITEM)
+    FORALL_DYNAMIC_TYPES_FAKE(DYNAMIC_TYPE_ITEM)
+#undef DYNAMIC_TYPE_ITEM
+  };
+
+  bool equals(const Type& rhs) const override;
+  bool isSubtypeOfExt(const Type& rhs, std::ostream* why_not) const override;
+  std::string str() const override;
+  static const TypeKind Kind = TypeKind::DynamicType;
+  static TORCH_API DynamicTypePtr create(Type& ty);
+
+  explicit DynamicType(Tag, Arguments);
+  explicit DynamicType(Tag, c10::string_view, Arguments);
+
+  TypePtr containedType(size_t) const override;
+  size_t containedTypeSize() const override;
+  Tag tag() const {
+    return tag_;
+  }
+  const c10::optional<std::string>& name() const {
+    return name_;
+  }
+  const Arguments& arguments() const {
+    return arguments_;
+  }
+  TORCH_API TypeKind dynamicKind() const;
+
+  // Should be used only on the server side to restore static type information.
+#ifndef C10_MOBILE
+  TORCH_API
+#endif
+  TypePtr fallback() const;
+
+ private:
+  bool symmetric() const override {
+    return false;
+  }
+  friend struct Type;
+  static std::shared_ptr<const DynamicType> create(const Type& ty);
+  DynamicType(const Type& other);
+  bool equals(const DynamicType& other) const;
+
+  template <typename F>
+  bool compareArguments(const DynamicType& other, F&& f) const {
+    if (arguments_.elems.size() != other.arguments_.elems.size()) {
+      return false;
+    }
+    for (size_t i = 0; i < arguments_.elems.size(); i++) {
+      if (!f(arguments_.elems[i], other.arguments_.elems[i])) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  Tag tag_;
+  c10::optional<std::string> name_;
+  union {
+    Arguments arguments_;
+    ClassTypePtr class_;
+  };
+};
+
+template <typename T>
+struct DynamicTypeTrait {
+  C10_NOINLINE static auto tagValue() {
+    TORCH_CHECK(false);
+    return DynamicType::Tag::Any;
+  }
+};
+
+namespace detail {
+C10_NOINLINE DynamicTypePtr makeBaseType(DynamicType::Tag tag);
+}
+
+#define DYNAMIC_TYPE_TAG_VALUE(NAME, _, IS_BASE_TYPE)      \
+  template <>                                              \
+  struct TORCH_API DynamicTypeTrait<NAME##Type> {          \
+    C10_ERASE static auto tagValue() {                     \
+      return DynamicType::Tag::NAME;                       \
+    }                                                      \
+    static constexpr bool isBaseType = IS_BASE_TYPE;       \
+    template <typename T = const DynamicTypePtr&>          \
+    static std::enable_if_t<isBaseType, T> getBaseType() { \
+      static auto type = detail::makeBaseType(tagValue()); \
+      return type;                                         \
+    }                                                      \
+  }; // namespace c10
+FORALL_DYNAMIC_TYPES(DYNAMIC_TYPE_TAG_VALUE)
+FORALL_DYNAMIC_TYPES_FAKE(DYNAMIC_TYPE_TAG_VALUE)
+#undef DYNAMIC_TYPE_TAG_VALUE
+
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/jit_type_base.h

@@ -0,0 +1,719 @@
+#pragma once
+
+#include <functional>
+#include <memory>
+#include <string>
+#include <utility>
+
+#include <ATen/core/qualified_name.h>
+#include <ATen/core/type_ptr.h>
+#include <c10/core/SymInt.h>
+#include <c10/core/SymFloat.h>
+#include <c10/core/SymBool.h>
+#include <c10/core/SymIntArrayRef.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+
+namespace c10 {
+
+#define C10_FORALL_TYPES(_) \
+  _(AnyType)                \
+  _(EnumType)               \
+  _(AnyEnumType)            \
+  _(TensorType)             \
+  _(StorageType)            \
+  _(TupleType)              \
+  _(ListType)               \
+  _(DictType)               \
+  _(NumberType)             \
+  _(FloatType)              \
+  _(ComplexType)            \
+  _(FutureType)             \
+  _(AwaitType)              \
+  _(RRefType)               \
+  _(IntType)                \
+  _(NoneType)               \
+  _(StringType)             \
+  _(GeneratorType)          \
+  _(QuantizerType)          \
+  _(BoolType)               \
+  _(OptionalType)           \
+  _(VarType)                \
+  _(DeviceObjType)          \
+  _(StreamObjType)          \
+  _(FunctionType)           \
+  _(ClassType)              \
+  _(PyObjectType)           \
+  _(CapsuleType)            \
+  _(InterfaceType)          \
+  _(QSchemeType)            \
+  _(ScalarTypeType)         \
+  _(LayoutType)             \
+  _(MemoryFormatType)       \
+  _(AnyListType)            \
+  _(AnyTupleType)           \
+  _(AnyClassType)           \
+  _(SymIntType)             \
+  _(SymFloatType)           \
+  _(SymBoolType)            \
+  _(UnionType)              \
+  _(DynamicType)
+
+enum class TypeKind {
+#define DEFINE_TYPE(T) T,
+  C10_FORALL_TYPES(DEFINE_TYPE)
+#undef DEFINE_TYPE
+};
+
+TORCH_API const char* typeKindToString(TypeKind kind);
+
+struct Type;
+struct SharedType;
+
+// Use this to customize how a Type is printed using `annotation_str()`. If
+// c10::nullopt is returned, `annotation_str()` falls through to its default
+// implementation.
+using TypePrinter = std::function<c10::optional<std::string>(const Type&)>;
+
+namespace detail {
+template <typename T>
+struct IsSingletonType : public std::integral_constant<bool, false> {};
+} // namespace detail
+#define TORCH_DECLARE_SINGLETON(Type) \
+  struct Type;                                                          \
+  namespace detail { \
+  template <> struct IsSingletonType<Type> : public std::integral_constant<bool, true> {}; \
+  }
+
+TORCH_DECLARE_SINGLETON(AnyType);
+TORCH_DECLARE_SINGLETON(AnyEnumType);
+TORCH_DECLARE_SINGLETON(NumberType);
+TORCH_DECLARE_SINGLETON(FloatType);
+TORCH_DECLARE_SINGLETON(ComplexType);
+TORCH_DECLARE_SINGLETON(IntType);
+TORCH_DECLARE_SINGLETON(BoolType);
+TORCH_DECLARE_SINGLETON(StringType);
+TORCH_DECLARE_SINGLETON(StorageType);
+TORCH_DECLARE_SINGLETON(NoneType);
+TORCH_DECLARE_SINGLETON(GeneratorType);
+TORCH_DECLARE_SINGLETON(QuantizerType);
+TORCH_DECLARE_SINGLETON(QSchemeType);
+TORCH_DECLARE_SINGLETON(DeviceObjType);
+TORCH_DECLARE_SINGLETON(StreamObjType);
+TORCH_DECLARE_SINGLETON(CapsuleType);
+TORCH_DECLARE_SINGLETON(PyObjectType);
+TORCH_DECLARE_SINGLETON(ScalarTypeType);
+TORCH_DECLARE_SINGLETON(LayoutType);
+TORCH_DECLARE_SINGLETON(MemoryFormatType);
+TORCH_DECLARE_SINGLETON(AnyListType);
+TORCH_DECLARE_SINGLETON(AnyTupleType);
+TORCH_DECLARE_SINGLETON(AnyClassType);
+
+namespace detail {
+template <typename T, typename Enable = void>
+struct CastReturnType {
+  using type = std::shared_ptr<T>;
+};
+
+template <typename T>
+struct CastReturnType<T, typename std::enable_if<IsSingletonType<T>::value>::type> {
+  using type = SingletonTypePtr<T>;
+};
+
+template <typename T, typename Enable = void>
+struct CastConstReturnType {
+  using type = std::shared_ptr<const T>;
+};
+
+template <typename T>
+struct CastConstReturnType<T, typename std::enable_if<IsSingletonType<T>::value>::type> {
+  using type = SingletonTypePtr<const T>;
+};
+
+template <typename T>
+struct as_shared_type {
+  using type = SharedType*;
+};
+
+template <typename T>
+struct as_shared_type<const T*> {
+  using type = const SharedType *;
+};
+} // namespace detail
+
+struct TORCH_API Type {
+  friend TORCH_API bool operator==(const Type& lhs, const Type& rhs);
+  private:
+  TypeKind kind_;
+
+  protected:
+  Type(TypeKind kind) : kind_(kind) {}
+
+  Type(const Type&) = default;
+  Type& operator=(const Type&) = default;
+  Type(Type&&) noexcept = default;
+  Type& operator=(Type&&) noexcept = default;
+
+  virtual std::string annotation_str_impl(TypePrinter /*printer*/) const {
+    return str();
+  }
+  // a == b
+  virtual bool equals(const Type& rhs) const = 0;
+  // a == b <=> b == a
+  virtual bool symmetric() const {
+    return true;
+  }
+
+ public:
+  template <typename T>
+  class SingletonOrSharedTypePtr {
+   public:
+    using element_type = typename std::shared_ptr<T>::element_type;
+
+    SingletonOrSharedTypePtr() = default;
+
+    /* implicit */ SingletonOrSharedTypePtr(std::shared_ptr<T> x)
+        : repr_(std::move(x)) {}
+
+    template <typename U, std::enable_if_t<std::is_convertible<U*, T*>::value, bool> = true>
+    /* implicit */ SingletonOrSharedTypePtr(std::shared_ptr<U> x)
+        : repr_(std::move(x)) {}
+
+    /* implicit */ SingletonOrSharedTypePtr(std::nullptr_t)
+        : repr_(nullptr) {}
+
+    /* implicit */ SingletonOrSharedTypePtr(SingletonTypePtr<T> p)
+        : repr_(p) {}
+
+    template <typename U, std::enable_if_t<std::is_convertible<U*, T*>::value, bool> = true>
+    /* implicit */ SingletonOrSharedTypePtr(SingletonTypePtr<U> p)
+        : repr_(SingletonTypePtr<T>(p.get())) {}
+
+
+    // We need to support construction from T* for pybind. The problem
+    // is that it's not clear if we are supposed to be taking shared
+    // ownership or not.
+    //
+    // Case 1: if T is known statically to derive from SharedType, we should use
+    // shared_from_this() and take shared_ownership.
+    //
+    // Case 2: if T is exactly Type, we need to do a dynamic_cast to
+    // check if it's a SharedType and do the right thing.
+    //
+    // Case 3: Otherwise, T is not a SharedType. (debug-check this
+    // assumption!) Use a singleton pointer.
+
+    template <typename U = T, std::enable_if_t<std::is_base_of<SharedType, U>::value, bool> = true>
+    /* implicit */ SingletonOrSharedTypePtr(T* p) : SingletonOrSharedTypePtr(static_cast<typename detail::as_shared_type<U>::type>(p)->shared_from_this()) {}
+
+    template <typename U = T, std::enable_if_t<std::is_same<Type, U>::value, bool> = true>
+    /* implicit */ SingletonOrSharedTypePtr(T* p) {
+      if (auto* shared_p = dynamic_cast<typename detail::as_shared_type<U>::type>(p)) {
+        repr_ = Repr(shared_p->shared_from_this());
+      } else {
+        repr_ = Repr(p);
+      }
+    }
+
+    template <typename U = T, std::enable_if_t<!std::is_same<Type, U>::value && !std::is_base_of<SharedType, U>::value, bool> = true>
+    /* implicit */ SingletonOrSharedTypePtr(T* p)
+        : repr_(p) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(dynamic_cast<typename detail::as_shared_type<U>::type>(p) == nullptr);
+    }
+
+    SingletonOrSharedTypePtr(const SingletonOrSharedTypePtr&) = default;
+    SingletonOrSharedTypePtr(SingletonOrSharedTypePtr&&) noexcept = default;
+    SingletonOrSharedTypePtr& operator=(const SingletonOrSharedTypePtr&) = default;
+    SingletonOrSharedTypePtr& operator=(SingletonOrSharedTypePtr&&) noexcept = default;
+
+    T* get() const {
+      return repr_.isSharedAndNonNull() ? repr_.shared_.repr_.get() : static_cast<T*>(repr_.rawRepr().first);
+    }
+
+    operator bool() const {
+      return repr_.isNonNull();
+    }
+
+    bool operator==(std::nullptr_t) const {
+      return !repr_.isNonNull();
+    }
+
+    bool operator!=(std::nullptr_t) const {
+      return repr_.isNonNull();
+    }
+
+    template <typename U = T, std::enable_if_t<!std::is_same<std::remove_const_t<U>, void>::value, bool> = true>
+    U& operator*() const {
+      return *get();
+    }
+
+    T* operator->() const {
+      return get();
+    }
+
+  private:
+    // NOTE: SharedPtrWrapper exists to work around a baffling bug in
+    // nvcc; see comment in destroy() below.
+    struct SharedPtrWrapper {
+      SharedPtrWrapper(std::shared_ptr<T> &&x)
+          : repr_(std::move(x)) {}
+      std::shared_ptr<T> repr_;
+    };
+    union Repr {
+      Repr() : Repr(nullptr) {}
+
+      explicit Repr(std::shared_ptr<T> x)
+          : shared_(std::move(x)) {}
+
+      explicit Repr(std::nullptr_t)
+          : singletonRepr_(nullptr) {}
+
+      explicit Repr(SingletonTypePtr<T> p)
+          : singletonRepr_(p.get()) {}
+
+      ~Repr() {
+        destroy();
+      }
+
+      // NOTE: the only non-UB way to access our null state is through
+      // rawRepr(), because our copy operation doesn't preserve which
+      // union member is active for null pointers.
+      Repr(const Repr& rhs) {
+        if (rhs.isSharedAndNonNull()) {
+          new (&shared_) SharedPtrWrapper(rhs.shared_);
+        } else {
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.singletonRepr_.unused_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+      }
+
+      Repr(Repr&& rhs) noexcept {
+        if (rhs.isSharedAndNonNull()) {
+          new (&shared_) SharedPtrWrapper(std::move(rhs.shared_));
+        } else {
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.singletonRepr_.unused_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+      }
+
+      Repr& operator=(const Repr& rhs) {
+        if (&rhs == this) {
+          return *this;
+        }
+        if (rhs.isSharedAndNonNull()) {
+          if (isSharedAndNonNull()) {
+            shared_ = rhs.shared_;
+          } else {
+            new (&shared_) SharedPtrWrapper(rhs.shared_);
+          }
+        } else {
+          if (isSharedAndNonNull()) {
+            destroy();
+          }
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.rawRepr().nullIfSingleton_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+        return *this;
+      }
+
+      Repr& operator=(Repr&& rhs) noexcept {
+        if (&rhs == this) {
+          return *this;
+        }
+        if (rhs.isSharedAndNonNull()) {
+          if (isSharedAndNonNull()) {
+            shared_ = std::move(rhs.shared_);
+          } else {
+            new (&shared_) SharedPtrWrapper(std::move(rhs.shared_));
+          }
+        } else {
+          if (isSharedAndNonNull()) {
+            destroy();
+          }
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.rawRepr().nullIfSingleton_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+        return *this;
+      }
+
+      SharedPtrWrapper shared_;
+
+      struct SingletonRepr {
+        explicit SingletonRepr(T* s) : singleton_(s) {}
+        T* singleton_;
+        void* unused_ = nullptr;
+      } singletonRepr_;
+      struct RawRepr {
+        void* first;
+        void* nullIfSingleton_;
+      };
+
+      // It is UB to read the singleton part of Repr if it was
+      // constructed as a shared_ptr and vice versa, but memcpying out
+      // the representation is always OK, so here's an accessor to obey
+      // the letter of the law.
+      RawRepr rawRepr() const {
+        RawRepr repr{};
+        memcpy(&repr, reinterpret_cast<const char *>(this), sizeof(RawRepr));
+        return repr;
+      }
+
+      bool isNonNull() const {
+        auto repr = rawRepr();
+        TORCH_INTERNAL_ASSERT_DEBUG_ONLY(repr.nullIfSingleton_ == nullptr || repr.first != nullptr);
+        return repr.first != nullptr;
+      }
+
+      bool isSharedAndNonNull() const {
+        return rawRepr().nullIfSingleton_ != nullptr;
+      }
+
+     private:
+      void destroy() {
+        if (isSharedAndNonNull()) {
+          // Without SharedPtrWrapper, this line would read
+          // `shared_.~shared_ptr()` and nvcc would complain with
+          // "error: expected primary-expression before '>' token"
+          // referring to the "t" in "shared_ptr". SharedPtrWrapper
+          // exists to work around this compiler bug.
+          shared_.~SharedPtrWrapper();
+        }
+      }
+    } repr_;
+  };
+
+  using TypePtr = SingletonOrSharedTypePtr<Type>;
+  using Ptr = TypePtr;
+  using ElementType = Type;
+
+  // subtyping relation. By default, we return true for the case
+  // when the type is exactly equal or if this <: T where rhs = Optional[T]
+
+  // if this returns false and the why_not stream is non-null, it contains
+  // additional details that describe why this is not a subtype of 'rhs'.
+  // This additional information should only contain details that are not
+  // obvious from the annotation_str() that describes the type. For instance it
+  // is clear that `int <: str` is false but not clear why `Foo <: InterfaceBar`
+  // might be false.
+  virtual bool isSubtypeOfExt(const Type& rhs, std::ostream* why_not) const;
+  virtual bool is_module() const;
+  bool isSubtypeOf(const Type& rhs) const {
+    return isSubtypeOfExt(rhs, nullptr);
+  }
+  // Compatibility shims to accommodate existing code that passes shared_ptrs
+  // around. Ideally, we would just delete this, but it should be harmless.
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOf(const std::shared_ptr<T>& rhs) const {
+    return isSubtypeOf(*rhs);
+  }
+
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOf(const SingletonOrSharedTypePtr<T>& rhs) const {
+    return isSubtypeOf(*rhs);
+  }
+
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOf(SingletonTypePtr<T> rhs) const {
+    return isSubtypeOf(*rhs);
+  }
+
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOfExt(const SingletonOrSharedTypePtr<T>& rhs, std::ostream* why_not) const {
+    return isSubtypeOfExt(*rhs, why_not);
+  }
+
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOfExt(const std::shared_ptr<T>& rhs, std::ostream* why_not) const {
+    return isSubtypeOfExt(*rhs, why_not);
+  }
+
+  template <typename T>
+  typename std::enable_if<std::is_base_of<Type, T>::value, bool>::type
+  isSubtypeOfExt(SingletonTypePtr<T> rhs, std::ostream* why_not) const {
+    return isSubtypeOfExt(*rhs, why_not);
+  }
+
+  // How this type will appear in FunctionSchema declarations
+  virtual std::string str() const = 0;
+
+  // How this type will appear as if it were a type annotation in Python
+  // which is sometimes different than how it appears in declarations (e.g.
+  // int[] vs List[int])
+  //
+  // Takes a custom printer that users can pass in to customize the output of
+  // this method.
+  std::string annotation_str(TypePrinter printer) const {
+    if (printer) {
+      // the printer can return nullopt to fall through to the default impl
+      if (auto renamed = printer(*this)) {
+        return *renamed;
+      }
+    }
+    return annotation_str_impl(std::move(printer));
+  }
+  std::string annotation_str() const {
+    // Overload instead of define a default value for `printer` to help
+    // debuggers out.
+    return annotation_str(nullptr);
+  }
+
+  // Returns a human readable string that includes additional information like
+  // "type is inferred rather than explicitly defined" to help construct more
+  // user-friendly messages.
+  virtual std::string repr_str() const {
+    return annotation_str();
+  }
+
+  TypeKind kind() const {
+    return kind_;
+  }
+
+  virtual bool isUnionType() const {
+    return false;
+  }
+
+  virtual bool requires_grad() const {
+    for (const auto& ct : containedTypes()) {
+      if (ct->requires_grad()) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  // Dynamically cast this object to the subclass indicated by the
+  // template variable, returning nullptr if the cast is invalid.
+  template <typename T, std::enable_if_t<!detail::IsSingletonType<T>::value, bool> = true>
+  typename detail::CastReturnType<T>::type cast() {
+    if (T::Kind == kind()) {
+      return std::static_pointer_cast<T>(static_cast<T*>(this)->shared_from_this());
+    }
+    return nullptr;
+  }
+  template <typename T, std::enable_if_t<detail::IsSingletonType<T>::value, bool> = true>
+  typename detail::CastReturnType<T>::type cast() {
+    if (T::Kind == kind()) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(this == T::get().get());
+      return typename detail::CastReturnType<T>::type(static_cast<T*>(this));
+    }
+    return nullptr;
+  }
+  template <typename T, std::enable_if_t<!detail::IsSingletonType<T>::value, bool> = true>
+  typename detail::CastConstReturnType<T>::type cast() const {
+    if (T::Kind == kind()) {
+      return std::static_pointer_cast<const T>(static_cast<const T*>(this)->shared_from_this());
+    }
+    return nullptr;
+  }
+  template <typename T, std::enable_if_t<detail::IsSingletonType<T>::value, bool> = true>
+  typename detail::CastConstReturnType<T>::type cast() const {
+    if (T::Kind == kind()) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(this == T::get().get());
+      return typename detail::CastConstReturnType<T>::type(static_cast<const T*>(this));
+    }
+    return nullptr;
+  }
+  template <typename T>
+  T* castRaw() {
+    if (T::Kind == kind()) {
+      return static_cast<T*>(this);
+    }
+    return nullptr;
+  }
+  template <typename T>
+  const T* castRaw() const {
+    if (T::Kind == kind()) {
+      return static_cast<const T*>(this);
+    }
+    return nullptr;
+  }
+  template <typename T>
+  auto expect() {
+    auto r = cast<T>();
+    AT_ASSERT(r);
+    return r;
+  }
+  template <typename T>
+  auto expect() const {
+    auto r = cast<const T>();
+    AT_ASSERT(r);
+    return r;
+  }
+  template <typename T>
+  T& expectRef() {
+    auto* r = castRaw<T>();
+    AT_ASSERT(r);
+    return *r;
+  }
+  template <typename T>
+  const T& expectRef() const {
+    auto* r = castRaw<const T>();
+    AT_ASSERT(r);
+    return *r;
+  }
+  virtual ~Type() = default;
+  virtual bool hasFreeVariables() const {
+    return false;
+  }
+  // list of types this type contains, e.g. for a List then element type of a
+  // list for a tuple, the types of the tuple elements
+  virtual at::ArrayRef<TypePtr> containedTypes() const {
+    return {};
+  }
+  virtual TypePtr containedType(size_t i) const {
+    return containedTypes().at(i);
+  }
+  virtual size_t containedTypeSize() const {
+    return containedTypes().size();
+  }
+  // create a new version of this type, replacing its contained types with
+  // contained_types
+  TypePtr withContained(std::vector<TypePtr> contained_types);
+  // per-type constructor, you only need to override this if the
+  // containedTypes() is not empty
+  virtual TypePtr createWithContained(
+      std::vector<TypePtr> /*contained_types*/) const {
+    AT_ERROR(
+        "type with contained types did not overload createWithContained: ",
+        str());
+  }
+
+};
+
+template <typename T>
+using SingletonOrSharedTypePtr = Type::SingletonOrSharedTypePtr<T>;
+
+
+template <typename T, typename U>
+bool operator==(const SingletonOrSharedTypePtr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return (void*)x.get() == (void*)y.get();
+}
+
+template <typename T, typename U>
+bool operator==(const SingletonOrSharedTypePtr<T>& x, const std::shared_ptr<U>& y) {
+  return (void*)x.get() == (void*)y.get();
+}
+
+template <typename T, typename U>
+bool operator==(const std::shared_ptr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return (void*)x.get() == (void*)y.get();
+}
+
+template <typename T, typename U>
+bool operator==(const SingletonOrSharedTypePtr<T>& x, const SingletonTypePtr<U>& y) {
+  return (void*)x.get() == (void*)y.get();
+}
+
+template <typename T, typename U>
+bool operator==(const SingletonTypePtr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return (void*)x.get() == (void*)y.get();
+}
+
+template <typename T, typename U>
+bool operator!=(const SingletonOrSharedTypePtr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return !(x == y);
+}
+
+template <typename T, typename U>
+bool operator!=(const SingletonOrSharedTypePtr<T>& x, const std::shared_ptr<U>& y) {
+  return !(x == y);
+}
+
+template <typename T, typename U>
+bool operator!=(const std::shared_ptr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return !(x == y);
+}
+
+template <typename T, typename U>
+bool operator!=(const SingletonOrSharedTypePtr<T>& x, const SingletonTypePtr<U>& y) {
+  return !(x == y);
+}
+
+template <typename T, typename U>
+bool operator!=(const SingletonTypePtr<T>& x, const SingletonOrSharedTypePtr<U>& y) {
+  return !(x == y);
+}
+
+using TypePtr = SingletonOrSharedTypePtr<Type>;
+using ConstTypePtr = SingletonOrSharedTypePtr<const Type>;
+
+// Explicitly enable MaybeOwned<shared_ptr<T>>, rather than allowing
+// MaybeOwned to be used for any type right away.
+template <typename T>
+struct MaybeOwnedTraits<SingletonOrSharedTypePtr<T>>
+    : public MaybeOwnedTraitsGenericImpl<SingletonOrSharedTypePtr<T>> {};
+
+// Base class for Types that are guaranteed to be owned by std::shared_ptr.
+struct TORCH_API SharedType : public Type, public std::enable_shared_from_this<SharedType> {
+  using Type::Type;
+};
+
+inline TypePtr Type::withContained(std::vector<TypePtr> contained_types) {
+  auto current_contained = containedTypes();
+  // Types with no contained_types don't need this call. Check before calling!
+  //
+  // (We can't support this efficiently because types without
+  // contained types may be singletons, in which case
+  // shared_from_this will crash; we would have to provide a virtual
+  // typeptr_from_this or isSingleton.)
+  TORCH_INTERNAL_ASSERT(!current_contained.empty() && current_contained.size() == contained_types.size());
+  if (current_contained.equals(contained_types)) {
+    return std::static_pointer_cast<Type>(static_cast<SharedType *>(this)->shared_from_this());
+  }
+  return createWithContained(std::move(contained_types));
+}
+
+
+TORCH_API inline bool operator==(const Type& lhs, const Type& rhs) {
+  if (C10_UNLIKELY(!rhs.symmetric())) {
+    return rhs.equals(lhs);
+  }
+  return lhs.equals(rhs);
+}
+
+struct NamedType;
+using NamedTypePtr = std::shared_ptr<NamedType>;
+using ConstNamedTypePtr = std::shared_ptr<const NamedType>;
+
+struct TORCH_API NamedType : public SharedType {
+  NamedType(TypeKind tk, c10::optional<QualifiedName> name)
+      : SharedType(tk), name_(std::move(name)) {
+    TORCH_INTERNAL_ASSERT(
+        tk == TypeKind::TupleType || tk == TypeKind::FunctionType ||
+            tk == TypeKind::ClassType || tk == TypeKind::InterfaceType ||
+            tk == TypeKind::EnumType,
+        "If you add a new kind of NamedType, ",
+        "please update the cast<NamedType> specialization and this assert");
+  }
+
+  // Fully qualified name of type
+  // Looks like: "foo.bar.Baz".
+  const c10::optional<QualifiedName>& name() const {
+    return name_;
+  }
+
+ private:
+  c10::optional<QualifiedName> name_;
+};
+
+} // namespace c10
+
+namespace std {
+template <typename T>
+struct hash<c10::SingletonOrSharedTypePtr<T>> {
+  size_t operator()(const c10::SingletonOrSharedTypePtr<T>& x) const {
+    return std::hash<T*>()(x.get());
+  }
+};
+} // namespace std

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/op_registration/adaption.h

@@ -0,0 +1,83 @@
+#pragma once
+
+#include <ATen/Tensor.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/core/List.h>
+#include <c10/core/TensorOptions.h>
+
+/*
+ * [Note: hacky wrapper removal for optional tensor]
+ *
+ * The kernel implementation takes an optional tensor marked in the schema as
+ * Tensor? but the C++ function takes Tensor instead of the optional<Tensor>
+ * expected by the dispatcher.
+ *
+ * To remove the hacky wrapper, the C++ function is changed to take
+ * optional<Tensor> and unwrap the Tensor value at the beginning of
+ * the function, e.g.:
+ *   > c10::MaybeOwned<Tensor> weight_maybe_owned =
+ *   >     at::borrow_from_optional_tensor(weight_opt);
+ *   > const Tensor& weight = *weight_maybe_owned;
+ *
+ * We may want to make the kernel handle optional directly without
+ * going through the creation of a default-constructed Tensor in
+ * at::borrow_from_optional_tensor.
+ */
+
+/*
+ * [Note: hacky wrapper removal for TensorOptions]
+ *
+ * The kernel implementation takes a TensorOptions argument but the dispatcher
+ * expects separate arguments for dtype, layout, device, pin_memory.
+ *
+ * To remove the hacky wrapper, the kernel implementation is changed to take
+ * the 4 arguments (dtype, layout, device, pin_memory), and assemble the
+ * TensorOptions value at the beginning of the function, e.g.:
+ *   > TensorOptions options = TensorOptions().dtype(dtype).layout(layout)
+ *   >    .device(device).pinned_memory(pin_memory);
+ *
+ * We may want make the kernel handle these parameters directly without going
+ * through the creation of a TensorOptions value.
+ */
+
+namespace c10 {
+namespace impl {
+
+TORCH_API void common_device_check_failure(Device common_device, const at::Tensor& tensor, at::CheckedFrom methodName, at::CheckedFrom argName);
+
+inline void check_and_update_common_device(optional<Device>& common_device, const at::Tensor& tensor, at::CheckedFrom methodName, at::CheckedFrom argName) {
+  // TODO: Remove this once the following issue is addressed:
+  // https://github.com/pytorch/pytorch/issues/57380
+  if (!tensor.defined()) {
+    return;
+  }
+
+  if (!common_device.has_value()) {
+    common_device = tensor.device();
+    return;
+  }
+
+  if (C10_UNLIKELY(common_device != tensor.device())) {
+    common_device_check_failure(*common_device, tensor, methodName, argName);
+  }
+}
+
+inline void check_and_update_common_device(optional<Device>& common_device, const optional<at::Tensor>& tensor, at::CheckedFrom methodName, at::CheckedFrom argName) {
+  if (tensor.has_value()) {
+    check_and_update_common_device(common_device, tensor.value(), methodName, argName);
+  }
+}
+
+inline void check_and_update_common_device(optional<Device>& common_device, at::ITensorListRef tensors, at::CheckedFrom methodName, at::CheckedFrom argName) {
+  for (const auto& tensor : tensors) {
+    check_and_update_common_device(common_device, tensor, methodName, argName);
+  }
+}
+
+inline void check_and_update_common_device(optional<Device>& common_device, const List<optional<at::Tensor>>& tensors, at::CheckedFrom methodName, at::CheckedFrom argName) {
+  for (const auto& tensor : tensors) {
+    check_and_update_common_device(common_device, tensor, methodName, argName);
+  }
+}
+} // namespace impl
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/op_registration/op_allowlist.h

@@ -0,0 +1,199 @@
+#pragma once
+
+// TODO: unify to C10_MOBILE. In theory this header could be used in OSS.
+#ifdef TEMPLATE_SELECTIVE_BUILD
+#include <ATen/selected_mobile_ops.h>
+#endif
+
+/**
+ * This header implements functionality to build PyTorch with only a certain
+ * set of operators (+ dependencies) included.
+ *
+ * - Build with -DTORCH_OPERATOR_WHITELIST="aten::add;aten::sub" and only these
+ *   two ops will be included in your build.  The allowlist records operators
+ *   only, no overloads; if you include aten::add, all overloads of aten::add
+ *   will be included.
+ *
+ * Internally, this is done by removing the operator registration calls
+ * using compile time programming, and the linker will then prune all
+ * operator functions that weren't registered.
+ * See Note [Selective build] for more details
+ *
+ * WARNING: The allowlist mechanism doesn't work for all ways you could go about
+ * registering an operator.  If the dispatch key / operator name is not
+ * sufficiently obvious at compile time, then the allowlisting mechanism
+ * will fail (and the operator will be included in the binary anyway).
+ */
+
+#include <c10/util/string_view.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/macros/Macros.h>
+
+
+#if defined(ENABLE_RECORD_KERNEL_FUNCTION_DTYPE)
+#include <ATen/record_function.h>
+#endif
+
+namespace c10 {
+
+namespace impl {
+
+constexpr bool allowlist_contains(string_view allowlist, string_view item);  // Forward Declare
+
+/**
+ * In selective build mode returns true/false depending on whether a build
+ * feature is available or not.
+ *
+ * In instrumenting mode (tracing mode), always returns true, and doesn't
+ * trigger any side effects.
+ */
+constexpr bool is_build_feature_available(const char* name) {
+#if !defined(ENABLE_RECORD_KERNEL_FUNCTION_DTYPE)
+  // Selective Build mode.
+#if !defined(TORCH_BUILD_FEATURE_ALLOWLIST)
+  (void)name;
+  return true;
+#else
+  return allowlist_contains(
+    C10_STRINGIZE(TORCH_BUILD_FEATURE_ALLOWLIST),
+    name);
+#endif
+
+#else
+  // Instrumenting mode.
+  (void)name;
+  return true;
+#endif
+}
+
+[[noreturn]] void build_feature_required_feature_not_available(const char* feature);
+
+/**
+ * Use BUILD_FEATURE_REQUIRED macro in user-code.
+ *
+ * In selective build mode becomes a no-op if the build feature passed
+ * in is available. If not available, throws an exception (c10::Error).
+ * The compiler is able to perform dead code elimination for code
+ * following this method if the build feature is not available.
+ *
+ * In instrumenting mode (tracing mode), registers (as a side effect)
+ * the presence of this specific build feature being triggered.
+ */
+#if !defined(ENABLE_RECORD_KERNEL_FUNCTION_DTYPE)  // selective build mode
+
+#if defined(TORCH_BUILD_FEATURE_ALLOWLIST)
+#define BUILD_FEATURE_REQUIRED(NAME)                                 \
+  if (!c10::impl::is_build_feature_available(NAME)) {                \
+    ::c10::impl::build_feature_required_feature_not_available(NAME); \
+  }
+#else  // Everything trivially selected
+#define BUILD_FEATURE_REQUIRED(NAME)
+
+#endif
+
+#else  // trace mode
+#define BUILD_FEATURE_REQUIRED(NAME)  \
+  RECORD_FUNCTION_WITH_SCOPE(         \
+      at::RecordScope::BUILD_FEATURE, \
+      std::string(NAME),              \
+      {});
+#endif
+
+// Use this macro, and not is_build_feature_available
+#define BUILD_FEATURE_AVAILABLE(NAME) ::c10::impl::is_build_feature_available(NAME)
+
+// returns true iff allowlist contains item
+// allowlist_contains("a;bc;d", "bc") == true
+constexpr bool allowlist_contains(string_view allowlist, string_view item) {
+    //Choose a really big value for next so that if something goes wrong
+    //this code will blow up in a hopefully detectable way.
+    size_t next = std::numeric_limits<size_t>::max();
+    for (size_t cur = 0; cur <= allowlist.size(); cur = next) {
+      next = allowlist.find(';', cur);
+      if (next != string_view::npos) {
+        if (allowlist.substr(cur, next - cur).compare(item) == 0) {
+          return true;
+        }
+        next++;
+      } else {
+        if (allowlist.substr(cur).compare(item) == 0) {
+          return true;
+        }
+        break;
+      }
+    }
+    return false;
+}
+
+// Returns true iff the given op name is on the allowlist
+// and should be registered
+constexpr bool op_allowlist_check(string_view op_name) {
+  assert(op_name.find("::") != string_view::npos);
+  // Use assert() instead of throw() due to a gcc bug. See:
+  // https://stackoverflow.com/questions/34280729/throw-in-constexpr-function
+  // https://github.com/fmtlib/fmt/issues/682
+  assert(op_name.find("(") == string_view::npos);
+#if !defined(TORCH_OPERATOR_WHITELIST)
+  // If the TORCH_OPERATOR_WHITELIST parameter is not defined,
+  // all ops are to be registered
+  return true;
+#else
+  return allowlist_contains(
+    C10_STRINGIZE(TORCH_OPERATOR_WHITELIST),
+    // This function is majorly used for mobile selective build with
+    // root operators, where the overload is included in the allowlist.
+    op_name);
+    // // Strip overload name (as allowlist doesn't contain overloads)
+    // // Another function based on this may be added when there's usage
+    // // on op names without overload.
+    // OperatorNameView::parse(op_name).name);
+#endif
+}
+
+// Returns true iff the given schema string is on the allowlist
+// and should be registered
+constexpr bool schema_allowlist_check(string_view schema) {
+#if defined(TORCH_FORCE_SCHEMA_REGISTRATION)
+  return true;
+#else
+  return op_allowlist_check(schema.substr(0, schema.find("(")));
+#endif
+}
+
+// Returns true iff the given custom class name is on the allowlist
+// and should be registered
+constexpr bool custom_class_allowlist_check(string_view custom_class_name) {
+#if !defined(TORCH_CUSTOM_CLASS_ALLOWLIST)
+  // If the TORCH_CUSTOM_CLASS_ALLOWLIST parameter is not defined,
+  // all custom classes are to be registered
+  (void)custom_class_name;
+  return true;
+#else
+  return allowlist_contains(
+    C10_STRINGIZE(TORCH_CUSTOM_CLASS_ALLOWLIST),
+    custom_class_name);
+#endif
+}
+
+// schema_allowlist_check() implicitly depends on a macro, TORCH_OPERATOR_WHITELIST.
+// Add this API to pass arbitrary allowlist.
+constexpr bool op_allowlist_contains_name_in_schema(string_view allowlist, string_view schema) {
+  return allowlist_contains(allowlist, schema.substr(0, schema.find("(")));
+}
+
+// Returns true iff the given dispatch key is on the allowlist
+// and should be registered.  When we turn this on, the list of valid
+// mobile dispatch keys is hard coded (but you need to make sure
+// that you have the correct set of dispatch keys for this).
+constexpr bool dispatch_key_allowlist_check(DispatchKey /*k*/) {
+#ifdef C10_MOBILE
+  return true;
+  // Disabled for now: to be enabled later!
+  // return k == DispatchKey::CPU || k == DispatchKey::Vulkan || k == DispatchKey::QuantizedCPU || k == DispatchKey::BackendSelect || k == DispatchKey::CatchAll;
+#else
+  return true;
+#endif
+}
+
+} // namespace impl
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/core/rref_interface.h

@@ -0,0 +1,40 @@
+#pragma once
+
+#include <c10/util/intrusive_ptr.h>
+#include <ATen/core/type_ptr.h>
+
+namespace c10 {
+
+struct Type;
+using worker_id_t = int16_t;
+
+// This abstract class contains only user-facing APIs, and will be shared
+// between jit and distributed to implement TorchScript support.
+class C10_EXPORT RRefInterface : public c10::intrusive_ptr_target {
+ public:
+  RRefInterface() = default;
+  // RRef is made NOT copyable NOT movable to prevent messing up reference
+  // counting.
+  RRefInterface(const RRefInterface& other) = delete;
+  RRefInterface(RRefInterface&& other) = delete;
+  RRefInterface& operator=(RRefInterface&& other) = delete;
+
+  ~RRefInterface() override = default;
+
+  // returns the worker id of the owner
+  virtual worker_id_t owner() const = 0;
+
+  // returns the worker name of the owner
+  virtual std::string ownerName() const = 0;
+
+  // Returns true if this is the ``OwnerRRef``
+  virtual bool isOwner() const = 0;
+
+  // Returns true if this is an ``OwnerRRef`` or if this ``UserRRef`` has been
+  // confirmed by its owner.
+  virtual bool confirmedByOwner() const = 0;
+
+  virtual const TypePtr type() const = 0;
+};
+
+}

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/cuda/detail/DeviceThreadHandles.h

@@ -0,0 +1,151 @@
+// Some stateful GPU libraries, such as cuDNN, cuBLAS, use handles to store states.
+// These handles are tied to device, and these libraries requires/recommends not to
+// share handles across host threads.
+//
+// These libraries recommend using one handle per host thread. We may not want to do
+// this because threads are relatively light-weight, but creating and destroying
+// handles is expensive (destroying the handle causes synchronizations). DataParallel,
+// for example, creates new threads for each forward pass.
+//
+// This file implements a handle pool mechanism. The handle pool returns handles on
+// demand as threads request them. If all existing handles in the pool are in use,
+// it creates a new one. As threads terminate, they release handles back into the pool.
+// In this way, the handle pool never creates more handles than the high-water mark of
+// active threads, so it's efficient with DataParallel.
+
+#pragma once
+
+#include <unordered_map>
+#include <vector>
+#include <utility>
+#include <mutex>
+#include <memory>
+
+#include <c10/util/Exception.h>
+
+namespace at::cuda { namespace {
+
+template <typename Handle_t, void Create(Handle_t *), void Destroy(Handle_t)>
+struct DeviceThreadHandlePool : public std::enable_shared_from_this<DeviceThreadHandlePool<Handle_t, Create, Destroy>> {
+
+    struct Handle {
+    Handle_t handle;
+    Handle(bool create = false) : handle(nullptr)
+    {
+        if(create) Create(&handle);
+    }
+    // std::vector.emplace() and push_back() may route through temporaries and call
+    // copy/move constructors along the way.  If this is the case, we don't want
+    // the destructors of temporaries to call cudnnDestroy on the handle.
+    // We can achieve safety (for the narrow case of stashing within std::vectors)
+    // by making Handle moveable but not copyable, and transferring handle ownership
+    // to the latest constructed object.  This is not a substitute for full-blown
+    // reference counting, but reference counting may be overkill here.
+    // Another alternative is to wrap the saved Handles in unique_ptrs, i.e.,
+    // unordered_map<int, vector<unique_ptr<Handle>>> created_handles;
+    Handle(const Handle& rhs) = delete;
+    // Following https://stackoverflow.com/questions/3279543/what-is-the-copy-and-swap-idiom
+    Handle(Handle&& rhs) : Handle() { std::swap(handle, rhs.handle); }
+    // operator= takes argument by value
+    Handle& operator=(Handle rhs) { std::swap(handle, rhs.handle); return *this; }
+    ~Handle() {
+        if(handle) Destroy(handle);
+    }
+    };
+
+    std::mutex mutex;
+
+    // Handles are lazily created as different threads request them,
+    // but are never destroyed until the end of the process.
+    // The maximum number of handles this process will create for each device is equal
+    // to the high-water mark of the number of concurrently active threads that request
+    // handles for that device.
+    // When threads terminate, they release their handles back into the pool for reuse.
+    // Otherwise, new handles would be created every time new threads were spawned,
+    // resulting in poor performance for Python modules that repeatedly or frequently
+    // spawned new sets of threads (like DataParallel, which creates a new set of threads
+    // for each forward pass).
+    //
+    // To prevent potential deadlocks, we explicitly choose not to cap the number
+    // of handles that are created per device.
+    // Example of danger: If we cap the max handles at 4, and 5 threads are sharing a device,
+    // only 4 can make forward progress at any time. The other 4 will not release their
+    // handles until they exit, so the fifth cannot make progress until then.  This is
+    // not a problem...UNLESS all 5 threads attempt some sort of synchronization at an
+    // intermediate point (ie, before any of them have exited).  We have no way to anticipate
+    // or enforce that user threads will not attempt such intermediate synchronization.
+    // The only way to ensure safety is to avoid imposing a cap on the number of handles.
+    std::unordered_map<int, std::vector<Handle>> created_handles;
+    std::unordered_map<int, std::vector<Handle_t>> available_handles;
+
+    // PoolWindow lazily creates and caches the handles that a particular thread is using,
+    // so in the common case handle access doesn't incur either handle creation or a mutex lock.
+    class PoolWindow
+    {
+    public:
+    PoolWindow(std::shared_ptr<DeviceThreadHandlePool> parent): weak_parent(std::move(parent)) {}
+    ~PoolWindow(){ release(); }
+
+    Handle_t reserve(int device)
+    {
+        // If this thread already has a handle for this device, return it
+        if(my_handles.find(device) != my_handles.end())
+        return my_handles[device];
+
+        // otherwise, either grab a handle from the pool if one is available,
+        // or if not, create a new one.
+        auto parent = weak_parent.lock();
+        TORCH_CHECK(parent, "Cannot create handle during program termination");
+        std::lock_guard<std::mutex> guard(parent->mutex);
+
+        if(parent->available_handles[device].size() > 0)
+        {
+        my_handles[device] = parent->available_handles[device].back();
+        parent->available_handles[device].pop_back();
+        }
+        else
+        {
+        // In local testing, I do observe that emplace_back sometimes routes through temporaries
+        // that incur move-constructor and destructor calls.  See comments in Handle above.
+        parent->created_handles[device].emplace_back(true /*create*/);
+        my_handles[device] = parent->created_handles[device].back().handle;
+        }
+
+        return my_handles[device];
+    }
+
+    private:
+    // Stores the per-device handles currently owned by this thread
+    std::unordered_map<int, Handle_t> my_handles;
+
+    std::weak_ptr<DeviceThreadHandlePool> weak_parent;
+
+    // Called by the destructor.  Releases this thread's handles back into the pool.
+    void release() {
+        if(my_handles.size() > 0) {
+            auto parent = weak_parent.lock();
+            if (!parent) {
+                // If this thread exits after atexit handlers have completed, the
+                // cuda context itself may be invalid, so we must leak the handles.
+                return;
+            }
+
+            std::lock_guard<std::mutex> guard(parent->mutex);
+            for(auto d_h : my_handles)
+                parent->available_handles[d_h.first].push_back(d_h.second);
+        }
+    }
+    };
+
+    // Warning:
+    // If you want to change this function, be aware that this function will be called
+    // by multiple threads and there is no mutex guarding the call of this function, so
+    // make sure your implementation is thread-safe.
+    PoolWindow *newPoolWindow() {
+        // The returned pointer will be owned by a thread local variable
+        // so that different threads does not share the same PoolWindow.
+        return new PoolWindow(this->shared_from_this());
+    }
+};
+
+}}  // namespace at::cuda::detail::<anonymous>

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/xpu/XPUContext.h

@@ -0,0 +1,20 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <c10/xpu/XPUFunctions.h>
+#include <c10/xpu/XPUStream.h>
+
+namespace at::xpu {
+
+// XPU is available if we compiled with XPU.
+inline bool is_available() {
+  return c10::xpu::device_count() > 0;
+}
+
+TORCH_XPU_API DeviceProp* getCurrentDeviceProperties();
+
+TORCH_XPU_API DeviceProp* getDeviceProperties(DeviceIndex device);
+
+TORCH_XPU_API int32_t getGlobalIdxFromDevice(DeviceIndex device);
+
+} // namespace at::xpu

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/xpu/XPUDevice.h

@@ -0,0 +1,13 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <c10/xpu/XPUFunctions.h>
+
+namespace at::xpu {
+
+inline Device getDeviceFromPtr(void* ptr) {
+  auto device = c10::xpu::get_device_idx_from_pointer(ptr);
+  return {c10::DeviceType::XPU, device};
+}
+
+} // namespace at::xpu

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/xpu/XPUEvent.h

@@ -0,0 +1,132 @@
+#pragma once
+#include <ATen/xpu/XPUContext.h>
+
+#include <optional>
+
+namespace at::xpu {
+
+/*
+ * XPUEvent are movable not copyable wrappers around SYCL event. XPUEvent are
+ * constructed lazily when first recorded. It has a device, and this device is
+ * acquired from the first recording stream. Later streams that record the event
+ * must match the same device.
+ *
+ * Currently, XPUEvent does NOT support to export an inter-process event from
+ * another process via inter-process comunication(IPC). So it means that
+ * inter-process communication for event handles between different processes is
+ * not available. This could impact some applications that rely on cross-process
+ * synchronization and communication.
+ */
+struct TORCH_XPU_API XPUEvent {
+  // Constructors
+  XPUEvent(bool enable_timing = false) noexcept
+      : enable_timing_{enable_timing} {}
+
+  ~XPUEvent() = default;
+
+  XPUEvent(const XPUEvent&) = delete;
+  XPUEvent& operator=(const XPUEvent&) = delete;
+
+  XPUEvent(XPUEvent&& other) = default;
+  XPUEvent& operator=(XPUEvent&& other) = default;
+
+  operator sycl::event&() const {
+    return event();
+  }
+
+  std::optional<at::Device> device() const {
+    if (isCreated()) {
+      return at::Device(at::kXPU, device_index_);
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  inline bool isCreated() const {
+    return (event_.get() != nullptr);
+  }
+
+  DeviceIndex device_index() const {
+    return device_index_;
+  }
+
+  sycl::event& event() const {
+    return *event_;
+  }
+
+  bool query() const {
+    using namespace sycl::info;
+    if (!isCreated()) {
+      return true;
+    }
+
+    return event().get_info<event::command_execution_status>() ==
+        event_command_status::complete;
+  }
+
+  void record() {
+    record(getCurrentXPUStream());
+  }
+
+  void recordOnce(const XPUStream& stream) {
+    if (!isCreated()) {
+      record(stream);
+    }
+  }
+
+  void record(const XPUStream& stream) {
+    if (!isCreated()) {
+      device_index_ = stream.device_index();
+    } else {
+      TORCH_CHECK(
+          device_index_ == stream.device_index(),
+          "Event device ",
+          device_index_,
+          " does not match recording stream's device ",
+          stream.device_index(),
+          ".");
+      event_.reset();
+    }
+    event_ = std::make_unique<sycl::event>(
+        stream.queue().ext_oneapi_submit_barrier());
+  }
+
+  void block(const XPUStream& stream) {
+    if (isCreated()) {
+      std::vector<sycl::event> event_list{event()};
+      // Make this stream wait until event_ is completed.
+      stream.queue().ext_oneapi_submit_barrier(event_list);
+    }
+  }
+
+  float elapsed_time(const XPUEvent& other) const {
+    TORCH_CHECK(
+        isCreated() && other.isCreated(),
+        "Both events must be recorded before calculating elapsed time.");
+    TORCH_CHECK(
+        query() && other.query(),
+        "Both events must be completed before calculating elapsed time.");
+    TORCH_CHECK(
+        enable_timing_ && other.enable_timing_,
+        "Both events must be created with argument 'enable_timing=True'.");
+    // TODO: provides the ability to time the execution of commands in a SYCL
+    // queue without enabling profiling on the entire queue
+    TORCH_CHECK_NOT_IMPLEMENTED(
+        false, "elapsed_time is not supported by XPUEvent.");
+  }
+
+  void synchronize() const {
+    if (isCreated()) {
+      event().wait_and_throw();
+    }
+  }
+
+ private:
+  bool enable_timing_ = false;
+  DeviceIndex device_index_ = -1;
+  // Only need to track the last event, as events in an in-order queue are
+  // executed sequentially.
+  std::unique_ptr<sycl::event> event_;
+};
+
+} // namespace at::xpu

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/xpu/XPUGeneratorImpl.h

@@ -0,0 +1,39 @@
+#pragma once
+
+#include <ATen/core/Generator.h>
+
+namespace at {
+
+struct TORCH_API XPUGeneratorImpl : public GeneratorImpl {
+  // Constructors
+  XPUGeneratorImpl(DeviceIndex device_index = -1);
+  ~XPUGeneratorImpl() override = default;
+
+  // XPUGeneratorImpl methods
+  std::shared_ptr<XPUGeneratorImpl> clone() const;
+  void set_current_seed(uint64_t seed) override;
+  void set_offset(uint64_t offset) override;
+  uint64_t get_offset() const override;
+  uint64_t current_seed() const override;
+  uint64_t seed() override;
+  void set_state(const c10::TensorImpl& new_state) override;
+  c10::intrusive_ptr<c10::TensorImpl> get_state() const override;
+  void set_philox_offset_per_thread(uint64_t offset);
+  uint64_t philox_offset_per_thread() const;
+  std::pair<uint64_t, uint64_t> philox_engine_inputs(uint64_t increment);
+  static c10::DeviceType device_type();
+
+ private:
+  XPUGeneratorImpl* clone_impl() const override;
+  uint64_t seed_ = default_rng_seed_val;
+  uint64_t philox_offset_per_thread_ = 0;
+};
+
+namespace xpu::detail {
+
+TORCH_XPU_API const Generator& getDefaultXPUGenerator(DeviceIndex device = -1);
+
+TORCH_XPU_API Generator createXPUGenerator(DeviceIndex device = -1);
+
+} // namespace xpu::detail
+} // namespace at

llmeval-env/lib/python3.10/site-packages/torch/include/ATen/xpu/detail/XPUHooks.h

@@ -0,0 +1,23 @@
+#pragma once
+
+#include <ATen/detail/XPUHooksInterface.h>
+
+namespace at::xpu::detail {
+
+// The real implementation of XPUHooksInterface
+struct XPUHooks : public at::XPUHooksInterface {
+  XPUHooks(at::XPUHooksArgs) {}
+  void initXPU() const override;
+  bool hasXPU() const override;
+  std::string showConfig() const override;
+  int32_t getGlobalIdxFromDevice(const at::Device& device) const override;
+  Generator getXPUGenerator(DeviceIndex device_index = -1) const override;
+  const Generator& getDefaultXPUGenerator(
+      DeviceIndex device_index = -1) const override;
+  Device getDeviceFromPtr(void* data) const override;
+  c10::DeviceIndex getNumGPUs() const override;
+  DeviceIndex current_device() const override;
+  void deviceSynchronize(DeviceIndex device_index) const override;
+};
+
+} // namespace at::xpu::detail

llmeval-env/lib/python3.10/site-packages/torch/include/c10/macros/Export.h

@@ -0,0 +1,160 @@
+#ifndef C10_MACROS_EXPORT_H_
+#define C10_MACROS_EXPORT_H_
+
+/* Header file to define the common scaffolding for exported symbols.
+ *
+ * Export is by itself a quite tricky situation to deal with, and if you are
+ * hitting this file, make sure you start with the background here:
+ * - Linux: https://gcc.gnu.org/wiki/Visibility
+ * - Windows:
+ * https://docs.microsoft.com/en-us/cpp/cpp/dllexport-dllimport?view=vs-2017
+ *
+ * Do NOT include this file directly. Instead, use c10/macros/Macros.h
+ */
+
+// You do not need to edit this part of file unless you are changing the core
+// pytorch export abstractions.
+//
+// This part defines the C10 core export and import macros. This is controlled
+// by whether we are building shared libraries or not, which is determined
+// during build time and codified in c10/core/cmake_macros.h.
+// When the library is built as a shared lib, EXPORT and IMPORT will contain
+// visibility attributes. If it is being built as a static lib, then EXPORT
+// and IMPORT basically have no effect.
+
+// As a rule of thumb, you should almost NEVER mix static and shared builds for
+// libraries that depend on c10. AKA, if c10 is built as a static library, we
+// recommend everything dependent on c10 to be built statically. If c10 is built
+// as a shared library, everything dependent on it should be built as shared. In
+// the PyTorch project, all native libraries shall use the macro
+// C10_BUILD_SHARED_LIB to check whether pytorch is building shared or static
+// libraries.
+
+// For build systems that do not directly depend on CMake and directly build
+// from the source directory (such as Buck), one may not have a cmake_macros.h
+// file at all. In this case, the build system is responsible for providing
+// correct macro definitions corresponding to the cmake_macros.h.in file.
+//
+// In such scenarios, one should define the macro
+//     C10_USING_CUSTOM_GENERATED_MACROS
+// to inform this header that it does not need to include the cmake_macros.h
+// file.
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+#include <c10/macros/cmake_macros.h>
+#endif // C10_USING_CUSTOM_GENERATED_MACROS
+
+#ifdef _WIN32
+#define C10_HIDDEN
+#if defined(C10_BUILD_SHARED_LIBS)
+#define C10_EXPORT __declspec(dllexport)
+#define C10_IMPORT __declspec(dllimport)
+#else
+#define C10_EXPORT
+#define C10_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_EXPORT __attribute__((__visibility__("default")))
+#define C10_HIDDEN __attribute__((__visibility__("hidden")))
+#else // defined(__GNUC__)
+#define C10_EXPORT
+#define C10_HIDDEN
+#endif // defined(__GNUC__)
+#define C10_IMPORT C10_EXPORT
+#endif // _WIN32
+
+#ifdef NO_EXPORT
+#undef C10_EXPORT
+#define C10_EXPORT
+#endif
+
+// Definition of an adaptive XX_API macro, that depends on whether you are
+// building the library itself or not, routes to XX_EXPORT and XX_IMPORT.
+// Basically, you will need to do this for each shared library that you are
+// building, and the instruction is as follows: assuming that you are building
+// a library called libawesome.so. You should:
+// (1) for your cmake target (usually done by "add_library(awesome, ...)"),
+//     define a macro called AWESOME_BUILD_MAIN_LIB using
+//     target_compile_options.
+// (2) define the AWESOME_API macro similar to the one below.
+// And in the source file of your awesome library, use AWESOME_API to
+// annotate public symbols.
+
+// Here, for the C10 library, we will define the macro C10_API for both import
+// and export.
+
+// This one is being used by libc10.so
+#ifdef C10_BUILD_MAIN_LIB
+#define C10_API C10_EXPORT
+#else
+#define C10_API C10_IMPORT
+#endif
+
+// This one is being used by libtorch.so
+#ifdef CAFFE2_BUILD_MAIN_LIB
+#define TORCH_API C10_EXPORT
+#else
+#define TORCH_API C10_IMPORT
+#endif
+
+// You may be wondering: Whose brilliant idea was it to split torch_cuda into
+// two pieces with confusing names?
+// Once upon a time, there _was_ only TORCH_CUDA_API. All was happy until we
+// tried to compile PyTorch for CUDA 11.1, which ran into relocation marker
+// issues when linking big binaries.
+// (https://github.com/pytorch/pytorch/issues/39968) We had two choices:
+//    (1) Stop supporting so many GPU architectures
+//    (2) Do something else
+// We chose #2 and decided to split the behemoth that was torch_cuda into two
+// smaller libraries, one with most of the core kernel functions (torch_cuda_cu)
+// and the other that had..well..everything else (torch_cuda_cpp). The idea was
+// this: instead of linking our static libraries (like the hefty
+// libcudnn_static.a) with another huge library, torch_cuda, and run into pesky
+// relocation marker issues, we could link our static libraries to a smaller
+// part of torch_cuda (torch_cuda_cpp) and avoid the issues.
+
+// libtorch_cuda_cu.so
+#ifdef TORCH_CUDA_CU_BUILD_MAIN_LIB
+#define TORCH_CUDA_CU_API C10_EXPORT
+#elif defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CU_API C10_IMPORT
+#endif
+
+// libtorch_cuda_cpp.so
+#ifdef TORCH_CUDA_CPP_BUILD_MAIN_LIB
+#define TORCH_CUDA_CPP_API C10_EXPORT
+#elif defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CPP_API C10_IMPORT
+#endif
+
+// libtorch_cuda.so (where torch_cuda_cu and torch_cuda_cpp are a part of the
+// same api)
+#ifdef TORCH_CUDA_BUILD_MAIN_LIB
+#define TORCH_CUDA_CPP_API C10_EXPORT
+#define TORCH_CUDA_CU_API C10_EXPORT
+#elif !defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CPP_API C10_IMPORT
+#define TORCH_CUDA_CU_API C10_IMPORT
+#endif
+
+#if defined(TORCH_HIP_BUILD_MAIN_LIB)
+#define TORCH_HIP_API C10_EXPORT
+#else
+#define TORCH_HIP_API C10_IMPORT
+#endif
+
+#if defined(TORCH_XPU_BUILD_MAIN_LIB)
+#define TORCH_XPU_API C10_EXPORT
+#else
+#define TORCH_XPU_API C10_IMPORT
+#endif
+
+// Enums only need to be exported on windows for non-CUDA files
+#if defined(_WIN32) && defined(__CUDACC__)
+#define C10_API_ENUM C10_API
+#else
+#define C10_API_ENUM
+#endif
+
+#endif // C10_MACROS_MACROS_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/macros/cmake_macros.h

@@ -0,0 +1,14 @@
+#ifndef C10_MACROS_CMAKE_MACROS_H_
+#define C10_MACROS_CMAKE_MACROS_H_
+
+// Automatically generated header file for the C10 library.
+// Do not include this file directly. Instead, include c10/macros/Macros.h.
+
+#define C10_BUILD_SHARED_LIBS
+/* #undef C10_USE_GLOG */
+/* #undef C10_USE_GFLAGS */
+/* #undef C10_USE_NUMA */
+/* #undef C10_USE_MSVC_STATIC_RUNTIME */
+/* #undef C10_USE_ROCM_KERNEL_ASSERT */
+
+#endif // C10_MACROS_CMAKE_MACROS_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/AbortHandler.h

@@ -0,0 +1,81 @@
+#include <c10/macros/Macros.h>
+#include <c10/util/Backtrace.h>
+#include <c10/util/env.h>
+#include <cstdlib>
+#include <exception>
+#include <iostream>
+#include <mutex>
+#include <optional>
+
+namespace c10 {
+class AbortHandlerHelper {
+ public:
+  static AbortHandlerHelper& getInstance() {
+#ifdef _WIN32
+    thread_local
+#endif // _WIN32
+        static AbortHandlerHelper instance;
+    return instance;
+  }
+
+  void set(std::terminate_handler handler) {
+    std::lock_guard<std::mutex> lk(mutex);
+    if (!inited) {
+      prev = std::set_terminate(handler);
+      curr = std::get_terminate();
+      inited = true;
+    }
+  }
+
+  std::terminate_handler getPrev() const {
+    return prev;
+  }
+
+ private:
+  std::terminate_handler prev = nullptr;
+  std::terminate_handler curr = nullptr;
+  bool inited = false;
+  std::mutex mutex;
+  AbortHandlerHelper() = default;
+  ~AbortHandlerHelper() {
+    // Only restore the handler if we are the current one
+    if (inited && curr == std::get_terminate()) {
+      std::set_terminate(prev);
+    }
+  }
+
+ public:
+  AbortHandlerHelper(AbortHandlerHelper const&) = delete;
+  void operator=(AbortHandlerHelper const&) = delete;
+};
+
+namespace detail {
+C10_ALWAYS_INLINE void terminate_handler() {
+  std::cout << "Unhandled exception caught in c10/util/AbortHandler.h" << '\n';
+  auto backtrace = get_backtrace();
+  std::cout << backtrace << '\n' << std::flush;
+  auto prev_handler = AbortHandlerHelper::getInstance().getPrev();
+  if (prev_handler) {
+    prev_handler();
+  } else {
+    std::abort();
+  }
+}
+} // namespace detail
+
+C10_ALWAYS_INLINE void set_terminate_handler() {
+  bool use_custom_terminate = false;
+  // On Windows it is enabled by default based on
+  // https://github.com/pytorch/pytorch/pull/50320#issuecomment-763147062
+#ifdef _WIN32
+  use_custom_terminate = true;
+#endif // _WIN32
+  auto result = c10::utils::check_env("TORCH_CUSTOM_TERMINATE");
+  if (result != std::nullopt) {
+    use_custom_terminate = result.value();
+  }
+  if (use_custom_terminate) {
+    AbortHandlerHelper::getInstance().set(detail::terminate_handler);
+  }
+}
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/ApproximateClock.h

@@ -0,0 +1,115 @@
+// Copyright 2023-present Facebook. All Rights Reserved.
+
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <array>
+#include <chrono>
+#include <cstddef>
+#include <cstdint>
+#include <ctime>
+#include <functional>
+#include <type_traits>
+
+#if defined(C10_IOS) && defined(C10_MOBILE)
+#include <sys/time.h> // for gettimeofday()
+#endif
+
+#if defined(__i386__) || defined(__x86_64__) || defined(__amd64__)
+#define C10_RDTSC
+#if defined(_MSC_VER)
+#include <intrin.h>
+#elif defined(__CUDACC__) || defined(__HIPCC__)
+#undef C10_RDTSC
+#elif defined(__clang__)
+// `__rdtsc` is available by default.
+// NB: This has to be first, because Clang will also define `__GNUC__`
+#elif defined(__GNUC__)
+#include <x86intrin.h>
+#else
+#undef C10_RDTSC
+#endif
+#endif
+
+namespace c10 {
+
+using time_t = int64_t;
+using steady_clock_t = std::conditional_t<
+    std::chrono::high_resolution_clock::is_steady,
+    std::chrono::high_resolution_clock,
+    std::chrono::steady_clock>;
+
+inline time_t getTimeSinceEpoch() {
+  auto now = std::chrono::system_clock::now().time_since_epoch();
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
+}
+
+inline time_t getTime(bool allow_monotonic = false) {
+#if defined(C10_IOS) && defined(C10_MOBILE)
+  // clock_gettime is only available on iOS 10.0 or newer. Unlike OS X, iOS
+  // can't rely on CLOCK_REALTIME, as it is defined no matter if clock_gettime
+  // is implemented or not
+  struct timeval now;
+  gettimeofday(&now, NULL);
+  return static_cast<time_t>(now.tv_sec) * 1000000000 +
+      static_cast<time_t>(now.tv_usec) * 1000;
+#elif defined(_WIN32) || defined(__MACH__)
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(
+             steady_clock_t::now().time_since_epoch())
+      .count();
+#else
+  // clock_gettime is *much* faster than std::chrono implementation on Linux
+  struct timespec t {};
+  auto mode = CLOCK_REALTIME;
+  if (allow_monotonic) {
+    mode = CLOCK_MONOTONIC;
+  }
+  clock_gettime(mode, &t);
+  return static_cast<time_t>(t.tv_sec) * 1000000000 +
+      static_cast<time_t>(t.tv_nsec);
+#endif
+}
+
+// We often do not need to capture true wall times. If a fast mechanism such
+// as TSC is available we can use that instead and convert back to epoch time
+// during post processing. This greatly reduce the clock's contribution to
+// profiling.
+//   http://btorpey.github.io/blog/2014/02/18/clock-sources-in-linux/
+//   https://quick-bench.com/q/r8opkkGZSJMu9wM_XTbDouq-0Io
+// TODO: We should use
+// `https://github.com/google/benchmark/blob/main/src/cycleclock.h`
+inline auto getApproximateTime() {
+#if defined(C10_RDTSC)
+  return static_cast<uint64_t>(__rdtsc());
+#else
+  return getTime();
+#endif
+}
+
+using approx_time_t = decltype(getApproximateTime());
+static_assert(
+    std::is_same_v<approx_time_t, int64_t> ||
+        std::is_same_v<approx_time_t, uint64_t>,
+    "Expected either int64_t (`getTime`) or uint64_t (some TSC reads).");
+
+// Convert `getCount` results to Nanoseconds since unix epoch.
+class C10_API ApproximateClockToUnixTimeConverter final {
+ public:
+  ApproximateClockToUnixTimeConverter();
+  std::function<time_t(approx_time_t)> makeConverter();
+
+  struct UnixAndApproximateTimePair {
+    time_t t_;
+    approx_time_t approx_t_;
+  };
+  static UnixAndApproximateTimePair measurePair();
+
+ private:
+  static constexpr size_t replicates = 1001;
+  using time_pairs = std::array<UnixAndApproximateTimePair, replicates>;
+  time_pairs measurePairs();
+
+  time_pairs start_times_;
+};
+
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/BFloat16-math.h

@@ -0,0 +1,287 @@
+#pragma once
+
+#include <c10/util/BFloat16.h>
+#include <c10/util/Half.h>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+
+namespace std {
+
+template <typename T>
+struct is_reduced_floating_point
+    : std::integral_constant<
+          bool,
+          std::is_same_v<T, c10::Half> || std::is_same_v<T, c10::BFloat16>> {};
+
+template <typename T>
+constexpr bool is_reduced_floating_point_v =
+    is_reduced_floating_point<T>::value;
+
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T acos(T a) {
+  return std::acos(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T asin(T a) {
+  return std::asin(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T atan(T a) {
+  return std::atan(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T atanh(T a) {
+  return std::atanh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T erf(T a) {
+  return std::erf(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T erfc(T a) {
+  return std::erfc(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T exp(T a) {
+  return std::exp(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T expm1(T a) {
+  return std::expm1(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log(T a) {
+  return std::log(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log10(T a) {
+  return std::log10(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log1p(T a) {
+  return std::log1p(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log2(T a) {
+  return std::log2(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T ceil(T a) {
+  return std::ceil(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T cos(T a) {
+  return std::cos(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T floor(T a) {
+  return std::floor(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T nearbyint(T a) {
+  return std::nearbyint(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sin(T a) {
+  return std::sin(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T tan(T a) {
+  return std::tan(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sinh(T a) {
+  return std::sinh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T cosh(T a) {
+  return std::cosh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T tanh(T a) {
+  return std::tanh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T trunc(T a) {
+  return std::trunc(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T lgamma(T a) {
+  return std::lgamma(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sqrt(T a) {
+  return std::sqrt(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T rsqrt(T a) {
+  return 1.0 / std::sqrt(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T abs(T a) {
+  return std::abs(float(a));
+}
+#if defined(_MSC_VER) && defined(__CUDACC__)
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, double b) {
+  return std::pow(float(a), float(b));
+}
+#else
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, double b) {
+  return std::pow(float(a), b);
+}
+#endif
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, T b) {
+  return std::pow(float(a), float(b));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T fmod(T a, T b) {
+  return std::fmod(float(a), float(b));
+}
+
+/*
+  The following function is inspired from the implementation in `musl`
+  Link to License: https://git.musl-libc.org/cgit/musl/tree/COPYRIGHT
+  ----------------------------------------------------------------------
+  Copyright © 2005-2020 Rich Felker, et al.
+
+  Permission is hereby granted, free of charge, to any person obtaining
+  a copy of this software and associated documentation files (the
+  "Software"), to deal in the Software without restriction, including
+  without limitation the rights to use, copy, modify, merge, publish,
+  distribute, sublicense, and/or sell copies of the Software, and to
+  permit persons to whom the Software is furnished to do so, subject to
+  the following conditions:
+
+  The above copyright notice and this permission notice shall be
+  included in all copies or substantial portions of the Software.
+
+  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+  EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+  CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+  TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+  SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+  ----------------------------------------------------------------------
+ */
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+C10_HOST_DEVICE inline T nextafter(T from, T to) {
+  // Reference:
+  // https://git.musl-libc.org/cgit/musl/tree/src/math/nextafter.c
+  using int_repr_t = uint16_t;
+  using float_t = T;
+  constexpr uint8_t bits = 16;
+  union {
+    float_t f;
+    int_repr_t i;
+  } ufrom = {from}, uto = {to};
+
+  // get a mask to get the sign bit i.e. MSB
+  int_repr_t sign_mask = int_repr_t{1} << (bits - 1);
+
+  // short-circuit: if either is NaN, return NaN
+  if (from != from || to != to) {
+    return from + to;
+  }
+
+  // short-circuit: if they are exactly the same.
+  if (ufrom.i == uto.i) {
+    return from;
+  }
+
+  // mask the sign-bit to zero i.e. positive
+  // equivalent to abs(x)
+  int_repr_t abs_from = ufrom.i & ~sign_mask;
+  int_repr_t abs_to = uto.i & ~sign_mask;
+  if (abs_from == 0) {
+    // if both are zero but with different sign,
+    // preserve the sign of `to`.
+    if (abs_to == 0) {
+      return to;
+    }
+    // smallest subnormal with sign of `to`.
+    ufrom.i = (uto.i & sign_mask) | int_repr_t{1};
+    return ufrom.f;
+  }
+
+  // if abs(from) > abs(to) or sign(from) != sign(to)
+  if (abs_from > abs_to || ((ufrom.i ^ uto.i) & sign_mask)) {
+    ufrom.i--;
+  } else {
+    ufrom.i++;
+  }
+
+  return ufrom.f;
+}
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Backtrace.h

@@ -0,0 +1,17 @@
+#ifndef C10_UTIL_BACKTRACE_H_
+#define C10_UTIL_BACKTRACE_H_
+
+#include <cstddef>
+#include <string>
+#include <typeinfo>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+C10_API std::string get_backtrace(
+    size_t frames_to_skip = 0,
+    size_t maximum_number_of_frames = 64,
+    bool skip_python_frames = true);
+} // namespace c10
+
+#endif // C10_UTIL_BACKTRACE_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Bitset.h

@@ -0,0 +1,116 @@
+#pragma once
+
+#include <cstddef>
+#if defined(_MSC_VER)
+#include <intrin.h>
+#endif
+
+namespace c10::utils {
+
+/**
+ * This is a simple bitset class with sizeof(long long int) bits.
+ * You can set bits, unset bits, query bits by index,
+ * and query for the first set bit.
+ * Before using this class, please also take a look at std::bitset,
+ * which has more functionality and is more generic. It is probably
+ * a better fit for your use case. The sole reason for c10::utils::bitset
+ * to exist is that std::bitset misses a find_first_set() method.
+ */
+struct bitset final {
+ private:
+#if defined(_MSC_VER)
+  // MSVCs _BitScanForward64 expects int64_t
+  using bitset_type = int64_t;
+#else
+  // POSIX ffsll expects long long int
+  using bitset_type = long long int;
+#endif
+ public:
+  static constexpr size_t NUM_BITS() {
+    return 8 * sizeof(bitset_type);
+  }
+
+  constexpr bitset() noexcept = default;
+  constexpr bitset(const bitset&) noexcept = default;
+  constexpr bitset(bitset&&) noexcept = default;
+  // there is an issure for gcc 5.3.0 when define default function as constexpr
+  // see https://gcc.gnu.org/bugzilla/show_bug.cgi?id=68754.
+  bitset& operator=(const bitset&) noexcept = default;
+  bitset& operator=(bitset&&) noexcept = default;
+
+  constexpr void set(size_t index) noexcept {
+    bitset_ |= (static_cast<long long int>(1) << index);
+  }
+
+  constexpr void unset(size_t index) noexcept {
+    bitset_ &= ~(static_cast<long long int>(1) << index);
+  }
+
+  constexpr bool get(size_t index) const noexcept {
+    return bitset_ & (static_cast<long long int>(1) << index);
+  }
+
+  constexpr bool is_entirely_unset() const noexcept {
+    return 0 == bitset_;
+  }
+
+  // Call the given functor with the index of each bit that is set
+  template <class Func>
+  void for_each_set_bit(Func&& func) const {
+    bitset cur = *this;
+    size_t index = cur.find_first_set();
+    while (0 != index) {
+      // -1 because find_first_set() is not one-indexed.
+      index -= 1;
+      func(index);
+      cur.unset(index);
+      index = cur.find_first_set();
+    }
+  }
+
+ private:
+  // Return the index of the first set bit. The returned index is one-indexed
+  // (i.e. if the very first bit is set, this function returns '1'), and a
+  // return of '0' means that there was no bit set.
+  size_t find_first_set() const {
+#if defined(_MSC_VER) && (defined(_M_X64) || defined(_M_ARM64))
+    unsigned long result;
+    bool has_bits_set = (0 != _BitScanForward64(&result, bitset_));
+    if (!has_bits_set) {
+      return 0;
+    }
+    return result + 1;
+#elif defined(_MSC_VER) && defined(_M_IX86)
+    unsigned long result;
+    if (static_cast<uint32_t>(bitset_) != 0) {
+      bool has_bits_set =
+          (0 != _BitScanForward(&result, static_cast<uint32_t>(bitset_)));
+      if (!has_bits_set) {
+        return 0;
+      }
+      return result + 1;
+    } else {
+      bool has_bits_set =
+          (0 != _BitScanForward(&result, static_cast<uint32_t>(bitset_ >> 32)));
+      if (!has_bits_set) {
+        return 32;
+      }
+      return result + 33;
+    }
+#else
+    return __builtin_ffsll(bitset_);
+#endif
+  }
+
+  friend bool operator==(bitset lhs, bitset rhs) noexcept {
+    return lhs.bitset_ == rhs.bitset_;
+  }
+
+  bitset_type bitset_{0};
+};
+
+inline bool operator!=(bitset lhs, bitset rhs) noexcept {
+  return !(lhs == rhs);
+}
+
+} // namespace c10::utils

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/C++17.h

@@ -0,0 +1,166 @@
+#pragma once
+#ifndef C10_UTIL_CPP17_H_
+#define C10_UTIL_CPP17_H_
+
+#include <c10/macros/Macros.h>
+#include <functional>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \
+    __GNUC__ < 9
+#error \
+    "You're trying to build PyTorch with a too old version of GCC. We need GCC 9 or later."
+#endif
+
+#if defined(__clang__) && __clang_major__ < 9
+#error \
+    "You're trying to build PyTorch with a too old version of Clang. We need Clang 9 or later."
+#endif
+
+#if (defined(_MSC_VER) && (!defined(_MSVC_LANG) || _MSVC_LANG < 201703L)) || \
+    (!defined(_MSC_VER) && __cplusplus < 201703L)
+#error You need C++17 to compile PyTorch
+#endif
+
+#if defined(_WIN32) && (defined(min) || defined(max))
+#error Macro clash with min and max -- define NOMINMAX when compiling your program on Windows
+#endif
+
+/*
+ * This header adds some polyfills with C++17 functionality
+ */
+
+namespace c10 {
+
+// in c++17 std::result_of has been superseded by std::invoke_result.  Since
+// c++20, std::result_of is removed.
+template <typename F, typename... args>
+#if defined(__cpp_lib_is_invocable) && __cpp_lib_is_invocable >= 201703L
+using invoke_result = typename std::invoke_result<F, args...>;
+#else
+using invoke_result = typename std::result_of<F && (args && ...)>;
+#endif
+
+template <typename F, typename... args>
+using invoke_result_t = typename invoke_result<F, args...>::type;
+
+// std::is_pod is deprecated in C++20, std::is_standard_layout and
+// std::is_trivial are introduced in C++11, std::conjunction has been introduced
+// in C++17.
+template <typename T>
+using is_pod = std::conjunction<std::is_standard_layout<T>, std::is_trivial<T>>;
+
+template <typename T>
+constexpr bool is_pod_v = is_pod<T>::value;
+
+namespace guts {
+
+template <typename Base, typename Child, typename... Args>
+std::enable_if_t<
+    !std::is_array_v<Base> && !std::is_array_v<Child> &&
+        std::is_base_of_v<Base, Child>,
+    std::unique_ptr<Base>>
+make_unique_base(Args&&... args) {
+  return std::unique_ptr<Base>(new Child(std::forward<Args>(args)...));
+}
+
+template <class... B>
+using conjunction = std::conjunction<B...>;
+template <class... B>
+using disjunction = std::disjunction<B...>;
+template <bool B>
+using bool_constant = std::bool_constant<B>;
+template <class B>
+using negation = std::negation<B>;
+
+template <class T>
+using void_t = std::void_t<T>;
+
+#if defined(__cpp_lib_apply) && !defined(__CUDA_ARCH__) && !defined(__HIP__)
+
+template <class F, class Tuple>
+C10_HOST_DEVICE inline constexpr decltype(auto) apply(F&& f, Tuple&& t) {
+  return std::apply(std::forward<F>(f), std::forward<Tuple>(t));
+}
+
+#else
+
+// Implementation from http://en.cppreference.com/w/cpp/utility/apply (but
+// modified)
+// TODO This is an incomplete implementation of std::apply, not working for
+// member functions.
+namespace detail {
+template <class F, class Tuple, std::size_t... INDEX>
+#if defined(_MSC_VER)
+// MSVC has a problem with the decltype() return type, but it also doesn't need
+// it
+C10_HOST_DEVICE constexpr auto apply_impl(
+    F&& f,
+    Tuple&& t,
+    std::index_sequence<INDEX...>)
+#else
+// GCC/Clang need the decltype() return type
+C10_HOST_DEVICE constexpr decltype(auto) apply_impl(
+    F&& f,
+    Tuple&& t,
+    std::index_sequence<INDEX...>)
+#endif
+{
+  return std::forward<F>(f)(std::get<INDEX>(std::forward<Tuple>(t))...);
+}
+} // namespace detail
+
+template <class F, class Tuple>
+C10_HOST_DEVICE constexpr decltype(auto) apply(F&& f, Tuple&& t) {
+  return detail::apply_impl(
+      std::forward<F>(f),
+      std::forward<Tuple>(t),
+      std::make_index_sequence<
+          std::tuple_size<std::remove_reference_t<Tuple>>::value>{});
+}
+
+#endif
+
+template <typename Functor, typename... Args>
+std::enable_if_t<
+    std::is_member_pointer_v<std::decay_t<Functor>>,
+    typename c10::invoke_result_t<Functor, Args...>>
+invoke(Functor&& f, Args&&... args) {
+  return std::mem_fn(std::forward<Functor>(f))(std::forward<Args>(args)...);
+}
+
+template <typename Functor, typename... Args>
+std::enable_if_t<
+    !std::is_member_pointer_v<std::decay_t<Functor>>,
+    typename c10::invoke_result_t<Functor, Args...>>
+invoke(Functor&& f, Args&&... args) {
+  return std::forward<Functor>(f)(std::forward<Args>(args)...);
+}
+
+namespace detail {
+struct _identity final {
+  template <class T>
+  using type_identity = T;
+
+  template <class T>
+  decltype(auto) operator()(T&& arg) {
+    return std::forward<T>(arg);
+  }
+};
+
+template <class Func, class Enable = void>
+struct function_takes_identity_argument : std::false_type {};
+
+template <class Func>
+struct function_takes_identity_argument<
+    Func,
+    std::void_t<decltype(std::declval<Func>()(_identity()))>> : std::true_type {
+};
+} // namespace detail
+
+} // namespace guts
+} // namespace c10
+
+#endif // C10_UTIL_CPP17_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/DeadlockDetection.h

@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+
+/// This file provides some simple utilities for detecting common deadlocks in
+/// PyTorch.  For now, we focus exclusively on detecting Python GIL deadlocks,
+/// as the GIL is a wide ranging lock that is taken out in many situations.
+/// The basic strategy is before performing an operation that may block, you
+/// can use TORCH_ASSERT_NO_GIL_WITHOUT_PYTHON_DEP() to assert that the GIL is
+/// not held.  This macro is to be used in contexts where no static dependency
+/// on Python is available (we will handle indirecting a virtual call for you).
+///
+/// If the GIL is held by a torchdeploy interpreter, we always report false.
+/// If you are in a context where Python bindings are available, it's better
+/// to directly assert on PyGILState_Check (as it avoids a vcall and also
+/// works correctly with torchdeploy.)
+
+#define TORCH_ASSERT_NO_GIL_WITHOUT_PYTHON_DEP() \
+  TORCH_INTERNAL_ASSERT(                         \
+      !c10::impl::check_python_gil(),            \
+      "Holding GIL before a blocking operation!  Please release the GIL before blocking, or see https://github.com/pytorch/pytorch/issues/56297 for how to release the GIL for destructors of objects")
+
+namespace c10::impl {
+
+C10_API bool check_python_gil();
+
+struct C10_API PythonGILHooks {
+  virtual ~PythonGILHooks() = default;
+  // Returns true if we hold the GIL.  If not linked against Python we
+  // always return false.
+  virtual bool check_python_gil() const = 0;
+};
+
+C10_API void SetPythonGILHooks(PythonGILHooks* factory);
+
+// DO NOT call this registerer from a torch deploy instance!  You will clobber
+// other registrations
+struct C10_API PythonGILHooksRegisterer {
+  explicit PythonGILHooksRegisterer(PythonGILHooks* factory) {
+    SetPythonGILHooks(factory);
+  }
+  ~PythonGILHooksRegisterer() {
+    SetPythonGILHooks(nullptr);
+  }
+};
+
+} // namespace c10::impl

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Exception.h

@@ -0,0 +1,711 @@
+#ifndef C10_UTIL_EXCEPTION_H_
+#define C10_UTIL_EXCEPTION_H_
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/StringUtil.h>
+
+#include <cstdint>
+#include <exception>
+#include <string>
+#include <variant>
+#include <vector>
+
+#if defined(_MSC_VER) && _MSC_VER <= 1900
+#define __func__ __FUNCTION__
+#endif
+
+namespace c10 {
+
+/// The primary ATen error class.
+/// Provides a complete error message with source location information via
+/// `what()`, and a more concise message via `what_without_backtrace()`.
+/// Don't throw this directly; use TORCH_CHECK/TORCH_INTERNAL_ASSERT instead.
+///
+/// NB: c10::Error is handled specially by the default torch to suppress the
+/// backtrace, see torch/csrc/Exceptions.h
+class C10_API Error : public std::exception {
+  // The actual error message.
+  std::string msg_;
+
+  // Context for the message (in order of decreasing specificity).  Context will
+  // be automatically formatted appropriately, so it is not necessary to add
+  // extra leading/trailing newlines to strings inside this vector
+  std::vector<std::string> context_;
+
+  // The C++ backtrace at the point when this exception was raised.  This
+  // may be empty if there is no valid backtrace.  (We don't use optional
+  // here to reduce the dependencies this file has.)
+  std::string backtrace_;
+
+  // These two are derived fields from msg_stack_ and backtrace_, but we need
+  // fields for the strings so that we can return a const char* (as the
+  // signature of std::exception requires).  Currently, the invariant
+  // is that these fields are ALWAYS populated consistently with respect
+  // to msg_stack_ and backtrace_.
+  std::string what_;
+  std::string what_without_backtrace_;
+
+  // This is a little debugging trick: you can stash a relevant pointer
+  // in caller, and then when you catch the exception, you can compare
+  // against pointers you have on hand to get more information about
+  // where the exception came from.  In Caffe2, this is used to figure
+  // out which operator raised an exception.
+  const void* caller_;
+
+ public:
+  // PyTorch-style Error constructor.  NB: the implementation of this
+  // is actually in Logging.cpp
+  Error(SourceLocation source_location, std::string msg);
+
+  // Caffe2-style error message
+  Error(
+      const char* file,
+      const uint32_t line,
+      const char* condition,
+      const std::string& msg,
+      const std::string& backtrace,
+      const void* caller = nullptr);
+
+  // Base constructor
+  Error(std::string msg, std::string backtrace, const void* caller = nullptr);
+
+  // Add some new context to the message stack.  The last added context
+  // will be formatted at the end of the context list upon printing.
+  // WARNING: This method is O(n) in the size of the stack, so don't go
+  // wild adding a ridiculous amount of context to error messages.
+  void add_context(std::string msg);
+
+  const std::string& msg() const {
+    return msg_;
+  }
+
+  const std::vector<std::string>& context() const {
+    return context_;
+  }
+
+  const std::string& backtrace() const {
+    return backtrace_;
+  }
+
+  /// Returns the complete error message, including the source location.
+  /// The returned pointer is invalidated if you call add_context() on
+  /// this object.
+  const char* what() const noexcept override {
+    return what_.c_str();
+  }
+
+  const void* caller() const noexcept {
+    return caller_;
+  }
+
+  /// Returns only the error message string, without source location.
+  /// The returned pointer is invalidated if you call add_context() on
+  /// this object.
+  virtual const char* what_without_backtrace() const noexcept {
+    return what_without_backtrace_.c_str();
+  }
+
+ private:
+  void refresh_what();
+  std::string compute_what(bool include_backtrace) const;
+};
+
+class C10_API Warning {
+ public:
+  class C10_API UserWarning {};
+  class C10_API DeprecationWarning {};
+
+  using warning_variant_t = std::variant<UserWarning, DeprecationWarning>;
+
+  Warning(
+      warning_variant_t type,
+      const SourceLocation& source_location,
+      std::string msg,
+      bool verbatim);
+
+  Warning(
+      warning_variant_t type,
+      SourceLocation source_location,
+      const char* msg,
+      bool verbatim);
+
+  Warning(
+      warning_variant_t type,
+      SourceLocation source_location,
+      ::c10::detail::CompileTimeEmptyString msg,
+      bool verbatim);
+
+  // Getters for members
+  warning_variant_t type() const;
+  const SourceLocation& source_location() const;
+  const std::string& msg() const;
+  bool verbatim() const;
+
+ private:
+  // The type of warning
+  warning_variant_t type_;
+
+  // Where the warning happened.
+  SourceLocation source_location_;
+
+  // The actual warning message.
+  std::string msg_;
+
+  // See note: [Verbatim Warnings]
+  bool verbatim_;
+};
+
+using UserWarning = Warning::UserWarning;
+using DeprecationWarning = Warning::DeprecationWarning;
+
+// Issue a warning with a given message. Dispatched to the current
+// warning handler.
+void C10_API warn(const Warning& warning);
+
+class C10_API WarningHandler {
+ public:
+  virtual ~WarningHandler() = default;
+  /// The default warning handler. Prints the message to stderr.
+  virtual void process(const Warning& warning);
+};
+
+namespace WarningUtils {
+
+// Note: [Verbatim Warnings]
+// Warnings originating in C++ code can appear out-of-place to Python users:
+// a user runs a line in Python, but the warning references a line in C++.
+// Some parts of PyTorch, like the JIT, are cognizant of this mismatch
+// and take care to map warnings back to the user's program, but most
+// of PyTorch simply throws a context-free warning. To allow warning
+// handlers to add context where appropriate, warn takes the
+// "verbatim" flag. When this is false a warning handler might append
+// the C++ warning to a Python warning message that relates the warning
+// back to the user's program. Callers who have already accounted for
+// context in their warnings should set verbatim to true so their warnings
+// appear without modification.
+
+/// Sets the global warning handler. This is not thread-safe, so it should
+/// generally be called once during initialization or while holding the GIL
+/// for programs that use python.
+/// User is responsible for keeping the WarningHandler alive until
+/// it is not needed.
+C10_API void set_warning_handler(WarningHandler* handler) noexcept(true);
+/// Gets the global warning handler.
+C10_API WarningHandler* get_warning_handler() noexcept(true);
+
+class C10_API WarningHandlerGuard {
+  WarningHandler* prev_handler_;
+
+ public:
+  WarningHandlerGuard(WarningHandler* new_handler)
+      : prev_handler_(c10::WarningUtils::get_warning_handler()) {
+    c10::WarningUtils::set_warning_handler(new_handler);
+  }
+  ~WarningHandlerGuard() {
+    c10::WarningUtils::set_warning_handler(prev_handler_);
+  }
+};
+
+/// The TORCH_WARN_ONCE macro is difficult to test for. Use
+/// setWarnAlways(true) to turn it into TORCH_WARN, which can be
+/// tested for more easily.
+C10_API void set_warnAlways(bool) noexcept(true);
+C10_API bool get_warnAlways() noexcept(true);
+
+// A RAII guard that sets warn_always (not thread-local) on
+// construction, and sets it back to the original value upon destruction.
+struct C10_API WarnAlways {
+ public:
+  explicit WarnAlways(bool setting = true);
+  ~WarnAlways();
+
+ private:
+  bool prev_setting;
+};
+
+} // namespace WarningUtils
+
+// Like Error, but we always report the C++ backtrace, instead of only
+// reporting when TORCH_SHOW_CPP_STACKTRACES
+class C10_API ErrorAlwaysShowCppStacktrace : public Error {
+  using Error::Error;
+  const char* what_without_backtrace() const noexcept override {
+    return what();
+  }
+};
+
+// Used in ATen for out-of-bound indices that can reasonably only be detected
+// lazily inside a kernel (See: advanced indexing).  These turn into
+// IndexError when they cross to Python.
+class C10_API IndexError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for invalid values.  These turn into
+// ValueError when they cross to Python.
+class C10_API ValueError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for invalid types.  These turn into
+// TypeError when they cross to Python.
+class C10_API TypeError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for functionality that is not implemented.  These turn into
+// NotImplementedError when they cross to Python.
+class C10_API NotImplementedError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for non finite indices.  These turn into
+// ExitException when they cross to Python.
+class C10_API EnforceFiniteError : public Error {
+  using Error::Error;
+};
+
+// Used in Onnxifi backend lowering.  These turn into
+// ExitException when they cross to Python.
+class C10_API OnnxfiBackendSystemError : public Error {
+  using Error::Error;
+};
+
+// Used for numerical errors from the linalg module. These
+// turn into LinAlgError when they cross into Python.
+class C10_API LinAlgError : public Error {
+  using Error::Error;
+};
+
+class C10_API OutOfMemoryError : public Error {
+  using Error::Error;
+};
+
+// Base error type for all distributed errors.
+// These turn into DistError when they cross into Python.
+class C10_API DistError : public Error {
+  using Error::Error;
+};
+
+// Used for collective communication library errors from the distributed module.
+// These turn into DistBackendError when they cross into Python.
+class C10_API DistBackendError : public DistError {
+  using DistError::DistError;
+};
+
+// Used for errors originating from the store.
+// These turn into DistStoreError when they cross into Python.
+class C10_API DistStoreError : public DistError {
+  using DistError::DistError;
+};
+
+// Used for errors originating from the TCP/IP stack and not from collective
+// libraries. These turn into DistNetworkError when they cross into Python.
+class C10_API DistNetworkError : public DistError {
+  using DistError::DistError;
+};
+
+// A utility function to return an exception std::string by prepending its
+// exception type before its what() content
+C10_API std::string GetExceptionString(const std::exception& e);
+
+} // namespace c10
+
+// Private helper macro for implementing TORCH_INTERNAL_ASSERT and TORCH_CHECK
+//
+// Note: In the debug build With MSVC, __LINE__ might be of long type (a.k.a
+// int32_t), which is different from the definition of `SourceLocation` that
+// requires unsigned int (a.k.a uint32_t) and may cause a compile error with the
+// message: error C2397: conversion from 'long' to 'uint32_t' requires a
+// narrowing conversion Here the static cast is used to pass the build. if this
+// is used inside a lambda the __func__ macro expands to operator(), which isn't
+// very useful, but hard to fix in a macro so suppressing the warning.
+#define C10_THROW_ERROR(err_type, msg) \
+  throw ::c10::err_type(               \
+      {__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, msg)
+
+#define C10_BUILD_ERROR(err_type, msg) \
+  ::c10::err_type({__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, msg)
+
+// Private helper macro for workaround MSVC misexpansion of nested macro
+// invocations involving __VA_ARGS__.  See
+// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly
+#define C10_EXPAND_MSVC_WORKAROUND(x) x
+
+// On nvcc, C10_UNLIKELY thwarts missing return statement analysis.  In cases
+// where the unlikely expression may be a constant, use this macro to ensure
+// return statement analysis keeps working (at the cost of not getting the
+// likely/unlikely annotation on nvcc).
+// https://github.com/pytorch/pytorch/issues/21418
+//
+// Currently, this is only used in the error reporting macros below.  If you
+// want to use it more generally, move me to Macros.h
+//
+// TODO: Brian Vaughan observed that we might be able to get this to work on
+// nvcc by writing some sort of C++ overload that distinguishes constexpr inputs
+// from non-constexpr.  Since there isn't any evidence that losing C10_UNLIKELY
+// in nvcc is causing us perf problems, this is not yet implemented, but this
+// might be an interesting piece of C++ code for an intrepid bootcamper to
+// write.
+#if defined(__CUDACC__)
+#define C10_UNLIKELY_OR_CONST(e) e
+#else
+#define C10_UNLIKELY_OR_CONST(e) C10_UNLIKELY(e)
+#endif
+
+// ----------------------------------------------------------------------------
+// Error reporting macros
+// ----------------------------------------------------------------------------
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_RETHROW(e, ...) throw
+#else
+#define TORCH_RETHROW(e, ...)               \
+  do {                                      \
+    e.add_context(::c10::str(__VA_ARGS__)); \
+    throw;                                  \
+  } while (false)
+#endif
+
+// A utility macro to provide assert()-like functionality; that is, enforcement
+// of internal invariants in code.  It supports an arbitrary number of extra
+// arguments (evaluated only on failure), which will be printed in the assert
+// failure message using operator<< (this is useful to print some variables
+// which may be useful for debugging.)
+//
+// Usage:
+//    TORCH_INTERNAL_ASSERT(should_be_true);
+//    TORCH_INTERNAL_ASSERT(x == 0, "x = ", x);
+//
+// Assuming no bugs in PyTorch, the conditions tested by this macro should
+// always be true; e.g., it should be possible to disable all of these
+// conditions without changing observable user behavior.  If you would like to
+// do error reporting for user input, please use TORCH_CHECK instead.
+//
+// NOTE: It is SAFE to use this macro in production code; on failure, this
+// simply raises an exception, it does NOT unceremoniously quit the process
+// (unlike assert()).
+//
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_INTERNAL_ASSERT(cond, ...)                              \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                               \
+    ::c10::detail::torchCheckFail(                                    \
+        __func__,                                                     \
+        __FILE__,                                                     \
+        static_cast<uint32_t>(__LINE__),                              \
+        #cond " INTERNAL ASSERT FAILED at " C10_STRINGIZE(__FILE__)); \
+  }
+#else
+// It would be nice if we could build a combined string literal out of
+// the TORCH_INTERNAL_ASSERT prefix and a user-provided string literal
+// as the first argument, but there doesn't seem to be any good way to
+// do that while still supporting having a first argument that isn't a
+// string literal.
+#define TORCH_INTERNAL_ASSERT(cond, ...)                                         \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                                          \
+    ::c10::detail::torchInternalAssertFail(                                      \
+        __func__,                                                                \
+        __FILE__,                                                                \
+        static_cast<uint32_t>(__LINE__),                                         \
+        #cond                                                                    \
+        " INTERNAL ASSERT FAILED at " C10_STRINGIZE(__FILE__) ":" C10_STRINGIZE( \
+            __LINE__) ", please report a bug to PyTorch. ",                      \
+        c10::str(__VA_ARGS__));                                                  \
+  }
+#endif
+
+// A utility macro to make it easier to test for error conditions from user
+// input.  Like TORCH_INTERNAL_ASSERT, it supports an arbitrary number of extra
+// arguments (evaluated only on failure), which will be printed in the error
+// message using operator<< (e.g., you can pass any object which has
+// operator<< defined.  Most objects in PyTorch have these definitions!)
+//
+// Usage:
+//    TORCH_CHECK(should_be_true); // A default error message will be provided
+//                                 // in this case; but we recommend writing an
+//                                 // explicit error message, as it is more
+//                                 // user friendly.
+//    TORCH_CHECK(x == 0, "Expected x to be 0, but got ", x);
+//
+// On failure, this macro will raise an exception.  If this exception propagates
+// to Python, it will convert into a Python RuntimeError.
+//
+// NOTE: It is SAFE to use this macro in production code; on failure, this
+// simply raises an exception, it does NOT unceremoniously quit the process
+// (unlike CHECK() from glog.)
+//
+#define TORCH_CHECK_WITH(error_t, cond, ...) \
+  TORCH_CHECK_WITH_MSG(error_t, cond, "", __VA_ARGS__)
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_CHECK_MSG(cond, type, ...) \
+  (#cond #type " CHECK FAILED at " C10_STRINGIZE(__FILE__))
+#define TORCH_CHECK_WITH_MSG(error_t, cond, type, ...)                \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                               \
+    C10_THROW_ERROR(Error, TORCH_CHECK_MSG(cond, type, __VA_ARGS__)); \
+  }
+#else
+
+namespace c10::detail {
+template <typename... Args>
+decltype(auto) torchCheckMsgImpl(const char* /*msg*/, const Args&... args) {
+  return ::c10::str(args...);
+}
+inline C10_API const char* torchCheckMsgImpl(const char* msg) {
+  return msg;
+}
+// If there is just 1 user-provided C-string argument, use it.
+inline C10_API const char* torchCheckMsgImpl(
+    const char* /*msg*/,
+    const char* args) {
+  return args;
+}
+} // namespace c10::detail
+
+#define TORCH_CHECK_MSG(cond, type, ...)                   \
+  (::c10::detail::torchCheckMsgImpl(                       \
+      "Expected " #cond                                    \
+      " to be true, but got false.  "                      \
+      "(Could this error message be improved?  If so, "    \
+      "please report an enhancement request to PyTorch.)", \
+      ##__VA_ARGS__))
+#define TORCH_CHECK_WITH_MSG(error_t, cond, type, ...)                  \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                                 \
+    C10_THROW_ERROR(error_t, TORCH_CHECK_MSG(cond, type, __VA_ARGS__)); \
+  }
+#endif
+
+namespace c10::detail {
+
+[[noreturn]] C10_API void torchCheckFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const std::string& msg);
+[[noreturn]] C10_API void torchCheckFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* msg);
+
+// The c10::str() call that creates userMsg can have 1 of 3 return
+// types depending on the number and types of arguments passed to
+// TORCH_INTERNAL_ASSERT.  0 arguments will get a
+// CompileTimeEmptyString, 1 const char * will be passed straight
+// through, and anything else will get converted to std::string.
+[[noreturn]] C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    const char* userMsg);
+[[noreturn]] inline C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    ::c10::detail::CompileTimeEmptyString /*userMsg*/) {
+  torchCheckFail(func, file, line, condMsg);
+}
+[[noreturn]] C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    const std::string& userMsg);
+
+} // namespace c10::detail
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_CHECK(cond, ...)                   \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {          \
+    ::c10::detail::torchCheckFail(               \
+        __func__,                                \
+        __FILE__,                                \
+        static_cast<uint32_t>(__LINE__),         \
+        TORCH_CHECK_MSG(cond, "", __VA_ARGS__)); \
+  }
+#else
+#define TORCH_CHECK(cond, ...)                     \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {            \
+    ::c10::detail::torchCheckFail(                 \
+        __func__,                                  \
+        __FILE__,                                  \
+        static_cast<uint32_t>(__LINE__),           \
+        TORCH_CHECK_MSG(cond, "", ##__VA_ARGS__)); \
+  }
+#endif
+
+// An utility macro that does what `TORCH_CHECK` does if compiled in the host
+// code, otherwise does nothing. Supposed to be used in the code shared between
+// host and device code as an alternative for `TORCH_CHECK`.
+#if defined(__CUDACC__) || defined(__HIPCC__)
+#define TORCH_CHECK_IF_NOT_ON_CUDA(cond, ...)
+#else
+#define TORCH_CHECK_IF_NOT_ON_CUDA(cond, ...) TORCH_CHECK(cond, ##__VA_ARGS__)
+#endif
+
+// Debug only version of TORCH_INTERNAL_ASSERT. This macro only checks in debug
+// build, and does nothing in release build.  It is appropriate to use
+// in situations where you want to add an assert to a hotpath, but it is
+// too expensive to run this assert on production builds.
+#ifdef NDEBUG
+// Optimized version - generates no code.
+#define TORCH_INTERNAL_ASSERT_DEBUG_ONLY(...) \
+  while (false)                               \
+  C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__))
+#else
+#define TORCH_INTERNAL_ASSERT_DEBUG_ONLY(...) \
+  C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__))
+#endif
+
+// TODO: We're going to get a lot of similar looking string literals
+// this way; check if this actually affects binary size.
+
+// Like TORCH_CHECK, but raises LinAlgError instead of Error.
+#define TORCH_CHECK_LINALG(cond, ...) \
+  TORCH_CHECK_WITH_MSG(LinAlgError, cond, "LINALG", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises IndexErrors instead of Errors.
+#define TORCH_CHECK_INDEX(cond, ...) \
+  TORCH_CHECK_WITH_MSG(IndexError, cond, "INDEX", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises ValueErrors instead of Errors.
+#define TORCH_CHECK_VALUE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(ValueError, cond, "VALUE", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises TypeErrors instead of Errors.
+#define TORCH_CHECK_TYPE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(TypeError, cond, "TYPE", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises NotImplementedErrors instead of Errors.
+#define TORCH_CHECK_NOT_IMPLEMENTED(cond, ...) \
+  TORCH_CHECK_WITH_MSG(NotImplementedError, cond, "TYPE", __VA_ARGS__)
+
+#define TORCH_CHECK_ALWAYS_SHOW_CPP_STACKTRACE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(                                   \
+      ErrorAlwaysShowCppStacktrace, cond, "TYPE", ##__VA_ARGS__)
+
+#ifdef STRIP_ERROR_MESSAGES
+#define WARNING_MESSAGE_STRING(...) \
+  ::c10::detail::CompileTimeEmptyString {}
+#else
+#define WARNING_MESSAGE_STRING(...) ::c10::str(__VA_ARGS__)
+#endif
+
+// Report a warning to the user.  Accepts an arbitrary number of extra
+// arguments which are concatenated into the warning message using operator<<
+//
+#ifdef DISABLE_WARN
+#define _TORCH_WARN_WITH(...) ((void)0);
+#else
+#define _TORCH_WARN_WITH(warning_t, ...)                     \
+  ::c10::warn(::c10::Warning(                                \
+      warning_t(),                                           \
+      {__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, \
+      WARNING_MESSAGE_STRING(__VA_ARGS__),                   \
+      false));
+#endif
+
+#define TORCH_WARN(...) _TORCH_WARN_WITH(::c10::UserWarning, __VA_ARGS__);
+
+#define TORCH_WARN_DEPRECATION(...) \
+  _TORCH_WARN_WITH(::c10::DeprecationWarning, __VA_ARGS__);
+
+// Report a warning to the user only once.  Accepts an arbitrary number of extra
+// arguments which are concatenated into the warning message using operator<<
+//
+#define _TORCH_WARN_ONCE(...)                                             \
+  C10_UNUSED static const auto C10_ANONYMOUS_VARIABLE(torch_warn_once_) = \
+      [&] {                                                               \
+        TORCH_WARN(__VA_ARGS__);                                          \
+        return true;                                                      \
+      }()
+
+#ifdef DISABLE_WARN
+#define TORCH_WARN_ONCE(...) ((void)0);
+#else
+#define TORCH_WARN_ONCE(...)                   \
+  if (::c10::WarningUtils::get_warnAlways()) { \
+    TORCH_WARN(__VA_ARGS__);                   \
+  } else {                                     \
+    _TORCH_WARN_ONCE(__VA_ARGS__);             \
+  }
+#endif
+
+// Report an error with a specific argument
+// NOTE: using the argument name in TORCH_CHECK's message is preferred
+#define TORCH_CHECK_ARG(cond, argN, ...) \
+  TORCH_CHECK(cond, "invalid argument ", argN, ": ", __VA_ARGS__)
+
+// ----------------------------------------------------------------------------
+// Deprecated macros
+// ----------------------------------------------------------------------------
+
+namespace c10::detail {
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ERROR(msg) is deprecated, use TORCH_CHECK(false, msg)
+instead.")
+*/
+inline void deprecated_AT_ERROR() {}
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ASSERT is deprecated, if you mean to indicate an
+internal invariant failure, use " \
+                       "TORCH_INTERNAL_ASSERT instead; if you mean to do user
+error checking, use " \ "TORCH_CHECK.  See
+https://github.com/pytorch/pytorch/issues/20287 for more details.")
+*/
+inline void deprecated_AT_ASSERT() {}
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ASSERTM is deprecated, if you mean to indicate an
+internal invariant failure, use " \
+                       "TORCH_INTERNAL_ASSERT instead; if you mean to do user
+error checking, use " \ "TORCH_CHECK.  See
+https://github.com/pytorch/pytorch/issues/20287 for more details.")
+*/
+inline void deprecated_AT_ASSERTM() {}
+
+} // namespace c10::detail
+
+// Deprecated alias; this alias was deprecated because people kept mistakenly
+// using it for user error checking.  Use TORCH_INTERNAL_ASSERT or TORCH_CHECK
+// instead. See https://github.com/pytorch/pytorch/issues/20287 for more
+// details.
+#define AT_ASSERT(...)                                              \
+  do {                                                              \
+    ::c10::detail::deprecated_AT_ASSERT();                          \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__)); \
+  } while (false)
+
+// Deprecated alias, like AT_ASSERT.  The new TORCH_INTERNAL_ASSERT macro
+// supports both 0-ary and variadic calls, so having a separate
+// message-accepting macro is not necessary.
+//
+// NB: we MUST include cond explicitly here, as MSVC will miscompile the macro
+// expansion, shunting all of __VA_ARGS__ to cond.  An alternate workaround
+// can be seen at
+// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly
+#define AT_ASSERTM(cond, ...)                                             \
+  do {                                                                    \
+    ::c10::detail::deprecated_AT_ASSERTM();                               \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(cond, __VA_ARGS__)); \
+  } while (false)
+
+// Deprecated alias; this alias was deprecated because it represents extra API
+// surface that makes it hard for people to understand what macro to use.
+// Use TORCH_CHECK(false, ...) or TORCH_INTERNAL_ASSERT(false, ...) to
+// unconditionally fail at a line of code.
+#define AT_ERROR(...)                                                        \
+  do {                                                                       \
+    ::c10::detail::deprecated_AT_ERROR();                                    \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
+  } while (false)
+
+#endif // C10_UTIL_EXCEPTION_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/ExclusivelyOwnedTensorTraits.h

@@ -0,0 +1,75 @@
+#pragma once
+
+#include <c10/core/TensorImpl.h>
+#include <c10/core/UndefinedTensorImpl.h>
+
+#include <utility>
+
+namespace c10 {
+// Shared ExclusivelyOwnedTraits implementation between caffe2::Tensor and
+// at::TensorBase.
+template <typename TensorType>
+struct ExclusivelyOwnedTensorTraits {
+  using repr_type = TensorType;
+  using pointer_type = TensorType*;
+  using const_pointer_type = const TensorType*;
+
+  static repr_type nullRepr() {
+    return TensorType();
+  }
+
+  template <class... Args>
+  static repr_type createInPlace(Args&&... args) {
+    return TensorType(std::forward<Args>(args)...);
+  }
+
+  static repr_type moveToRepr(TensorType&& x) {
+    return std::move(x);
+  }
+
+  static void destroyOwned(TensorType& x) {
+    TensorImpl* const toDestroy = x.unsafeReleaseTensorImpl();
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy != nullptr, "Tensor somehow got null TensorImpl?");
+    // May be 0 because UndefinedTensorImpl doesn't get its refcount
+    // incremented.
+    const bool isUndefined = toDestroy == UndefinedTensorImpl::singleton();
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy->refcount_ == 1 || (toDestroy->refcount_ == 0 && isUndefined),
+        "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
+        isUndefined,
+        " and refcount ",
+        toDestroy->refcount_,
+        ", expected 1 or, if isUndefined, 0!");
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy->weakcount_ == 1 ||
+            (toDestroy->weakcount_ == 0 &&
+             toDestroy == UndefinedTensorImpl::singleton()),
+        "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
+        isUndefined,
+        " and weakcount ",
+        toDestroy->weakcount_,
+        ", expected 1 or, if isUndefined, 0!");
+    if (!isUndefined) {
+#ifndef NDEBUG
+      // Needed to pass the debug assertions in ~intrusive_ptr_target.
+      toDestroy->refcount_ = 0;
+      toDestroy->weakcount_ = 0;
+#endif
+      delete toDestroy;
+    }
+  }
+
+  static TensorType take(TensorType& x) {
+    return std::move(x);
+  }
+
+  static pointer_type getImpl(repr_type& x) {
+    return &x;
+  }
+
+  static const_pointer_type getImpl(const repr_type& x) {
+    return &x;
+  }
+};
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Float8_e5m2.h

@@ -0,0 +1,143 @@
+#pragma once
+
+/// Defines the Float8_e5m2 type (8-bit floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration:
+/// s eeeee mm
+/// 1 sign bit
+/// 5 exponent bits
+/// 2 mantissa bits
+/// bias = 15
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2209.05433.pdf
+/// and inspired by Half implementation from pytorch/c10/util/Half.h
+
+#include <c10/util/Half.h>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E5M2 format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline C10_HOST_DEVICE float fp8e5m2_to_fp32_value(uint8_t input) {
+  /*
+   * Extend the fp8 E5M2 number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEEE|MM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31 26-30 24-25          0-23
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  uint16_t half_representation = input;
+  half_representation <<= 8;
+  return fp16_ieee_to_fp32_value(half_representation);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E5M2 format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e5m2_from_fp32_value(float f) {
+  /*
+   * Binary representation of fp32 infinity
+   * 0 11111111 00000000000000000000000
+   */
+  constexpr uint32_t fp32_inf = UINT32_C(255) << 23;
+
+  /*
+   * Binary representation of 65536.0f, which is the first value
+   * not representable in fp8e5m2 range:
+   * 0 11111 00 - fp8e5m2
+   * 0 10001111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fp8_max = UINT32_C(143) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e5m2 normal range
+   * into denorm representation
+   * magic number: ((127 - 15) + (23 - 2) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(134) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+  uint8_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fp8_max) {
+    // NaN - all exponent and mantissa bits set to 1
+    result = f_bits > fp32_inf ? UINT8_C(0x7F) : UINT8_C(0x7C);
+  } else {
+    if (f_bits < (UINT32_C(113) << 23)) {
+      // Input number is smaller than 2^(-14), which is the smallest
+      // fp8e5m2 normal number
+      f_bits =
+          fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+      result = static_cast<uint8_t>(f_bits - denorm_mask);
+    } else {
+      // resulting mantissa is odd
+      uint32_t mant_odd = (f_bits >> 21) & 1;
+
+      // update exponent, rounding bias part 1
+      f_bits += ((uint32_t)(15 - 127) << 23) + 0xFFFFF;
+
+      // rounding bias part 2
+      f_bits += mant_odd;
+
+      // take the bits!
+      result = static_cast<uint8_t>(f_bits >> 21);
+    }
+  }
+
+  result |= static_cast<uint8_t>(sign >> 24);
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e5m2 {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e5m2() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e5m2(uint8_t bits, from_bits_t) : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e5m2(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+  inline C10_HOST_DEVICE bool isinf() const;
+};
+
+C10_API std::ostream& operator<<(std::ostream& out, const Float8_e5m2& value);
+
+} // namespace c10
+
+#include <c10/util/Float8_e5m2-inl.h> // IWYU pragma: keep

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Float8_e5m2fnuz.h

@@ -0,0 +1,135 @@
+#pragma once
+
+/// Defines the Float8_e5m2fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration remains the same as e5m2:
+/// s eeeee mm
+/// 1 sign bit
+/// 5 exponent bits
+/// 2 mantissa bits
+/// The key differences that e5m2fnuz brings are:
+/// bias = 16
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <c10/macros/Macros.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/floating_point_utils.h>
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E5M2 format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e5m2fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 65536.0f, which is the first value not
+   * representable (i.e. the first value which would overflow in to the sign
+   * bit, resulting in a NaN) in fp8e4m3fnuz range:
+   * 1 00000 00 - fp8e5m2fnuz
+   * 0 10001111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x8F) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e5m2fnuz normal range
+   * into denormalized representation.
+   * magic number: ((127 - 16) + (23 - 2) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x85) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x70) << 23) /* 2^-15 in float32 */) {
+    // Input exponent is less than -15, the smallest e5m2fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 21) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(16 - 127) << 23) + 0xFFFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 21);
+  }
+
+  result |= sign >> 24;
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e5m2fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e5m2fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e5m2fnuz(uint8_t bits, from_bits_t)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e5m2fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+  inline C10_HOST_DEVICE bool isinf() const;
+};
+
+C10_API std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e5m2fnuz& value);
+
+} // namespace c10
+
+#include <c10/util/Float8_e5m2fnuz-inl.h> // IWYU pragma: keep

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/IdWrapper.h

@@ -0,0 +1,77 @@
+#pragma once
+
+#include <cstddef>
+#include <functional>
+#include <utility>
+
+namespace c10 {
+
+/**
+ * This template simplifies generation of simple classes that wrap an id
+ * in a typesafe way. Namely, you can use it to create a very lightweight
+ * type that only offers equality comparators and hashing. Example:
+ *
+ *   struct MyIdType final : IdWrapper<MyIdType, uint32_t> {
+ *     constexpr explicit MyIdType(uint32_t id): IdWrapper(id) {}
+ *   };
+ *
+ * Then in the global top level namespace:
+ *
+ *   C10_DEFINE_HASH_FOR_IDWRAPPER(MyIdType);
+ *
+ * That's it - equality operators and hash functions are automatically defined
+ * for you, given the underlying type supports it.
+ */
+template <class ConcreteType, class UnderlyingType>
+class IdWrapper {
+ public:
+  using underlying_type = UnderlyingType;
+  using concrete_type = ConcreteType;
+
+ protected:
+  constexpr explicit IdWrapper(underlying_type id) noexcept(
+      noexcept(underlying_type(std::declval<underlying_type>())))
+      : id_(id) {}
+
+  constexpr underlying_type underlyingId() const
+      noexcept(noexcept(underlying_type(std::declval<underlying_type>()))) {
+    return id_;
+  }
+
+ private:
+  friend size_t hash_value(const concrete_type& v) {
+    return std::hash<underlying_type>()(v.id_);
+  }
+
+  // TODO Making operator== noexcept if underlying type is noexcept equality
+  // comparable doesn't work with GCC 4.8.
+  //      Fix this once we don't need GCC 4.8 anymore.
+  friend constexpr bool operator==(
+      const concrete_type& lhs,
+      const concrete_type& rhs) noexcept {
+    return lhs.id_ == rhs.id_;
+  }
+
+  // TODO Making operator!= noexcept if operator== is noexcept doesn't work with
+  // GCC 4.8.
+  //      Fix this once we don't need GCC 4.8 anymore.
+  friend constexpr bool operator!=(
+      const concrete_type& lhs,
+      const concrete_type& rhs) noexcept {
+    return !(lhs == rhs);
+  }
+
+  underlying_type id_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_HASH_FOR_IDWRAPPER(ClassName) \
+  namespace std {                                \
+  template <>                                    \
+  struct hash<ClassName> {                       \
+    size_t operator()(ClassName x) const {       \
+      return hash_value(x);                      \
+    }                                            \
+  };                                             \
+  }

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/LeftRight.h

@@ -0,0 +1,223 @@
+#include <c10/macros/Macros.h>
+#include <c10/util/Synchronized.h>
+#include <array>
+#include <atomic>
+#include <mutex>
+#include <thread>
+
+namespace c10 {
+
+namespace detail {
+
+struct IncrementRAII final {
+ public:
+  explicit IncrementRAII(std::atomic<int32_t>* counter) : _counter(counter) {
+    _counter->fetch_add(1);
+  }
+
+  ~IncrementRAII() {
+    _counter->fetch_sub(1);
+  }
+
+ private:
+  std::atomic<int32_t>* _counter;
+
+  C10_DISABLE_COPY_AND_ASSIGN(IncrementRAII);
+};
+
+} // namespace detail
+
+// LeftRight wait-free readers synchronization primitive
+// https://hal.archives-ouvertes.fr/hal-01207881/document
+//
+// LeftRight is quite easy to use (it can make an arbitrary
+// data structure permit wait-free reads), but it has some
+// particular performance characteristics you should be aware
+// of if you're deciding to use it:
+//
+//  - Reads still incur an atomic write (this is how LeftRight
+//    keeps track of how long it needs to keep around the old
+//    data structure)
+//
+//  - Writes get executed twice, to keep both the left and right
+//    versions up to date.  So if your write is expensive or
+//    nondeterministic, this is also an inappropriate structure
+//
+// LeftRight is used fairly rarely in PyTorch's codebase.  If you
+// are still not sure if you need it or not, consult your local
+// C++ expert.
+//
+template <class T>
+class LeftRight final {
+ public:
+  template <class... Args>
+  explicit LeftRight(const Args&... args)
+      : _counters{{{0}, {0}}},
+        _foregroundCounterIndex(0),
+        _foregroundDataIndex(0),
+        _data{{T{args...}, T{args...}}},
+        _writeMutex() {}
+
+  // Copying and moving would not be threadsafe.
+  // Needs more thought and careful design to make that work.
+  LeftRight(const LeftRight&) = delete;
+  LeftRight(LeftRight&&) noexcept = delete;
+  LeftRight& operator=(const LeftRight&) = delete;
+  LeftRight& operator=(LeftRight&&) noexcept = delete;
+
+  ~LeftRight() {
+    // wait until any potentially running writers are finished
+    { std::unique_lock<std::mutex> lock(_writeMutex); }
+
+    // wait until any potentially running readers are finished
+    while (_counters[0].load() != 0 || _counters[1].load() != 0) {
+      std::this_thread::yield();
+    }
+  }
+
+  template <typename F>
+  auto read(F&& readFunc) const {
+    detail::IncrementRAII _increment_counter(
+        &_counters[_foregroundCounterIndex.load()]);
+
+    return std::forward<F>(readFunc)(_data[_foregroundDataIndex.load()]);
+  }
+
+  // Throwing an exception in writeFunc is ok but causes the state to be either
+  // the old or the new state, depending on if the first or the second call to
+  // writeFunc threw.
+  template <typename F>
+  auto write(F&& writeFunc) {
+    std::unique_lock<std::mutex> lock(_writeMutex);
+
+    return _write(std::forward<F>(writeFunc));
+  }
+
+ private:
+  template <class F>
+  auto _write(const F& writeFunc) {
+    /*
+     * Assume, A is in background and B in foreground. In simplified terms, we
+     * want to do the following:
+     * 1. Write to A (old background)
+     * 2. Switch A/B
+     * 3. Write to B (new background)
+     *
+     * More detailed algorithm (explanations on why this is important are below
+     * in code):
+     * 1. Write to A
+     * 2. Switch A/B data pointers
+     * 3. Wait until A counter is zero
+     * 4. Switch A/B counters
+     * 5. Wait until B counter is zero
+     * 6. Write to B
+     */
+
+    auto localDataIndex = _foregroundDataIndex.load();
+
+    // 1. Write to A
+    _callWriteFuncOnBackgroundInstance(writeFunc, localDataIndex);
+
+    // 2. Switch A/B data pointers
+    localDataIndex = localDataIndex ^ 1;
+    _foregroundDataIndex = localDataIndex;
+
+    /*
+     * 3. Wait until A counter is zero
+     *
+     * In the previous write run, A was foreground and B was background.
+     * There was a time after switching _foregroundDataIndex (B to foreground)
+     * and before switching _foregroundCounterIndex, in which new readers could
+     * have read B but incremented A's counter.
+     *
+     * In this current run, we just switched _foregroundDataIndex (A back to
+     * foreground), but before writing to the new background B, we have to make
+     * sure A's counter was zero briefly, so all these old readers are gone.
+     */
+    auto localCounterIndex = _foregroundCounterIndex.load();
+    _waitForBackgroundCounterToBeZero(localCounterIndex);
+
+    /*
+     * 4. Switch A/B counters
+     *
+     * Now that we know all readers on B are really gone, we can switch the
+     * counters and have new readers increment A's counter again, which is the
+     * correct counter since they're reading A.
+     */
+    localCounterIndex = localCounterIndex ^ 1;
+    _foregroundCounterIndex = localCounterIndex;
+
+    /*
+     * 5. Wait until B counter is zero
+     *
+     * This waits for all the readers on B that came in while both data and
+     * counter for B was in foreground, i.e. normal readers that happened
+     * outside of that brief gap between switching data and counter.
+     */
+    _waitForBackgroundCounterToBeZero(localCounterIndex);
+
+    // 6. Write to B
+    return _callWriteFuncOnBackgroundInstance(writeFunc, localDataIndex);
+  }
+
+  template <class F>
+  auto _callWriteFuncOnBackgroundInstance(
+      const F& writeFunc,
+      uint8_t localDataIndex) {
+    try {
+      return writeFunc(_data[localDataIndex ^ 1]);
+    } catch (...) {
+      // recover invariant by copying from the foreground instance
+      _data[localDataIndex ^ 1] = _data[localDataIndex];
+      // rethrow
+      throw;
+    }
+  }
+
+  void _waitForBackgroundCounterToBeZero(uint8_t counterIndex) {
+    while (_counters[counterIndex ^ 1].load() != 0) {
+      std::this_thread::yield();
+    }
+  }
+
+  mutable std::array<std::atomic<int32_t>, 2> _counters;
+  std::atomic<uint8_t> _foregroundCounterIndex;
+  std::atomic<uint8_t> _foregroundDataIndex;
+  std::array<T, 2> _data;
+  std::mutex _writeMutex;
+};
+
+// RWSafeLeftRightWrapper is API compatible with LeftRight and uses a
+// read-write lock to protect T (data).
+template <class T>
+class RWSafeLeftRightWrapper final {
+ public:
+  template <class... Args>
+  explicit RWSafeLeftRightWrapper(const Args&... args) : data_{args...} {}
+
+  // RWSafeLeftRightWrapper is not copyable or moveable since LeftRight
+  // is not copyable or moveable.
+  RWSafeLeftRightWrapper(const RWSafeLeftRightWrapper&) = delete;
+  RWSafeLeftRightWrapper(RWSafeLeftRightWrapper&&) noexcept = delete;
+  RWSafeLeftRightWrapper& operator=(const RWSafeLeftRightWrapper&) = delete;
+  RWSafeLeftRightWrapper& operator=(RWSafeLeftRightWrapper&&) noexcept = delete;
+
+  template <typename F>
+  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+  auto read(F&& readFunc) const {
+    return data_.withLock(
+        [&readFunc](T const& data) { return std::forward<F>(readFunc)(data); });
+  }
+
+  template <typename F>
+  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+  auto write(F&& writeFunc) {
+    return data_.withLock(
+        [&writeFunc](T& data) { return std::forward<F>(writeFunc)(data); });
+  }
+
+ private:
+  c10::Synchronized<T> data_;
+};
+
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Optional.h

@@ -0,0 +1,48 @@
+#ifndef C10_UTIL_OPTIONAL_H_
+#define C10_UTIL_OPTIONAL_H_
+
+#include <optional>
+#include <type_traits>
+
+// Macros.h is not needed, but it does namespace shenanigans that lots
+// of downstream code seems to rely on. Feel free to remove it and fix
+// up builds.
+
+namespace c10 {
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::bad_optional_access;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::make_optional;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::nullopt;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::nullopt_t;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::optional;
+
+namespace detail_ {
+// the call to convert<A>(b) has return type A and converts b to type A iff b
+// decltype(b) is implicitly convertible to A
+template <class U>
+constexpr U convert(U v) {
+  return v;
+}
+} // namespace detail_
+template <class T, class F>
+constexpr T value_or_else(const optional<T>& v, F&& func) {
+  static_assert(
+      std::is_convertible_v<typename std::invoke_result_t<F>, T>,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? *v : detail_::convert<T>(std::forward<F>(func)());
+}
+
+template <class T, class F>
+constexpr T value_or_else(optional<T>&& v, F&& func) {
+  static_assert(
+      std::is_convertible_v<typename std::invoke_result_t<F>, T>,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? constexpr_move(std::move(v).contained_val())
+                       : detail_::convert<T>(std::forward<F>(func)());
+}
+} // namespace c10
+#endif // C10_UTIL_OPTIONAL_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/OptionalArrayRef.h

@@ -0,0 +1,236 @@
+// This file defines OptionalArrayRef<T>, a class that has almost the same
+// exact functionality as c10::optional<ArrayRef<T>>, except that its
+// converting constructor fixes a dangling pointer issue.
+//
+// The implicit converting constructor of both c10::optional<ArrayRef<T>> and
+// std::optional<ArrayRef<T>> can cause the underlying ArrayRef<T> to store
+// a dangling pointer. OptionalArrayRef<T> prevents this by wrapping
+// a c10::optional<ArrayRef<T>> and fixing the constructor implementation.
+//
+// See https://github.com/pytorch/pytorch/issues/63645 for more on this.
+
+#pragma once
+
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Optional.h>
+#include <cstdint>
+#include <initializer_list>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+template <typename T>
+class OptionalArrayRef final {
+ public:
+  // Constructors
+
+  constexpr OptionalArrayRef() noexcept = default;
+
+  constexpr OptionalArrayRef(nullopt_t) noexcept {}
+
+  OptionalArrayRef(const OptionalArrayRef& other) = default;
+
+  OptionalArrayRef(OptionalArrayRef&& other) noexcept = default;
+
+  constexpr OptionalArrayRef(const optional<ArrayRef<T>>& other) noexcept
+      : wrapped_opt_array_ref(other) {}
+
+  constexpr OptionalArrayRef(optional<ArrayRef<T>>&& other) noexcept
+      : wrapped_opt_array_ref(std::move(other)) {}
+
+  constexpr OptionalArrayRef(const T& value) noexcept
+      : wrapped_opt_array_ref(value) {}
+
+  template <
+      typename U = ArrayRef<T>,
+      std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+              !std::is_same_v<std::decay_t<U>, std::in_place_t> &&
+              std::is_constructible_v<ArrayRef<T>, U&&> &&
+              std::is_convertible_v<U&&, ArrayRef<T>> &&
+              !std::is_convertible_v<U&&, T>,
+          bool> = false>
+  constexpr OptionalArrayRef(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&>)
+      : wrapped_opt_array_ref(std::forward<U>(value)) {}
+
+  template <
+      typename U = ArrayRef<T>,
+      std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+              !std::is_same_v<std::decay_t<U>, std::in_place_t> &&
+              std::is_constructible_v<ArrayRef<T>, U&&> &&
+              !std::is_convertible_v<U&&, ArrayRef<T>>,
+          bool> = false>
+  constexpr explicit OptionalArrayRef(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&>)
+      : wrapped_opt_array_ref(std::forward<U>(value)) {}
+
+  template <typename... Args>
+  constexpr explicit OptionalArrayRef(
+      std::in_place_t ip,
+      Args&&... args) noexcept
+      : wrapped_opt_array_ref(ip, std::forward<Args>(args)...) {}
+
+  template <typename U, typename... Args>
+  constexpr explicit OptionalArrayRef(
+      std::in_place_t ip,
+      std::initializer_list<U> il,
+      Args&&... args)
+      : wrapped_opt_array_ref(ip, il, std::forward<Args>(args)...) {}
+
+  constexpr OptionalArrayRef(const std::initializer_list<T>& Vec)
+      : wrapped_opt_array_ref(ArrayRef<T>(Vec)) {}
+
+  // Destructor
+
+  ~OptionalArrayRef() = default;
+
+  // Assignment
+
+  constexpr OptionalArrayRef& operator=(nullopt_t) noexcept {
+    wrapped_opt_array_ref = c10::nullopt;
+    return *this;
+  }
+
+  OptionalArrayRef& operator=(const OptionalArrayRef& other) = default;
+
+  OptionalArrayRef& operator=(OptionalArrayRef&& other) noexcept = default;
+
+  constexpr OptionalArrayRef& operator=(
+      const optional<ArrayRef<T>>& other) noexcept {
+    wrapped_opt_array_ref = other;
+    return *this;
+  }
+
+  constexpr OptionalArrayRef& operator=(
+      optional<ArrayRef<T>>&& other) noexcept {
+    wrapped_opt_array_ref = std::move(other);
+    return *this;
+  }
+
+  template <
+      typename U = ArrayRef<T>,
+      typename = std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+          std::is_constructible_v<ArrayRef<T>, U&&> &&
+          std::is_assignable_v<ArrayRef<T>&, U&&>>>
+  constexpr OptionalArrayRef& operator=(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&> &&
+      std::is_nothrow_assignable_v<ArrayRef<T>&, U&&>) {
+    wrapped_opt_array_ref = std::forward<U>(value);
+    return *this;
+  }
+
+  // Observers
+
+  constexpr ArrayRef<T>* operator->() noexcept {
+    return &wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>* operator->() const noexcept {
+    return &wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>& operator*() & noexcept {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>& operator*() const& noexcept {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>&& operator*() && noexcept {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr const ArrayRef<T>&& operator*() const&& noexcept {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr explicit operator bool() const noexcept {
+    return wrapped_opt_array_ref.has_value();
+  }
+
+  constexpr bool has_value() const noexcept {
+    return wrapped_opt_array_ref.has_value();
+  }
+
+  constexpr ArrayRef<T>& value() & {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>& value() const& {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>&& value() && {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr const ArrayRef<T>&& value() const&& {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  template <typename U>
+  constexpr std::
+      enable_if_t<std::is_convertible_v<U&&, ArrayRef<T>>, ArrayRef<T>>
+      value_or(U&& default_value) const& {
+    return wrapped_opt_array_ref.value_or(std::forward<U>(default_value));
+  }
+
+  template <typename U>
+  constexpr std::
+      enable_if_t<std::is_convertible_v<U&&, ArrayRef<T>>, ArrayRef<T>>
+      value_or(U&& default_value) && {
+    return wrapped_opt_array_ref.value_or(std::forward<U>(default_value));
+  }
+
+  // Modifiers
+
+  constexpr void swap(OptionalArrayRef& other) noexcept {
+    std::swap(wrapped_opt_array_ref, other.wrapped_opt_array_ref);
+  }
+
+  constexpr void reset() noexcept {
+    wrapped_opt_array_ref.reset();
+  }
+
+  template <typename... Args>
+  constexpr std::
+      enable_if_t<std::is_constructible_v<ArrayRef<T>, Args&&...>, ArrayRef<T>&>
+      emplace(Args&&... args) noexcept(
+          std::is_nothrow_constructible_v<ArrayRef<T>, Args&&...>) {
+    return wrapped_opt_array_ref.emplace(std::forward<Args>(args)...);
+  }
+
+  template <typename U, typename... Args>
+  constexpr ArrayRef<T>& emplace(
+      std::initializer_list<U> il,
+      Args&&... args) noexcept {
+    return wrapped_opt_array_ref.emplace(il, std::forward<Args>(args)...);
+  }
+
+ private:
+  optional<ArrayRef<T>> wrapped_opt_array_ref;
+};
+
+using OptionalIntArrayRef = OptionalArrayRef<int64_t>;
+
+inline bool operator==(
+    const OptionalIntArrayRef& a1,
+    const IntArrayRef& other) {
+  if (!a1.has_value()) {
+    return false;
+  }
+  return a1.value() == other;
+}
+
+inline bool operator==(
+    const c10::IntArrayRef& a1,
+    const c10::OptionalIntArrayRef& a2) {
+  return a2 == a1;
+}
+
+} // namespace c10

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/Registry.h

@@ -0,0 +1,326 @@
+#ifndef C10_UTIL_REGISTRY_H_
+#define C10_UTIL_REGISTRY_H_
+
+/**
+ * Simple registry implementation that uses static variables to
+ * register object creators during program initialization time.
+ */
+
+// NB: This Registry works poorly when you have other namespaces.
+// Make all macro invocations from inside the at namespace.
+
+#include <cstdio>
+#include <cstdlib>
+#include <functional>
+#include <memory>
+#include <mutex>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Type.h>
+
+namespace c10 {
+
+template <typename KeyType>
+inline std::string KeyStrRepr(const KeyType& /*key*/) {
+  return "[key type printing not supported]";
+}
+
+template <>
+inline std::string KeyStrRepr(const std::string& key) {
+  return key;
+}
+
+enum RegistryPriority {
+  REGISTRY_FALLBACK = 1,
+  REGISTRY_DEFAULT = 2,
+  REGISTRY_PREFERRED = 3,
+};
+
+/**
+ * @brief A template class that allows one to register classes by keys.
+ *
+ * The keys are usually a std::string specifying the name, but can be anything
+ * that can be used in a std::map.
+ *
+ * You should most likely not use the Registry class explicitly, but use the
+ * helper macros below to declare specific registries as well as registering
+ * objects.
+ */
+template <class SrcType, class ObjectPtrType, class... Args>
+class Registry {
+ public:
+  typedef std::function<ObjectPtrType(Args...)> Creator;
+
+  Registry(bool warning = true) : registry_(), priority_(), warning_(warning) {}
+
+  void Register(
+      const SrcType& key,
+      Creator creator,
+      const RegistryPriority priority = REGISTRY_DEFAULT) {
+    std::lock_guard<std::mutex> lock(register_mutex_);
+    // The if statement below is essentially the same as the following line:
+    // TORCH_CHECK_EQ(registry_.count(key), 0) << "Key " << key
+    //                                   << " registered twice.";
+    // However, TORCH_CHECK_EQ depends on google logging, and since registration
+    // is carried out at static initialization time, we do not want to have an
+    // explicit dependency on glog's initialization function.
+    if (registry_.count(key) != 0) {
+      auto cur_priority = priority_[key];
+      if (priority > cur_priority) {
+#ifdef DEBUG
+        std::string warn_msg =
+            "Overwriting already registered item for key " + KeyStrRepr(key);
+        fprintf(stderr, "%s\n", warn_msg.c_str());
+#endif
+        registry_[key] = creator;
+        priority_[key] = priority;
+      } else if (priority == cur_priority) {
+        std::string err_msg =
+            "Key already registered with the same priority: " + KeyStrRepr(key);
+        fprintf(stderr, "%s\n", err_msg.c_str());
+        if (terminate_) {
+          std::exit(1);
+        } else {
+          throw std::runtime_error(err_msg);
+        }
+      } else if (warning_) {
+        std::string warn_msg =
+            "Higher priority item already registered, skipping registration of " +
+            KeyStrRepr(key);
+        fprintf(stderr, "%s\n", warn_msg.c_str());
+      }
+    } else {
+      registry_[key] = creator;
+      priority_[key] = priority;
+    }
+  }
+
+  void Register(
+      const SrcType& key,
+      Creator creator,
+      const std::string& help_msg,
+      const RegistryPriority priority = REGISTRY_DEFAULT) {
+    Register(key, creator, priority);
+    help_message_[key] = help_msg;
+  }
+
+  inline bool Has(const SrcType& key) {
+    return (registry_.count(key) != 0);
+  }
+
+  ObjectPtrType Create(const SrcType& key, Args... args) {
+    auto it = registry_.find(key);
+    if (it == registry_.end()) {
+      // Returns nullptr if the key is not registered.
+      return nullptr;
+    }
+    return it->second(args...);
+  }
+
+  /**
+   * Returns the keys currently registered as a std::vector.
+   */
+  std::vector<SrcType> Keys() const {
+    std::vector<SrcType> keys;
+    keys.reserve(registry_.size());
+    for (const auto& it : registry_) {
+      keys.push_back(it.first);
+    }
+    return keys;
+  }
+
+  inline const std::unordered_map<SrcType, std::string>& HelpMessage() const {
+    return help_message_;
+  }
+
+  const char* HelpMessage(const SrcType& key) const {
+    auto it = help_message_.find(key);
+    if (it == help_message_.end()) {
+      return nullptr;
+    }
+    return it->second.c_str();
+  }
+
+  // Used for testing, if terminate is unset, Registry throws instead of
+  // calling std::exit
+  void SetTerminate(bool terminate) {
+    terminate_ = terminate;
+  }
+
+ private:
+  std::unordered_map<SrcType, Creator> registry_;
+  std::unordered_map<SrcType, RegistryPriority> priority_;
+  bool terminate_{true};
+  const bool warning_;
+  std::unordered_map<SrcType, std::string> help_message_;
+  std::mutex register_mutex_;
+
+  C10_DISABLE_COPY_AND_ASSIGN(Registry);
+};
+
+template <class SrcType, class ObjectPtrType, class... Args>
+class Registerer {
+ public:
+  explicit Registerer(
+      const SrcType& key,
+      Registry<SrcType, ObjectPtrType, Args...>* registry,
+      typename Registry<SrcType, ObjectPtrType, Args...>::Creator creator,
+      const std::string& help_msg = "") {
+    registry->Register(key, creator, help_msg);
+  }
+
+  explicit Registerer(
+      const SrcType& key,
+      const RegistryPriority priority,
+      Registry<SrcType, ObjectPtrType, Args...>* registry,
+      typename Registry<SrcType, ObjectPtrType, Args...>::Creator creator,
+      const std::string& help_msg = "") {
+    registry->Register(key, creator, help_msg, priority);
+  }
+
+  template <class DerivedType>
+  static ObjectPtrType DefaultCreator(Args... args) {
+    return ObjectPtrType(new DerivedType(args...));
+  }
+};
+
+/**
+ * C10_DECLARE_TYPED_REGISTRY is a macro that expands to a function
+ * declaration, as well as creating a convenient typename for its corresponding
+ * registerer.
+ */
+// Note on C10_IMPORT and C10_EXPORT below: we need to explicitly mark DECLARE
+// as import and DEFINE as export, because these registry macros will be used
+// in downstream shared libraries as well, and one cannot use *_API - the API
+// macro will be defined on a per-shared-library basis. Semantically, when one
+// declares a typed registry it is always going to be IMPORT, and when one
+// defines a registry (which should happen ONLY ONCE and ONLY IN SOURCE FILE),
+// the instantiation unit is always going to be exported.
+//
+// The only unique condition is when in the same file one does DECLARE and
+// DEFINE - in Windows compilers, this generates a warning that dllimport and
+// dllexport are mixed, but the warning is fine and linker will be properly
+// exporting the symbol. Same thing happens in the gflags flag declaration and
+// definition caes.
+#define C10_DECLARE_TYPED_REGISTRY(                                      \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                     \
+  C10_API ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*  \
+  RegistryName();                                                        \
+  typedef ::c10::Registerer<SrcType, PtrType<ObjectType>, ##__VA_ARGS__> \
+      Registerer##RegistryName
+
+#define TORCH_DECLARE_TYPED_REGISTRY(                                     \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                      \
+  TORCH_API ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>* \
+  RegistryName();                                                         \
+  typedef ::c10::Registerer<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>  \
+      Registerer##RegistryName
+
+#define C10_DEFINE_TYPED_REGISTRY(                                         \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                       \
+  C10_EXPORT ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>* \
+  RegistryName() {                                                         \
+    static ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*   \
+        registry = new ::c10::                                             \
+            Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>();       \
+    return registry;                                                       \
+  }
+
+#define C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(                            \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                          \
+  C10_EXPORT ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*    \
+  RegistryName() {                                                            \
+    static ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*      \
+        registry =                                                            \
+            new ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>( \
+                false);                                                       \
+    return registry;                                                          \
+  }
+
+// Note(Yangqing): The __VA_ARGS__ below allows one to specify a templated
+// creator with comma in its templated arguments.
+#define C10_REGISTER_TYPED_CREATOR(RegistryName, key, ...)                  \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key, RegistryName(), ##__VA_ARGS__);
+
+#define C10_REGISTER_TYPED_CREATOR_WITH_PRIORITY(                           \
+    RegistryName, key, priority, ...)                                       \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key, priority, RegistryName(), ##__VA_ARGS__);
+
+#define C10_REGISTER_TYPED_CLASS(RegistryName, key, ...)                    \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key,                                                                  \
+      RegistryName(),                                                       \
+      Registerer##RegistryName::DefaultCreator<__VA_ARGS__>,                \
+      ::c10::demangle_type<__VA_ARGS__>());
+
+#define C10_REGISTER_TYPED_CLASS_WITH_PRIORITY(                             \
+    RegistryName, key, priority, ...)                                       \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key,                                                                  \
+      priority,                                                             \
+      RegistryName(),                                                       \
+      Registerer##RegistryName::DefaultCreator<__VA_ARGS__>,                \
+      ::c10::demangle_type<__VA_ARGS__>());
+
+// C10_DECLARE_REGISTRY and C10_DEFINE_REGISTRY are hard-wired to use
+// std::string as the key type, because that is the most commonly used cases.
+#define C10_DECLARE_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DECLARE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define TORCH_DECLARE_REGISTRY(RegistryName, ObjectType, ...) \
+  TORCH_DECLARE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_REGISTRY_WITHOUT_WARNING(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DECLARE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DECLARE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define TORCH_DECLARE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  TORCH_DECLARE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_SHARED_REGISTRY_WITHOUT_WARNING( \
+    RegistryName, ObjectType, ...)                  \
+  C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(        \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+// C10_REGISTER_CREATOR and C10_REGISTER_CLASS are hard-wired to use std::string
+// as the key
+// type, because that is the most commonly used cases.
+#define C10_REGISTER_CREATOR(RegistryName, key, ...) \
+  C10_REGISTER_TYPED_CREATOR(RegistryName, #key, __VA_ARGS__)
+
+#define C10_REGISTER_CREATOR_WITH_PRIORITY(RegistryName, key, priority, ...) \
+  C10_REGISTER_TYPED_CREATOR_WITH_PRIORITY(                                  \
+      RegistryName, #key, priority, __VA_ARGS__)
+
+#define C10_REGISTER_CLASS(RegistryName, key, ...) \
+  C10_REGISTER_TYPED_CLASS(RegistryName, #key, __VA_ARGS__)
+
+#define C10_REGISTER_CLASS_WITH_PRIORITY(RegistryName, key, priority, ...) \
+  C10_REGISTER_TYPED_CLASS_WITH_PRIORITY(                                  \
+      RegistryName, #key, priority, __VA_ARGS__)
+
+} // namespace c10
+
+#endif // C10_UTIL_REGISTRY_H_

llmeval-env/lib/python3.10/site-packages/torch/include/c10/util/SmallBuffer.h

@@ -0,0 +1,87 @@
+#pragma once
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <type_traits>
+
+/** Helper class for allocating temporary fixed size arrays with SBO.
+ *
+ * This is intentionally much simpler than SmallVector, to improve performance
+ * at the expense of many features:
+ * - No zero-initialization for numeric types
+ * - No resizing after construction
+ * - No copy/move
+ * - No non-trivial types
+ */
+
+namespace c10 {
+
+template <typename T, size_t N>
+class SmallBuffer {
+  static_assert(std::is_trivial_v<T>, "SmallBuffer is intended for POD types");
+
+  std::array<T, N> storage_;
+  size_t size_{};
+  T* data_{};
+
+ public:
+  SmallBuffer(size_t size) : size_(size) {
+    if (size > N) {
+      data_ = new T[size];
+    } else {
+      data_ = &storage_[0];
+    }
+  }
+
+  SmallBuffer(const SmallBuffer&) = delete;
+  SmallBuffer& operator=(const SmallBuffer&) = delete;
+
+  // move constructor is needed in function return
+  SmallBuffer(SmallBuffer&& rhs) noexcept : size_{rhs.size_} {
+    rhs.size_ = 0;
+    if (size_ > N) {
+      data_ = rhs.data_;
+      rhs.data_ = nullptr;
+    } else {
+      storage_ = std::move(rhs.storage_);
+      data_ = &storage_[0];
+    }
+  }
+
+  SmallBuffer& operator=(SmallBuffer&&) = delete;
+
+  ~SmallBuffer() {
+    if (size_ > N) {
+      delete[] data_;
+    }
+  }
+  T& operator[](size_t idx) {
+    return data()[idx];
+  }
+  const T& operator[](size_t idx) const {
+    return data()[idx];
+  }
+  T* data() {
+    return data_;
+  }
+  const T* data() const {
+    return data_;
+  }
+  size_t size() const {
+    return size_;
+  }
+  T* begin() {
+    return data_;
+  }
+  const T* begin() const {
+    return data_;
+  }
+  T* end() {
+    return data_ + size_;
+  }
+  const T* end() const {
+    return data_ + size_;
+  }
+};
+
+} // namespace c10