Add files using upload-large-folder tool

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

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+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractmethod
+from argparse import _SubParsersAction
+
+
+class BaseHuggingfaceCLICommand(ABC):
+    @staticmethod
+    @abstractmethod
+    def register_subcommand(parser: _SubParsersAction):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def run(self):
+        raise NotImplementedError()

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/_cli_utils.py

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+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a utility for good-looking prints."""
+
+import os
+from typing import List, Union
+
+
+class ANSI:
+    """
+    Helper for en.wikipedia.org/wiki/ANSI_escape_code
+    """
+
+    _bold = "\u001b[1m"
+    _gray = "\u001b[90m"
+    _red = "\u001b[31m"
+    _reset = "\u001b[0m"
+
+    @classmethod
+    def bold(cls, s: str) -> str:
+        return cls._format(s, cls._bold)
+
+    @classmethod
+    def gray(cls, s: str) -> str:
+        return cls._format(s, cls._gray)
+
+    @classmethod
+    def red(cls, s: str) -> str:
+        return cls._format(s, cls._bold + cls._red)
+
+    @classmethod
+    def _format(cls, s: str, code: str) -> str:
+        if os.environ.get("NO_COLOR"):
+            # See https://no-color.org/
+            return s
+        return f"{code}{s}{cls._reset}"
+
+
+def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str:
+    """
+    Inspired by:
+
+    - stackoverflow.com/a/8356620/593036
+    - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
+    """
+    col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
+    row_format = ("{{:{}}} " * len(headers)).format(*col_widths)
+    lines = []
+    lines.append(row_format.format(*headers))
+    lines.append(row_format.format(*["-" * w for w in col_widths]))
+    for row in rows:
+        lines.append(row_format.format(*row))
+    return "\n".join(lines)

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/delete_cache.py

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+# coding=utf-8
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to delete some revisions from the HF cache directory.
+
+Usage:
+    huggingface-cli delete-cache
+    huggingface-cli delete-cache --disable-tui
+    huggingface-cli delete-cache --dir ~/.cache/huggingface/hub
+
+NOTE:
+    This command is based on `InquirerPy` to build the multiselect menu in the terminal.
+    This dependency has to be installed with `pip install huggingface_hub[cli]`. Since
+    we want to avoid as much as possible cross-platform issues, I chose a library that
+    is built on top of `python-prompt-toolkit` which seems to be a reference in terminal
+    GUI (actively maintained on both Unix and Windows, 7.9k stars).
+
+    For the moment, the TUI feature is in beta.
+
+    See:
+    - https://github.com/kazhala/InquirerPy
+    - https://inquirerpy.readthedocs.io/en/latest/
+    - https://github.com/prompt-toolkit/python-prompt-toolkit
+
+    Other solutions could have been:
+    - `simple_term_menu`: would be good as well for our use case but some issues suggest
+      that Windows is less supported.
+      See: https://github.com/IngoMeyer441/simple-term-menu
+    - `PyInquirer`: very similar to `InquirerPy` but older and not maintained anymore.
+      In particular, no support of Python3.10.
+      See: https://github.com/CITGuru/PyInquirer
+    - `pick` (or `pickpack`): easy to use and flexible but built on top of Python's
+      standard library `curses` that is specific to Unix (not implemented on Windows).
+      See https://github.com/wong2/pick and https://github.com/anafvana/pickpack.
+    - `inquirer`: lot of traction (700 stars) but explicitly states "experimental
+      support of Windows". Not built on top of `python-prompt-toolkit`.
+      See https://github.com/magmax/python-inquirer
+
+TODO: add support for `huggingface-cli delete-cache aaaaaa bbbbbb cccccc (...)` ?
+TODO: add "--keep-last" arg to delete revisions that are not on `main` ref
+TODO: add "--filter" arg to filter repositories by name ?
+TODO: add "--sort" arg to sort by size ?
+TODO: add "--limit" arg to limit to X repos ?
+TODO: add "-y" arg for immediate deletion ?
+See discussions in https://github.com/huggingface/huggingface_hub/issues/1025.
+"""
+
+import os
+from argparse import Namespace, _SubParsersAction
+from functools import wraps
+from tempfile import mkstemp
+from typing import Any, Callable, Iterable, List, Optional, Union
+
+from ..utils import CachedRepoInfo, CachedRevisionInfo, HFCacheInfo, scan_cache_dir
+from . import BaseHuggingfaceCLICommand
+from ._cli_utils import ANSI
+
+
+try:
+    from InquirerPy import inquirer
+    from InquirerPy.base.control import Choice
+    from InquirerPy.separator import Separator
+
+    _inquirer_py_available = True
+except ImportError:
+    _inquirer_py_available = False
+
+
+def require_inquirer_py(fn: Callable) -> Callable:
+    """Decorator to flag methods that require `InquirerPy`."""
+
+    # TODO: refactor this + imports in a unified pattern across codebase
+    @wraps(fn)
+    def _inner(*args, **kwargs):
+        if not _inquirer_py_available:
+            raise ImportError(
+                "The `delete-cache` command requires extra dependencies to work with"
+                " the TUI.\nPlease run `pip install huggingface_hub[cli]` to install"
+                " them.\nOtherwise, disable TUI using the `--disable-tui` flag."
+            )
+
+        return fn(*args, **kwargs)
+
+    return _inner
+
+
+# Possibility for the user to cancel deletion
+_CANCEL_DELETION_STR = "CANCEL_DELETION"
+
+
+class DeleteCacheCommand(BaseHuggingfaceCLICommand):
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        delete_cache_parser = parser.add_parser("delete-cache", help="Delete revisions from the cache directory.")
+
+        delete_cache_parser.add_argument(
+            "--dir",
+            type=str,
+            default=None,
+            help="cache directory (optional). Default to the default HuggingFace cache.",
+        )
+
+        delete_cache_parser.add_argument(
+            "--disable-tui",
+            action="store_true",
+            help=(
+                "Disable Terminal User Interface (TUI) mode. Useful if your"
+                " platform/terminal doesn't support the multiselect menu."
+            ),
+        )
+
+        delete_cache_parser.set_defaults(func=DeleteCacheCommand)
+
+    def __init__(self, args: Namespace) -> None:
+        self.cache_dir: Optional[str] = args.dir
+        self.disable_tui: bool = args.disable_tui
+
+    def run(self):
+        """Run `delete-cache` command with or without TUI."""
+        # Scan cache directory
+        hf_cache_info = scan_cache_dir(self.cache_dir)
+
+        # Manual review from the user
+        if self.disable_tui:
+            selected_hashes = _manual_review_no_tui(hf_cache_info, preselected=[])
+        else:
+            selected_hashes = _manual_review_tui(hf_cache_info, preselected=[])
+
+        # If deletion is not cancelled
+        if len(selected_hashes) > 0 and _CANCEL_DELETION_STR not in selected_hashes:
+            confirm_message = _get_expectations_str(hf_cache_info, selected_hashes) + " Confirm deletion ?"
+
+            # Confirm deletion
+            if self.disable_tui:
+                confirmed = _ask_for_confirmation_no_tui(confirm_message)
+            else:
+                confirmed = _ask_for_confirmation_tui(confirm_message)
+
+            # Deletion is confirmed
+            if confirmed:
+                strategy = hf_cache_info.delete_revisions(*selected_hashes)
+                print("Start deletion.")
+                strategy.execute()
+                print(
+                    f"Done. Deleted {len(strategy.repos)} repo(s) and"
+                    f" {len(strategy.snapshots)} revision(s) for a total of"
+                    f" {strategy.expected_freed_size_str}."
+                )
+                return
+
+        # Deletion is cancelled
+        print("Deletion is cancelled. Do nothing.")
+
+
+@require_inquirer_py
+def _manual_review_tui(hf_cache_info: HFCacheInfo, preselected: List[str]) -> List[str]:
+    """Ask the user for a manual review of the revisions to delete.
+
+    Displays a multi-select menu in the terminal (TUI).
+    """
+    # Define multiselect list
+    choices = _get_tui_choices_from_scan(repos=hf_cache_info.repos, preselected=preselected)
+    checkbox = inquirer.checkbox(
+        message="Select revisions to delete:",
+        choices=choices,  # List of revisions with some pre-selection
+        cycle=False,  # No loop between top and bottom
+        height=100,  # Large list if possible
+        # We use the instruction to display to the user the expected effect of the
+        # deletion.
+        instruction=_get_expectations_str(
+            hf_cache_info,
+            selected_hashes=[c.value for c in choices if isinstance(c, Choice) and c.enabled],
+        ),
+        # We use the long instruction to should keybindings instructions to the user
+        long_instruction="Press <space> to select, <enter> to validate and <ctrl+c> to quit without modification.",
+        # Message that is displayed once the user validates its selection.
+        transformer=lambda result: f"{len(result)} revision(s) selected.",
+    )
+
+    # Add a callback to update the information line when a revision is
+    # selected/unselected
+    def _update_expectations(_) -> None:
+        # Hacky way to dynamically set an instruction message to the checkbox when
+        # a revision hash is selected/unselected.
+        checkbox._instruction = _get_expectations_str(
+            hf_cache_info,
+            selected_hashes=[choice["value"] for choice in checkbox.content_control.choices if choice["enabled"]],
+        )
+
+    checkbox.kb_func_lookup["toggle"].append({"func": _update_expectations})
+
+    # Finally display the form to the user.
+    try:
+        return checkbox.execute()
+    except KeyboardInterrupt:
+        return []  # Quit without deletion
+
+
+@require_inquirer_py
+def _ask_for_confirmation_tui(message: str, default: bool = True) -> bool:
+    """Ask for confirmation using Inquirer."""
+    return inquirer.confirm(message, default=default).execute()
+
+
+def _get_tui_choices_from_scan(repos: Iterable[CachedRepoInfo], preselected: List[str]) -> List:
+    """Build a list of choices from the scanned repos.
+
+    Args:
+        repos (*Iterable[`CachedRepoInfo`]*):
+            List of scanned repos on which we want to delete revisions.
+        preselected (*List[`str`]*):
+            List of revision hashes that will be preselected.
+
+    Return:
+        The list of choices to pass to `inquirer.checkbox`.
+    """
+    choices: List[Union[Choice, Separator]] = []
+
+    # First choice is to cancel the deletion. If selected, nothing will be deleted,
+    # no matter the other selected items.
+    choices.append(
+        Choice(
+            _CANCEL_DELETION_STR,
+            name="None of the following (if selected, nothing will be deleted).",
+            enabled=False,
+        )
+    )
+
+    # Display a separator per repo and a Choice for each revisions of the repo
+    for repo in sorted(repos, key=_repo_sorting_order):
+        # Repo as separator
+        choices.append(
+            Separator(
+                f"\n{repo.repo_type.capitalize()} {repo.repo_id} ({repo.size_on_disk_str},"
+                f" used {repo.last_accessed_str})"
+            )
+        )
+        for revision in sorted(repo.revisions, key=_revision_sorting_order):
+            # Revision as choice
+            choices.append(
+                Choice(
+                    revision.commit_hash,
+                    name=(
+                        f"{revision.commit_hash[:8]}:"
+                        f" {', '.join(sorted(revision.refs)) or '(detached)'} #"
+                        f" modified {revision.last_modified_str}"
+                    ),
+                    enabled=revision.commit_hash in preselected,
+                )
+            )
+
+    # Return choices
+    return choices
+
+
+def _manual_review_no_tui(hf_cache_info: HFCacheInfo, preselected: List[str]) -> List[str]:
+    """Ask the user for a manual review of the revisions to delete.
+
+    Used when TUI is disabled. Manual review happens in a separate tmp file that the
+    user can manually edit.
+    """
+    # 1. Generate temporary file with delete commands.
+    fd, tmp_path = mkstemp(suffix=".txt")  # suffix to make it easier to find by editors
+    os.close(fd)
+
+    lines = []
+    for repo in sorted(hf_cache_info.repos, key=_repo_sorting_order):
+        lines.append(
+            f"\n# {repo.repo_type.capitalize()} {repo.repo_id} ({repo.size_on_disk_str},"
+            f" used {repo.last_accessed_str})"
+        )
+        for revision in sorted(repo.revisions, key=_revision_sorting_order):
+            lines.append(
+                # Deselect by prepending a '#'
+                f"{'' if revision.commit_hash in preselected else '#'}   "
+                f" {revision.commit_hash} # Refs:"
+                # Print `refs` as comment on same line
+                f" {', '.join(sorted(revision.refs)) or '(detached)'} # modified"
+                # Print `last_modified` as comment on same line
+                f" {revision.last_modified_str}"
+            )
+
+    with open(tmp_path, "w") as f:
+        f.write(_MANUAL_REVIEW_NO_TUI_INSTRUCTIONS)
+        f.write("\n".join(lines))
+
+    # 2. Prompt instructions to user.
+    instructions = f"""
+    TUI is disabled. In order to select which revisions you want to delete, please edit
+    the following file using the text editor of your choice. Instructions for manual
+    editing are located at the beginning of the file. Edit the file, save it and confirm
+    to continue.
+    File to edit: {ANSI.bold(tmp_path)}
+    """
+    print("\n".join(line.strip() for line in instructions.strip().split("\n")))
+
+    # 3. Wait for user confirmation.
+    while True:
+        selected_hashes = _read_manual_review_tmp_file(tmp_path)
+        if _ask_for_confirmation_no_tui(
+            _get_expectations_str(hf_cache_info, selected_hashes) + " Continue ?",
+            default=False,
+        ):
+            break
+
+    # 4. Return selected_hashes
+    os.remove(tmp_path)
+    return selected_hashes
+
+
+def _ask_for_confirmation_no_tui(message: str, default: bool = True) -> bool:
+    """Ask for confirmation using pure-python."""
+    YES = ("y", "yes", "1")
+    NO = ("n", "no", "0")
+    DEFAULT = ""
+    ALL = YES + NO + (DEFAULT,)
+    full_message = message + (" (Y/n) " if default else " (y/N) ")
+    while True:
+        answer = input(full_message).lower()
+        if answer == DEFAULT:
+            return default
+        if answer in YES:
+            return True
+        if answer in NO:
+            return False
+        print(f"Invalid input. Must be one of {ALL}")
+
+
+def _get_expectations_str(hf_cache_info: HFCacheInfo, selected_hashes: List[str]) -> str:
+    """Format a string to display to the user how much space would be saved.
+
+    Example:
+    ```
+    >>> _get_expectations_str(hf_cache_info, selected_hashes)
+    '7 revisions selected counting for 4.3G.'
+    ```
+    """
+    if _CANCEL_DELETION_STR in selected_hashes:
+        return "Nothing will be deleted."
+    strategy = hf_cache_info.delete_revisions(*selected_hashes)
+    return f"{len(selected_hashes)} revisions selected counting for {strategy.expected_freed_size_str}."
+
+
+def _read_manual_review_tmp_file(tmp_path: str) -> List[str]:
+    """Read the manually reviewed instruction file and return a list of revision hash.
+
+    Example:
+        ```txt
+        # This is the tmp file content
+        ###
+
+        # Commented out line
+        123456789 # revision hash
+
+        # Something else
+        #      a_newer_hash # 2 days ago
+            an_older_hash # 3 days ago
+        ```
+
+        ```py
+        >>> _read_manual_review_tmp_file(tmp_path)
+        ['123456789', 'an_older_hash']
+        ```
+    """
+    with open(tmp_path) as f:
+        content = f.read()
+
+    # Split lines
+    lines = [line.strip() for line in content.split("\n")]
+
+    # Filter commented lines
+    selected_lines = [line for line in lines if not line.startswith("#")]
+
+    # Select only before comment
+    selected_hashes = [line.split("#")[0].strip() for line in selected_lines]
+
+    # Return revision hashes
+    return [hash for hash in selected_hashes if len(hash) > 0]
+
+
+_MANUAL_REVIEW_NO_TUI_INSTRUCTIONS = f"""
+# INSTRUCTIONS
+# ------------
+# This is a temporary file created by running `huggingface-cli delete-cache` with the
+# `--disable-tui` option. It contains a set of revisions that can be deleted from your
+# local cache directory.
+#
+# Please manually review the revisions you want to delete:
+#   - Revision hashes can be commented out with '#'.
+#   - Only non-commented revisions in this file will be deleted.
+#   - Revision hashes that are removed from this file are ignored as well.
+#   - If `{_CANCEL_DELETION_STR}` line is uncommented, the all cache deletion is cancelled and
+#     no changes will be applied.
+#
+# Once you've manually reviewed this file, please confirm deletion in the terminal. This
+# file will be automatically removed once done.
+# ------------
+
+# KILL SWITCH
+# ------------
+# Un-comment following line to completely cancel the deletion process
+# {_CANCEL_DELETION_STR}
+# ------------
+
+# REVISIONS
+# ------------
+""".strip()
+
+
+def _repo_sorting_order(repo: CachedRepoInfo) -> Any:
+    # First split by Dataset/Model, then sort by last accessed (oldest first)
+    return (repo.repo_type, repo.last_accessed)
+
+
+def _revision_sorting_order(revision: CachedRevisionInfo) -> Any:
+    # Sort by last modified (oldest first)
+    return revision.last_modified

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/download.py

@@ -0,0 +1,192 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to download files from the Hub with the CLI.
+
+Usage:
+    huggingface-cli download --help
+
+    # Download file
+    huggingface-cli download gpt2 config.json
+
+    # Download entire repo
+    huggingface-cli download fffiloni/zeroscope --repo-type=space --revision=refs/pr/78
+
+    # Download repo with filters
+    huggingface-cli download gpt2 --include="*.safetensors"
+
+    # Download with token
+    huggingface-cli download Wauplin/private-model --token=hf_***
+
+    # Download quietly (no progress bar, no warnings, only the returned path)
+    huggingface-cli download gpt2 config.json --quiet
+
+    # Download to local dir
+    huggingface-cli download gpt2 --local-dir=./models/gpt2
+"""
+
+import warnings
+from argparse import Namespace, _SubParsersAction
+from typing import List, Optional
+
+from huggingface_hub import logging
+from huggingface_hub._snapshot_download import snapshot_download
+from huggingface_hub.commands import BaseHuggingfaceCLICommand
+from huggingface_hub.file_download import hf_hub_download
+from huggingface_hub.utils import disable_progress_bars, enable_progress_bars
+
+
+logger = logging.get_logger(__name__)
+
+
+class DownloadCommand(BaseHuggingfaceCLICommand):
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        download_parser = parser.add_parser("download", help="Download files from the Hub")
+        download_parser.add_argument(
+            "repo_id", type=str, help="ID of the repo to download from (e.g. `username/repo-name`)."
+        )
+        download_parser.add_argument(
+            "filenames", type=str, nargs="*", help="Files to download (e.g. `config.json`, `data/metadata.jsonl`)."
+        )
+        download_parser.add_argument(
+            "--repo-type",
+            choices=["model", "dataset", "space"],
+            default="model",
+            help="Type of repo to download from (defaults to 'model').",
+        )
+        download_parser.add_argument(
+            "--revision",
+            type=str,
+            help="An optional Git revision id which can be a branch name, a tag, or a commit hash.",
+        )
+        download_parser.add_argument(
+            "--include", nargs="*", type=str, help="Glob patterns to match files to download."
+        )
+        download_parser.add_argument(
+            "--exclude", nargs="*", type=str, help="Glob patterns to exclude from files to download."
+        )
+        download_parser.add_argument(
+            "--cache-dir", type=str, help="Path to the directory where to save the downloaded files."
+        )
+        download_parser.add_argument(
+            "--local-dir",
+            type=str,
+            help=(
+                "If set, the downloaded file will be placed under this directory. Check out"
+                " https://huggingface.co/docs/huggingface_hub/guides/download#download-files-to-local-folder for more"
+                " details."
+            ),
+        )
+        download_parser.add_argument(
+            "--local-dir-use-symlinks",
+            choices=["auto", "True", "False"],
+            help=("Deprecated and ignored. Downloading to a local directory does not use symlinks anymore."),
+        )
+        download_parser.add_argument(
+            "--force-download",
+            action="store_true",
+            help="If True, the files will be downloaded even if they are already cached.",
+        )
+        download_parser.add_argument(
+            "--resume-download",
+            action="store_true",
+            help="Deprecated and ignored. Downloading a file to local dir always attempts to resume previously interrupted downloads (unless hf-transfer is enabled).",
+        )
+        download_parser.add_argument(
+            "--token", type=str, help="A User Access Token generated from https://huggingface.co/settings/tokens"
+        )
+        download_parser.add_argument(
+            "--quiet",
+            action="store_true",
+            help="If True, progress bars are disabled and only the path to the download files is printed.",
+        )
+        download_parser.set_defaults(func=DownloadCommand)
+
+    def __init__(self, args: Namespace) -> None:
+        self.token = args.token
+        self.repo_id: str = args.repo_id
+        self.filenames: List[str] = args.filenames
+        self.repo_type: str = args.repo_type
+        self.revision: Optional[str] = args.revision
+        self.include: Optional[List[str]] = args.include
+        self.exclude: Optional[List[str]] = args.exclude
+        self.cache_dir: Optional[str] = args.cache_dir
+        self.local_dir: Optional[str] = args.local_dir
+        self.force_download: bool = args.force_download
+        self.resume_download: Optional[bool] = args.resume_download or None
+        self.quiet: bool = args.quiet
+
+        if args.local_dir_use_symlinks is not None:
+            warnings.warn(
+                "Ignoring --local-dir-use-symlinks. Downloading to a local directory does not use symlinks anymore.",
+                FutureWarning,
+            )
+
+    def run(self) -> None:
+        if self.quiet:
+            disable_progress_bars()
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                print(self._download())  # Print path to downloaded files
+            enable_progress_bars()
+        else:
+            logging.set_verbosity_info()
+            print(self._download())  # Print path to downloaded files
+            logging.set_verbosity_warning()
+
+    def _download(self) -> str:
+        # Warn user if patterns are ignored
+        if len(self.filenames) > 0:
+            if self.include is not None and len(self.include) > 0:
+                warnings.warn("Ignoring `--include` since filenames have being explicitly set.")
+            if self.exclude is not None and len(self.exclude) > 0:
+                warnings.warn("Ignoring `--exclude` since filenames have being explicitly set.")
+
+        # Single file to download: use `hf_hub_download`
+        if len(self.filenames) == 1:
+            return hf_hub_download(
+                repo_id=self.repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                filename=self.filenames[0],
+                cache_dir=self.cache_dir,
+                resume_download=self.resume_download,
+                force_download=self.force_download,
+                token=self.token,
+                local_dir=self.local_dir,
+                library_name="huggingface-cli",
+            )
+
+        # Otherwise: use `snapshot_download` to ensure all files comes from same revision
+        elif len(self.filenames) == 0:
+            allow_patterns = self.include
+            ignore_patterns = self.exclude
+        else:
+            allow_patterns = self.filenames
+            ignore_patterns = None
+
+        return snapshot_download(
+            repo_id=self.repo_id,
+            repo_type=self.repo_type,
+            revision=self.revision,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            resume_download=self.resume_download,
+            force_download=self.force_download,
+            cache_dir=self.cache_dir,
+            token=self.token,
+            local_dir=self.local_dir,
+            library_name="huggingface-cli",
+        )

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/env.py

@@ -0,0 +1,36 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to print information about the environment.
+
+Usage:
+    huggingface-cli env
+"""
+
+from argparse import _SubParsersAction
+
+from ..utils import dump_environment_info
+from . import BaseHuggingfaceCLICommand
+
+
+class EnvironmentCommand(BaseHuggingfaceCLICommand):
+    def __init__(self, args):
+        self.args = args
+
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        env_parser = parser.add_parser("env", help="Print information about the environment.")
+        env_parser.set_defaults(func=EnvironmentCommand)
+
+    def run(self) -> None:
+        dump_environment_info()

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from argparse import ArgumentParser
+
+from huggingface_hub.commands.delete_cache import DeleteCacheCommand
+from huggingface_hub.commands.download import DownloadCommand
+from huggingface_hub.commands.env import EnvironmentCommand
+from huggingface_hub.commands.lfs import LfsCommands
+from huggingface_hub.commands.scan_cache import ScanCacheCommand
+from huggingface_hub.commands.tag import TagCommands
+from huggingface_hub.commands.upload import UploadCommand
+from huggingface_hub.commands.user import UserCommands
+
+
+def main():
+    parser = ArgumentParser("huggingface-cli", usage="huggingface-cli <command> [<args>]")
+    commands_parser = parser.add_subparsers(help="huggingface-cli command helpers")
+
+    # Register commands
+    EnvironmentCommand.register_subcommand(commands_parser)
+    UserCommands.register_subcommand(commands_parser)
+    UploadCommand.register_subcommand(commands_parser)
+    DownloadCommand.register_subcommand(commands_parser)
+    LfsCommands.register_subcommand(commands_parser)
+    ScanCacheCommand.register_subcommand(commands_parser)
+    DeleteCacheCommand.register_subcommand(commands_parser)
+    TagCommands.register_subcommand(commands_parser)
+
+    # Let's go
+    args = parser.parse_args()
+
+    if not hasattr(args, "func"):
+        parser.print_help()
+        exit(1)
+
+    # Run
+    service = args.func(args)
+    service.run()
+
+
+if __name__ == "__main__":
+    main()

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/lfs.py

@@ -0,0 +1,199 @@
+"""
+Implementation of a custom transfer agent for the transfer type "multipart" for
+git-lfs.
+
+Inspired by:
+github.com/cbartz/git-lfs-swift-transfer-agent/blob/master/git_lfs_swift_transfer.py
+
+Spec is: github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
+
+
+To launch debugger while developing:
+
+``` [lfs "customtransfer.multipart"]
+path = /path/to/huggingface_hub/.env/bin/python args = -m debugpy --listen 5678
+--wait-for-client
+/path/to/huggingface_hub/src/huggingface_hub/commands/huggingface_cli.py
+lfs-multipart-upload ```"""
+
+import json
+import os
+import subprocess
+import sys
+from argparse import _SubParsersAction
+from typing import Dict, List, Optional
+
+from huggingface_hub.commands import BaseHuggingfaceCLICommand
+from huggingface_hub.lfs import LFS_MULTIPART_UPLOAD_COMMAND, SliceFileObj
+
+from ..utils import get_session, hf_raise_for_status, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LfsCommands(BaseHuggingfaceCLICommand):
+    """
+    Implementation of a custom transfer agent for the transfer type "multipart"
+    for git-lfs. This lets users upload large files >5GB 🔥. Spec for LFS custom
+    transfer agent is:
+    https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
+
+    This introduces two commands to the CLI:
+
+    1. $ huggingface-cli lfs-enable-largefiles
+
+    This should be executed once for each model repo that contains a model file
+    >5GB. It's documented in the error message you get if you just try to git
+    push a 5GB file without having enabled it before.
+
+    2. $ huggingface-cli lfs-multipart-upload
+
+    This command is called by lfs directly and is not meant to be called by the
+    user.
+    """
+
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        enable_parser = parser.add_parser(
+            "lfs-enable-largefiles", help="Configure your repository to enable upload of files > 5GB."
+        )
+        enable_parser.add_argument("path", type=str, help="Local path to repository you want to configure.")
+        enable_parser.set_defaults(func=lambda args: LfsEnableCommand(args))
+
+        # Command will get called by git-lfs, do not call it directly.
+        upload_parser = parser.add_parser(LFS_MULTIPART_UPLOAD_COMMAND, add_help=False)
+        upload_parser.set_defaults(func=lambda args: LfsUploadCommand(args))
+
+
+class LfsEnableCommand:
+    def __init__(self, args):
+        self.args = args
+
+    def run(self):
+        local_path = os.path.abspath(self.args.path)
+        if not os.path.isdir(local_path):
+            print("This does not look like a valid git repo.")
+            exit(1)
+        subprocess.run(
+            "git config lfs.customtransfer.multipart.path huggingface-cli".split(),
+            check=True,
+            cwd=local_path,
+        )
+        subprocess.run(
+            f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split(),
+            check=True,
+            cwd=local_path,
+        )
+        print("Local repo set up for largefiles")
+
+
+def write_msg(msg: Dict):
+    """Write out the message in Line delimited JSON."""
+    msg_str = json.dumps(msg) + "\n"
+    sys.stdout.write(msg_str)
+    sys.stdout.flush()
+
+
+def read_msg() -> Optional[Dict]:
+    """Read Line delimited JSON from stdin."""
+    msg = json.loads(sys.stdin.readline().strip())
+
+    if "terminate" in (msg.get("type"), msg.get("event")):
+        # terminate message received
+        return None
+
+    if msg.get("event") not in ("download", "upload"):
+        logger.critical("Received unexpected message")
+        sys.exit(1)
+
+    return msg
+
+
+class LfsUploadCommand:
+    def __init__(self, args) -> None:
+        self.args = args
+
+    def run(self) -> None:
+        # Immediately after invoking a custom transfer process, git-lfs
+        # sends initiation data to the process over stdin.
+        # This tells the process useful information about the configuration.
+        init_msg = json.loads(sys.stdin.readline().strip())
+        if not (init_msg.get("event") == "init" and init_msg.get("operation") == "upload"):
+            write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}})
+            sys.exit(1)
+
+        # The transfer process should use the information it needs from the
+        # initiation structure, and also perform any one-off setup tasks it
+        # needs to do. It should then respond on stdout with a simple empty
+        # confirmation structure, as follows:
+        write_msg({})
+
+        # After the initiation exchange, git-lfs will send any number of
+        # transfer requests to the stdin of the transfer process, in a serial sequence.
+        while True:
+            msg = read_msg()
+            if msg is None:
+                # When all transfers have been processed, git-lfs will send
+                # a terminate event to the stdin of the transfer process.
+                # On receiving this message the transfer process should
+                # clean up and terminate. No response is expected.
+                sys.exit(0)
+
+            oid = msg["oid"]
+            filepath = msg["path"]
+            completion_url = msg["action"]["href"]
+            header = msg["action"]["header"]
+            chunk_size = int(header.pop("chunk_size"))
+            presigned_urls: List[str] = list(header.values())
+
+            # Send a "started" progress event to allow other workers to start.
+            # Otherwise they're delayed until first "progress" event is reported,
+            # i.e. after the first 5GB by default (!)
+            write_msg(
+                {
+                    "event": "progress",
+                    "oid": oid,
+                    "bytesSoFar": 1,
+                    "bytesSinceLast": 0,
+                }
+            )
+
+            parts = []
+            with open(filepath, "rb") as file:
+                for i, presigned_url in enumerate(presigned_urls):
+                    with SliceFileObj(
+                        file,
+                        seek_from=i * chunk_size,
+                        read_limit=chunk_size,
+                    ) as data:
+                        r = get_session().put(presigned_url, data=data)
+                        hf_raise_for_status(r)
+                        parts.append(
+                            {
+                                "etag": r.headers.get("etag"),
+                                "partNumber": i + 1,
+                            }
+                        )
+                        # In order to support progress reporting while data is uploading / downloading,
+                        # the transfer process should post messages to stdout
+                        write_msg(
+                            {
+                                "event": "progress",
+                                "oid": oid,
+                                "bytesSoFar": (i + 1) * chunk_size,
+                                "bytesSinceLast": chunk_size,
+                            }
+                        )
+                        # Not precise but that's ok.
+
+            r = get_session().post(
+                completion_url,
+                json={
+                    "oid": oid,
+                    "parts": parts,
+                },
+            )
+            hf_raise_for_status(r)
+
+            write_msg({"event": "complete", "oid": oid})

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/scan_cache.py

@@ -0,0 +1,139 @@
+# coding=utf-8
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to scan the HF cache directory.
+
+Usage:
+    huggingface-cli scan-cache
+    huggingface-cli scan-cache -v
+    huggingface-cli scan-cache -vvv
+    huggingface-cli scan-cache --dir ~/.cache/huggingface/hub
+"""
+
+import time
+from argparse import Namespace, _SubParsersAction
+from typing import Optional
+
+from ..utils import CacheNotFound, HFCacheInfo, scan_cache_dir
+from . import BaseHuggingfaceCLICommand
+from ._cli_utils import ANSI, tabulate
+
+
+class ScanCacheCommand(BaseHuggingfaceCLICommand):
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        scan_cache_parser = parser.add_parser("scan-cache", help="Scan cache directory.")
+
+        scan_cache_parser.add_argument(
+            "--dir",
+            type=str,
+            default=None,
+            help="cache directory to scan (optional). Default to the default HuggingFace cache.",
+        )
+        scan_cache_parser.add_argument(
+            "-v",
+            "--verbose",
+            action="count",
+            default=0,
+            help="show a more verbose output",
+        )
+        scan_cache_parser.set_defaults(func=ScanCacheCommand)
+
+    def __init__(self, args: Namespace) -> None:
+        self.verbosity: int = args.verbose
+        self.cache_dir: Optional[str] = args.dir
+
+    def run(self):
+        try:
+            t0 = time.time()
+            hf_cache_info = scan_cache_dir(self.cache_dir)
+            t1 = time.time()
+        except CacheNotFound as exc:
+            cache_dir = exc.cache_dir
+            print(f"Cache directory not found: {cache_dir}")
+            return
+
+        self._print_hf_cache_info_as_table(hf_cache_info)
+
+        print(
+            f"\nDone in {round(t1-t0,1)}s. Scanned {len(hf_cache_info.repos)} repo(s)"
+            f" for a total of {ANSI.red(hf_cache_info.size_on_disk_str)}."
+        )
+        if len(hf_cache_info.warnings) > 0:
+            message = f"Got {len(hf_cache_info.warnings)} warning(s) while scanning."
+            if self.verbosity >= 3:
+                print(ANSI.gray(message))
+                for warning in hf_cache_info.warnings:
+                    print(ANSI.gray(warning))
+            else:
+                print(ANSI.gray(message + " Use -vvv to print details."))
+
+    def _print_hf_cache_info_as_table(self, hf_cache_info: HFCacheInfo) -> None:
+        if self.verbosity == 0:
+            print(
+                tabulate(
+                    rows=[
+                        [
+                            repo.repo_id,
+                            repo.repo_type,
+                            "{:>12}".format(repo.size_on_disk_str),
+                            repo.nb_files,
+                            repo.last_accessed_str,
+                            repo.last_modified_str,
+                            ", ".join(sorted(repo.refs)),
+                            str(repo.repo_path),
+                        ]
+                        for repo in sorted(hf_cache_info.repos, key=lambda repo: repo.repo_path)
+                    ],
+                    headers=[
+                        "REPO ID",
+                        "REPO TYPE",
+                        "SIZE ON DISK",
+                        "NB FILES",
+                        "LAST_ACCESSED",
+                        "LAST_MODIFIED",
+                        "REFS",
+                        "LOCAL PATH",
+                    ],
+                )
+            )
+        else:
+            print(
+                tabulate(
+                    rows=[
+                        [
+                            repo.repo_id,
+                            repo.repo_type,
+                            revision.commit_hash,
+                            "{:>12}".format(revision.size_on_disk_str),
+                            revision.nb_files,
+                            revision.last_modified_str,
+                            ", ".join(sorted(revision.refs)),
+                            str(revision.snapshot_path),
+                        ]
+                        for repo in sorted(hf_cache_info.repos, key=lambda repo: repo.repo_path)
+                        for revision in sorted(repo.revisions, key=lambda revision: revision.commit_hash)
+                    ],
+                    headers=[
+                        "REPO ID",
+                        "REPO TYPE",
+                        "REVISION",
+                        "SIZE ON DISK",
+                        "NB FILES",
+                        "LAST_MODIFIED",
+                        "REFS",
+                        "LOCAL PATH",
+                    ],
+                )
+            )

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/tag.py

@@ -0,0 +1,159 @@
+# coding=utf-8
+# Copyright 2024-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Contains commands to perform tag management with the CLI.
+
+Usage Examples:
+    - Create a tag:
+        $ huggingface-cli tag user/my-model 1.0 --message "First release"
+        $ huggingface-cli tag user/my-model 1.0 -m "First release" --revision develop
+        $ huggingface-cli tag user/my-dataset 1.0 -m "First release" --repo-type dataset
+        $ huggingface-cli tag user/my-space 1.0
+    - List all tags:
+        $ huggingface-cli tag -l user/my-model
+        $ huggingface-cli tag --list user/my-dataset --repo-type dataset
+    - Delete a tag:
+        $ huggingface-cli tag -d user/my-model 1.0
+        $ huggingface-cli tag --delete user/my-dataset 1.0 --repo-type dataset
+        $ huggingface-cli tag -d user/my-space 1.0 -y
+"""
+
+from argparse import Namespace, _SubParsersAction
+
+from requests.exceptions import HTTPError
+
+from huggingface_hub.commands import BaseHuggingfaceCLICommand
+from huggingface_hub.constants import (
+    REPO_TYPES,
+)
+from huggingface_hub.hf_api import HfApi
+
+from ..utils import HfHubHTTPError, RepositoryNotFoundError, RevisionNotFoundError
+from ._cli_utils import ANSI
+
+
+class TagCommands(BaseHuggingfaceCLICommand):
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        tag_parser = parser.add_parser("tag", help="(create, list, delete) tags for a repo in the hub")
+
+        tag_parser.add_argument("repo_id", type=str, help="The ID of the repo to tag (e.g. `username/repo-name`).")
+        tag_parser.add_argument("tag", nargs="?", type=str, help="The name of the tag for creation or deletion.")
+        tag_parser.add_argument("-m", "--message", type=str, help="The description of the tag to create.")
+        tag_parser.add_argument("--revision", type=str, help="The git revision to tag.")
+        tag_parser.add_argument(
+            "--token", type=str, help="A User Access Token generated from https://huggingface.co/settings/tokens."
+        )
+        tag_parser.add_argument(
+            "--repo-type",
+            choices=["model", "dataset", "space"],
+            default="model",
+            help="Set the type of repository (model, dataset, or space).",
+        )
+        tag_parser.add_argument("-y", "--yes", action="store_true", help="Answer Yes to prompts automatically.")
+
+        tag_parser.add_argument("-l", "--list", action="store_true", help="List tags for a repository.")
+        tag_parser.add_argument("-d", "--delete", action="store_true", help="Delete a tag for a repository.")
+
+        tag_parser.set_defaults(func=lambda args: handle_commands(args))
+
+
+def handle_commands(args: Namespace):
+    if args.list:
+        return TagListCommand(args)
+    elif args.delete:
+        return TagDeleteCommand(args)
+    else:
+        return TagCreateCommand(args)
+
+
+class TagCommand:
+    def __init__(self, args: Namespace):
+        self.args = args
+        self.api = HfApi(token=self.args.token)
+        self.repo_id = self.args.repo_id
+        self.repo_type = self.args.repo_type
+        if self.repo_type not in REPO_TYPES:
+            print("Invalid repo --repo-type")
+            exit(1)
+
+
+class TagCreateCommand(TagCommand):
+    def run(self):
+        print(f"You are about to create tag {ANSI.bold(self.args.tag)} on {self.repo_type} {ANSI.bold(self.repo_id)}")
+
+        try:
+            self.api.create_tag(
+                repo_id=self.repo_id,
+                tag=self.args.tag,
+                tag_message=self.args.message,
+                revision=self.args.revision,
+                repo_type=self.repo_type,
+            )
+        except RepositoryNotFoundError:
+            print(f"{self.repo_type.capitalize()} {ANSI.bold(self.repo_id)} not found.")
+            exit(1)
+        except RevisionNotFoundError:
+            print(f"Revision {ANSI.bold(self.args.revision)} not found.")
+            exit(1)
+        except HfHubHTTPError as e:
+            if e.response.status_code == 409:
+                print(f"Tag {ANSI.bold(self.args.tag)} already exists on {ANSI.bold(self.repo_id)}")
+                exit(1)
+            raise e
+
+        print(f"Tag {ANSI.bold(self.args.tag)} created on {ANSI.bold(self.repo_id)}")
+
+
+class TagListCommand(TagCommand):
+    def run(self):
+        try:
+            refs = self.api.list_repo_refs(
+                repo_id=self.repo_id,
+                repo_type=self.repo_type,
+            )
+        except RepositoryNotFoundError:
+            print(f"{self.repo_type.capitalize()} {ANSI.bold(self.repo_id)} not found.")
+            exit(1)
+        except HTTPError as e:
+            print(e)
+            print(ANSI.red(e.response.text))
+            exit(1)
+        if len(refs.tags) == 0:
+            print("No tags found")
+            exit(0)
+        print(f"Tags for {self.repo_type} {ANSI.bold(self.repo_id)}:")
+        for tag in refs.tags:
+            print(tag.name)
+
+
+class TagDeleteCommand(TagCommand):
+    def run(self):
+        print(f"You are about to delete tag {ANSI.bold(self.args.tag)} on {self.repo_type} {ANSI.bold(self.repo_id)}")
+
+        if not self.args.yes:
+            choice = input("Proceed? [Y/n] ").lower()
+            if choice not in ("", "y", "yes"):
+                print("Abort")
+                exit()
+        try:
+            self.api.delete_tag(repo_id=self.repo_id, tag=self.args.tag, repo_type=self.repo_type)
+        except RepositoryNotFoundError:
+            print(f"{self.repo_type.capitalize()} {ANSI.bold(self.repo_id)} not found.")
+            exit(1)
+        except RevisionNotFoundError:
+            print(f"Tag {ANSI.bold(self.args.tag)} not found on {ANSI.bold(self.repo_id)}")
+            exit(1)
+        print(f"Tag {ANSI.bold(self.args.tag)} deleted on {ANSI.bold(self.repo_id)}")

llmeval-env/lib/python3.10/site-packages/huggingface_hub/commands/upload.py

@@ -0,0 +1,298 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to upload a repo or file with the CLI.
+
+Usage:
+    # Upload file (implicit)
+    huggingface-cli upload my-cool-model ./my-cool-model.safetensors
+
+    # Upload file (explicit)
+    huggingface-cli upload my-cool-model ./my-cool-model.safetensors  model.safetensors
+
+    # Upload directory (implicit). If `my-cool-model/` is a directory it will be uploaded, otherwise an exception is raised.
+    huggingface-cli upload my-cool-model
+
+    # Upload directory (explicit)
+    huggingface-cli upload my-cool-model ./models/my-cool-model .
+
+    # Upload filtered directory (example: tensorboard logs except for the last run)
+    huggingface-cli upload my-cool-model ./model/training /logs --include "*.tfevents.*" --exclude "*20230905*"
+
+    # Upload private dataset
+    huggingface-cli upload Wauplin/my-cool-dataset ./data . --repo-type=dataset --private
+
+    # Upload with token
+    huggingface-cli upload Wauplin/my-cool-model --token=hf_****
+
+    # Sync local Space with Hub (upload new files, delete removed files)
+    huggingface-cli upload Wauplin/space-example --repo-type=space --exclude="/logs/*" --delete="*" --commit-message="Sync local Space with Hub"
+
+    # Schedule commits every 30 minutes
+    huggingface-cli upload Wauplin/my-cool-model --every=30
+"""
+
+import os
+import time
+import warnings
+from argparse import Namespace, _SubParsersAction
+from typing import List, Optional
+
+from huggingface_hub import logging
+from huggingface_hub._commit_scheduler import CommitScheduler
+from huggingface_hub.commands import BaseHuggingfaceCLICommand
+from huggingface_hub.constants import HF_HUB_ENABLE_HF_TRANSFER
+from huggingface_hub.hf_api import HfApi
+from huggingface_hub.utils import RevisionNotFoundError, disable_progress_bars, enable_progress_bars
+
+
+logger = logging.get_logger(__name__)
+
+
+class UploadCommand(BaseHuggingfaceCLICommand):
+    @staticmethod
+    def register_subcommand(parser: _SubParsersAction):
+        upload_parser = parser.add_parser("upload", help="Upload a file or a folder to a repo on the Hub")
+        upload_parser.add_argument(
+            "repo_id", type=str, help="The ID of the repo to upload to (e.g. `username/repo-name`)."
+        )
+        upload_parser.add_argument(
+            "local_path", nargs="?", help="Local path to the file or folder to upload. Defaults to current directory."
+        )
+        upload_parser.add_argument(
+            "path_in_repo",
+            nargs="?",
+            help="Path of the file or folder in the repo. Defaults to the relative path of the file or folder.",
+        )
+        upload_parser.add_argument(
+            "--repo-type",
+            choices=["model", "dataset", "space"],
+            default="model",
+            help="Type of the repo to upload to (e.g. `dataset`).",
+        )
+        upload_parser.add_argument(
+            "--revision",
+            type=str,
+            help=(
+                "An optional Git revision to push to. It can be a branch name or a PR reference. If revision does not"
+                " exist and `--create-pr` is not set, a branch will be automatically created."
+            ),
+        )
+        upload_parser.add_argument(
+            "--private",
+            action="store_true",
+            help=(
+                "Whether to create a private repo if repo doesn't exist on the Hub. Ignored if the repo already"
+                " exists."
+            ),
+        )
+        upload_parser.add_argument("--include", nargs="*", type=str, help="Glob patterns to match files to upload.")
+        upload_parser.add_argument(
+            "--exclude", nargs="*", type=str, help="Glob patterns to exclude from files to upload."
+        )
+        upload_parser.add_argument(
+            "--delete",
+            nargs="*",
+            type=str,
+            help="Glob patterns for file to be deleted from the repo while committing.",
+        )
+        upload_parser.add_argument(
+            "--commit-message", type=str, help="The summary / title / first line of the generated commit."
+        )
+        upload_parser.add_argument("--commit-description", type=str, help="The description of the generated commit.")
+        upload_parser.add_argument(
+            "--create-pr", action="store_true", help="Whether to upload content as a new Pull Request."
+        )
+        upload_parser.add_argument(
+            "--every",
+            type=float,
+            help="If set, a background job is scheduled to create commits every `every` minutes.",
+        )
+        upload_parser.add_argument(
+            "--token", type=str, help="A User Access Token generated from https://huggingface.co/settings/tokens"
+        )
+        upload_parser.add_argument(
+            "--quiet",
+            action="store_true",
+            help="If True, progress bars are disabled and only the path to the uploaded files is printed.",
+        )
+        upload_parser.set_defaults(func=UploadCommand)
+
+    def __init__(self, args: Namespace) -> None:
+        self.repo_id: str = args.repo_id
+        self.repo_type: Optional[str] = args.repo_type
+        self.revision: Optional[str] = args.revision
+        self.private: bool = args.private
+
+        self.include: Optional[List[str]] = args.include
+        self.exclude: Optional[List[str]] = args.exclude
+        self.delete: Optional[List[str]] = args.delete
+
+        self.commit_message: Optional[str] = args.commit_message
+        self.commit_description: Optional[str] = args.commit_description
+        self.create_pr: bool = args.create_pr
+        self.api: HfApi = HfApi(token=args.token, library_name="huggingface-cli")
+        self.quiet: bool = args.quiet  # disable warnings and progress bars
+
+        # Check `--every` is valid
+        if args.every is not None and args.every <= 0:
+            raise ValueError(f"`every` must be a positive value (got '{args.every}')")
+        self.every: Optional[float] = args.every
+
+        # Resolve `local_path` and `path_in_repo`
+        repo_name: str = args.repo_id.split("/")[-1]  # e.g. "Wauplin/my-cool-model" => "my-cool-model"
+        self.local_path: str
+        self.path_in_repo: str
+        if args.local_path is None and os.path.isfile(repo_name):
+            # Implicit case 1: user provided only a repo_id which happen to be a local file as well => upload it with same name
+            self.local_path = repo_name
+            self.path_in_repo = repo_name
+        elif args.local_path is None and os.path.isdir(repo_name):
+            # Implicit case 2: user provided only a repo_id which happen to be a local folder as well => upload it at root
+            self.local_path = repo_name
+            self.path_in_repo = "."
+        elif args.local_path is None:
+            # Implicit case 3: user provided only a repo_id that does not match a local file or folder
+            # => the user must explicitly provide a local_path => raise exception
+            raise ValueError(f"'{repo_name}' is not a local file or folder. Please set `local_path` explicitly.")
+        elif args.path_in_repo is None and os.path.isfile(args.local_path):
+            # Explicit local path to file, no path in repo => upload it at root with same name
+            self.local_path = args.local_path
+            self.path_in_repo = os.path.basename(args.local_path)
+        elif args.path_in_repo is None:
+            # Explicit local path to folder, no path in repo => upload at root
+            self.local_path = args.local_path
+            self.path_in_repo = "."
+        else:
+            # Finally, if both paths are explicit
+            self.local_path = args.local_path
+            self.path_in_repo = args.path_in_repo
+
+    def run(self) -> None:
+        if self.quiet:
+            disable_progress_bars()
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                print(self._upload())
+            enable_progress_bars()
+        else:
+            logging.set_verbosity_info()
+            print(self._upload())
+            logging.set_verbosity_warning()
+
+    def _upload(self) -> str:
+        if os.path.isfile(self.local_path):
+            if self.include is not None and len(self.include) > 0:
+                warnings.warn("Ignoring `--include` since a single file is uploaded.")
+            if self.exclude is not None and len(self.exclude) > 0:
+                warnings.warn("Ignoring `--exclude` since a single file is uploaded.")
+            if self.delete is not None and len(self.delete) > 0:
+                warnings.warn("Ignoring `--delete` since a single file is uploaded.")
+
+        if not HF_HUB_ENABLE_HF_TRANSFER:
+            logger.info(
+                "Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See"
+                " https://huggingface.co/docs/huggingface_hub/hf_transfer for more details."
+            )
+
+        # Schedule commits if `every` is set
+        if self.every is not None:
+            if os.path.isfile(self.local_path):
+                # If file => watch entire folder + use allow_patterns
+                folder_path = os.path.dirname(self.local_path)
+                path_in_repo = (
+                    self.path_in_repo[: -len(self.local_path)]  # remove filename from path_in_repo
+                    if self.path_in_repo.endswith(self.local_path)
+                    else self.path_in_repo
+                )
+                allow_patterns = [self.local_path]
+                ignore_patterns = []
+            else:
+                folder_path = self.local_path
+                path_in_repo = self.path_in_repo
+                allow_patterns = self.include or []
+                ignore_patterns = self.exclude or []
+                if self.delete is not None and len(self.delete) > 0:
+                    warnings.warn("Ignoring `--delete` when uploading with scheduled commits.")
+
+            scheduler = CommitScheduler(
+                folder_path=folder_path,
+                repo_id=self.repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                allow_patterns=allow_patterns,
+                ignore_patterns=ignore_patterns,
+                path_in_repo=path_in_repo,
+                private=self.private,
+                every=self.every,
+                hf_api=self.api,
+            )
+            print(f"Scheduling commits every {self.every} minutes to {scheduler.repo_id}.")
+            try:  # Block main thread until KeyboardInterrupt
+                while True:
+                    time.sleep(100)
+            except KeyboardInterrupt:
+                scheduler.stop()
+                return "Stopped scheduled commits."
+
+        # Otherwise, create repo and proceed with the upload
+        if not os.path.isfile(self.local_path) and not os.path.isdir(self.local_path):
+            raise FileNotFoundError(f"No such file or directory: '{self.local_path}'.")
+        repo_id = self.api.create_repo(
+            repo_id=self.repo_id,
+            repo_type=self.repo_type,
+            exist_ok=True,
+            private=self.private,
+            space_sdk="gradio" if self.repo_type == "space" else None,
+            # ^ We don't want it to fail when uploading to a Space => let's set Gradio by default.
+            # ^ I'd rather not add CLI args to set it explicitly as we already have `huggingface-cli repo create` for that.
+        ).repo_id
+
+        # Check if branch already exists and if not, create it
+        if self.revision is not None and not self.create_pr:
+            try:
+                self.api.repo_info(repo_id=repo_id, repo_type=self.repo_type, revision=self.revision)
+            except RevisionNotFoundError:
+                logger.info(f"Branch '{self.revision}' not found. Creating it...")
+                self.api.create_branch(repo_id=repo_id, repo_type=self.repo_type, branch=self.revision, exist_ok=True)
+                # ^ `exist_ok=True` to avoid race concurrency issues
+
+        # File-based upload
+        if os.path.isfile(self.local_path):
+            return self.api.upload_file(
+                path_or_fileobj=self.local_path,
+                path_in_repo=self.path_in_repo,
+                repo_id=repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                commit_message=self.commit_message,
+                commit_description=self.commit_description,
+                create_pr=self.create_pr,
+            )
+
+        # Folder-based upload
+        else:
+            return self.api.upload_folder(
+                folder_path=self.local_path,
+                path_in_repo=self.path_in_repo,
+                repo_id=repo_id,
+                repo_type=self.repo_type,
+                revision=self.revision,
+                commit_message=self.commit_message,
+                commit_description=self.commit_description,
+                create_pr=self.create_pr,
+                allow_patterns=self.include,
+                ignore_patterns=self.exclude,
+                delete_patterns=self.delete,
+            )

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_common.py

@@ -0,0 +1,472 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities used by both the sync and async inference clients."""
+
+import base64
+import io
+import json
+import logging
+from contextlib import contextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterable,
+    BinaryIO,
+    ContextManager,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Literal,
+    NoReturn,
+    Optional,
+    Set,
+    Union,
+    overload,
+)
+
+from requests import HTTPError
+
+from huggingface_hub.errors import (
+    GenerationError,
+    IncompleteGenerationError,
+    OverloadedError,
+    TextGenerationError,
+    UnknownError,
+    ValidationError,
+)
+
+from ..constants import ENDPOINT
+from ..utils import (
+    build_hf_headers,
+    get_session,
+    hf_raise_for_status,
+    is_aiohttp_available,
+    is_numpy_available,
+    is_pillow_available,
+)
+from ._generated.types import (
+    ChatCompletionStreamOutput,
+    ChatCompletionStreamOutputChoice,
+    ChatCompletionStreamOutputDelta,
+    TextGenerationStreamOutput,
+)
+
+
+if TYPE_CHECKING:
+    from aiohttp import ClientResponse, ClientSession
+    from PIL.Image import Image
+
+# TYPES
+UrlT = str
+PathT = Union[str, Path]
+BinaryT = Union[bytes, BinaryIO]
+ContentT = Union[BinaryT, PathT, UrlT]
+
+# Use to set a Accept: image/png header
+TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"}
+
+logger = logging.getLogger(__name__)
+
+
+# Add dataclass for ModelStatus. We use this dataclass in get_model_status function.
+@dataclass
+class ModelStatus:
+    """
+    This Dataclass represents the the model status in the Hugging Face Inference API.
+
+    Args:
+        loaded (`bool`):
+            If the model is currently loaded into Hugging Face's InferenceAPI. Models
+            are loaded on-demand, leading to the user's first request taking longer.
+            If a model is loaded, you can be assured that it is in a healthy state.
+        state (`str`):
+            The current state of the model. This can be 'Loaded', 'Loadable', 'TooBig'.
+            If a model's state is 'Loadable', it's not too big and has a supported
+            backend. Loadable models are automatically loaded when the user first
+            requests inference on the endpoint. This means it is transparent for the
+            user to load a model, except that the first call takes longer to complete.
+        compute_type (`Dict`):
+            Information about the compute resource the model is using or will use, such as 'gpu' type and number of
+            replicas.
+        framework (`str`):
+            The name of the framework that the model was built with, such as 'transformers'
+            or 'text-generation-inference'.
+    """
+
+    loaded: bool
+    state: str
+    compute_type: Dict
+    framework: str
+
+
+## IMPORT UTILS
+
+
+def _import_aiohttp():
+    # Make sure `aiohttp` is installed on the machine.
+    if not is_aiohttp_available():
+        raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).")
+    import aiohttp
+
+    return aiohttp
+
+
+def _import_numpy():
+    """Make sure `numpy` is installed on the machine."""
+    if not is_numpy_available():
+        raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).")
+    import numpy
+
+    return numpy
+
+
+def _import_pil_image():
+    """Make sure `PIL` is installed on the machine."""
+    if not is_pillow_available():
+        raise ImportError(
+            "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be"
+            " post-processed, use `client.post(...)` and get the raw response from the server."
+        )
+    from PIL import Image
+
+    return Image
+
+
+## RECOMMENDED MODELS
+
+# Will be globally fetched only once (see '_fetch_recommended_models')
+_RECOMMENDED_MODELS: Optional[Dict[str, Optional[str]]] = None
+
+
+def _fetch_recommended_models() -> Dict[str, Optional[str]]:
+    global _RECOMMENDED_MODELS
+    if _RECOMMENDED_MODELS is None:
+        response = get_session().get(f"{ENDPOINT}/api/tasks", headers=build_hf_headers())
+        hf_raise_for_status(response)
+        _RECOMMENDED_MODELS = {
+            task: _first_or_none(details["widgetModels"]) for task, details in response.json().items()
+        }
+    return _RECOMMENDED_MODELS
+
+
+def _first_or_none(items: List[Any]) -> Optional[Any]:
+    try:
+        return items[0] or None
+    except IndexError:
+        return None
+
+
+## ENCODING / DECODING UTILS
+
+
+@overload
+def _open_as_binary(
+    content: ContentT,
+) -> ContextManager[BinaryT]: ...  # means "if input is not None, output is not None"
+
+
+@overload
+def _open_as_binary(
+    content: Literal[None],
+) -> ContextManager[Literal[None]]: ...  # means "if input is None, output is None"
+
+
+@contextmanager  # type: ignore
+def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]:
+    """Open `content` as a binary file, either from a URL, a local path, or raw bytes.
+
+    Do nothing if `content` is None,
+
+    TODO: handle a PIL.Image as input
+    TODO: handle base64 as input
+    """
+    # If content is a string => must be either a URL or a path
+    if isinstance(content, str):
+        if content.startswith("https://") or content.startswith("http://"):
+            logger.debug(f"Downloading content from {content}")
+            yield get_session().get(content).content  # TODO: retrieve as stream and pipe to post request ?
+            return
+        content = Path(content)
+        if not content.exists():
+            raise FileNotFoundError(
+                f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local"
+                " file. To pass raw content, please encode it as bytes first."
+            )
+
+    # If content is a Path => open it
+    if isinstance(content, Path):
+        logger.debug(f"Opening content from {content}")
+        with content.open("rb") as f:
+            yield f
+    else:
+        # Otherwise: already a file-like object or None
+        yield content
+
+
+def _b64_encode(content: ContentT) -> str:
+    """Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL."""
+    with _open_as_binary(content) as data:
+        data_as_bytes = data if isinstance(data, bytes) else data.read()
+        return base64.b64encode(data_as_bytes).decode()
+
+
+def _b64_to_image(encoded_image: str) -> "Image":
+    """Parse a base64-encoded string into a PIL Image."""
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(base64.b64decode(encoded_image)))
+
+
+def _bytes_to_list(content: bytes) -> List:
+    """Parse bytes from a Response object into a Python list.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a
+    dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_dict(content: bytes) -> Dict:
+    """Parse bytes from a Response object into a Python dictionary.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a
+    list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_image(content: bytes) -> "Image":
+    """Parse bytes from a Response object into a PIL Image.
+
+    Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead.
+    """
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(content))
+
+
+## STREAMING UTILS
+
+
+def _stream_text_generation_response(
+    bytes_output_as_lines: Iterable[bytes], details: bool
+) -> Union[Iterable[str], Iterable[TextGenerationStreamOutput]]:
+    """Used in `InferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    for byte_payload in bytes_output_as_lines:
+        output = _format_text_generation_stream_output(byte_payload, details)
+        if output is not None:
+            yield output
+
+
+async def _async_stream_text_generation_response(
+    bytes_output_as_lines: AsyncIterable[bytes], details: bool
+) -> Union[AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]:
+    """Used in `AsyncInferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    async for byte_payload in bytes_output_as_lines:
+        output = _format_text_generation_stream_output(byte_payload, details)
+        if output is not None:
+            yield output
+
+
+def _format_text_generation_stream_output(
+    byte_payload: bytes, details: bool
+) -> Optional[Union[str, TextGenerationStreamOutput]]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+
+    # Either an error as being returned
+    if json_payload.get("error") is not None:
+        raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+    # Or parse token payload
+    output = TextGenerationStreamOutput.parse_obj_as_instance(json_payload)
+    return output.token.text if not details else output
+
+
+def _format_chat_completion_stream_output_from_text_generation(
+    item: TextGenerationStreamOutput, created: int
+) -> ChatCompletionStreamOutput:
+    if item.details is None:
+        # new token generated => return delta
+        return ChatCompletionStreamOutput(
+            # explicitly set 'dummy' values to reduce expectations from users
+            id="dummy",
+            model="dummy",
+            object="dummy",
+            system_fingerprint="dummy",
+            choices=[
+                ChatCompletionStreamOutputChoice(
+                    delta=ChatCompletionStreamOutputDelta(
+                        role="assistant",
+                        content=item.token.text,
+                    ),
+                    finish_reason=None,
+                    index=0,
+                )
+            ],
+            created=created,
+        )
+    else:
+        # generation is completed => return finish reason
+        return ChatCompletionStreamOutput(
+            # explicitly set 'dummy' values to reduce expectations from users
+            id="dummy",
+            model="dummy",
+            object="dummy",
+            system_fingerprint="dummy",
+            choices=[
+                ChatCompletionStreamOutputChoice(
+                    delta=ChatCompletionStreamOutputDelta(role="assistant"),
+                    finish_reason=item.details.finish_reason,
+                    index=0,
+                )
+            ],
+            created=created,
+        )
+
+
+def _stream_chat_completion_response_from_bytes(
+    bytes_lines: Iterable[bytes],
+) -> Iterable[ChatCompletionStreamOutput]:
+    """Used in `InferenceClient.chat_completion` if model is served with TGI."""
+    for item in bytes_lines:
+        output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
+        if output is not None:
+            yield output
+
+
+async def _async_stream_chat_completion_response_from_bytes(
+    bytes_lines: AsyncIterable[bytes],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+    """Used in `AsyncInferenceClient.chat_completion`."""
+    async for item in bytes_lines:
+        output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
+        if output is not None:
+            yield output
+
+
+def _format_chat_completion_stream_output_from_text_generation_from_bytes(
+    byte_payload: bytes,
+) -> Optional[ChatCompletionStreamOutput]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+    return ChatCompletionStreamOutput.parse_obj_as_instance(json_payload)
+
+
+async def _async_yield_from(client: "ClientSession", response: "ClientResponse") -> AsyncIterable[bytes]:
+    async for byte_payload in response.content:
+        yield byte_payload
+    await client.close()
+
+
+# "TGI servers" are servers running with the `text-generation-inference` backend.
+# This backend is the go-to solution to run large language models at scale. However,
+# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference`
+# solution is still in use.
+#
+# Both approaches have very similar APIs, but not exactly the same. What we do first in
+# the `text_generation` method is to assume the model is served via TGI. If we realize
+# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fallback to the
+# default API with a warning message. When that's the case, We remember the unsupported
+# attributes for this model in the `_UNSUPPORTED_TEXT_GENERATION_KWARGS` global variable.
+#
+# In addition, TGI servers have a built-in API route for chat-completion, which is not
+# available on the default API. We use this route to provide a more consistent behavior
+# when available.
+#
+# For more details, see https://github.com/huggingface/text-generation-inference and
+# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task.
+
+_UNSUPPORTED_TEXT_GENERATION_KWARGS: Dict[Optional[str], List[str]] = {}
+
+
+def _set_unsupported_text_generation_kwargs(model: Optional[str], unsupported_kwargs: List[str]) -> None:
+    _UNSUPPORTED_TEXT_GENERATION_KWARGS.setdefault(model, []).extend(unsupported_kwargs)
+
+
+def _get_unsupported_text_generation_kwargs(model: Optional[str]) -> List[str]:
+    return _UNSUPPORTED_TEXT_GENERATION_KWARGS.get(model, [])
+
+
+_NON_CHAT_COMPLETION_SERVER: Set[str] = set()
+
+
+def _set_as_non_chat_completion_server(model: str) -> None:
+    _NON_CHAT_COMPLETION_SERVER.add(model)
+
+
+def _is_chat_completion_server(model: str) -> bool:
+    return model not in _NON_CHAT_COMPLETION_SERVER
+
+
+# TEXT GENERATION ERRORS
+# ----------------------
+# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
+# inference project (https://github.com/huggingface/text-generation-inference).
+# ----------------------
+
+
+def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
+    """
+    Try to parse text-generation-inference error message and raise HTTPError in any case.
+
+    Args:
+        error (`HTTPError`):
+            The HTTPError that have been raised.
+    """
+    # Try to parse a Text Generation Inference error
+
+    try:
+        # Hacky way to retrieve payload in case of aiohttp error
+        payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
+        error = payload.get("error")
+        error_type = payload.get("error_type")
+    except Exception:  # no payload
+        raise http_error
+
+    # If error_type => more information than `hf_raise_for_status`
+    if error_type is not None:
+        exception = _parse_text_generation_error(error, error_type)
+        raise exception from http_error
+
+    # Otherwise, fallback to default error
+    raise http_error
+
+
+def _parse_text_generation_error(error: Optional[str], error_type: Optional[str]) -> TextGenerationError:
+    if error_type == "generation":
+        return GenerationError(error)  # type: ignore
+    if error_type == "incomplete_generation":
+        return IncompleteGenerationError(error)  # type: ignore
+    if error_type == "overloaded":
+        return OverloadedError(error)  # type: ignore
+    if error_type == "validation":
+        return ValidationError(error)  # type: ignore
+    return UnknownError(error)  # type: ignore

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class AudioClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Audio Classification
+    """
+
+    function_to_apply: Optional["ClassificationOutputTransform"] = None
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class AudioClassificationInput(BaseInferenceType):
+    """Inputs for Audio Classification inference"""
+
+    inputs: Any
+    """The input audio data"""
+    parameters: Optional[AudioClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class AudioClassificationOutputElement(BaseInferenceType):
+    """Outputs for Audio Classification inference"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py

@@ -0,0 +1,31 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class AudioToAudioInput(BaseInferenceType):
+    """Inputs for Audio to Audio inference"""
+
+    inputs: Any
+    """The input audio data"""
+
+
+@dataclass
+class AudioToAudioOutputElement(BaseInferenceType):
+    """Outputs of inference for the Audio To Audio task
+    A generated audio file with its label.
+    """
+
+    blob: Any
+    """The generated audio file."""
+    content_type: str
+    """The content type of audio file."""
+    label: str
+    """The label of the audio file."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py

@@ -0,0 +1,116 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Literal, Optional, Union
+
+from .base import BaseInferenceType
+
+
+EarlyStoppingEnum = Literal["never"]
+
+
+@dataclass
+class AutomaticSpeechRecognitionGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process
+    Ad-hoc parametrization of the text generation process
+    """
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "EarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over maxLength."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over maxLength."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass
+class AutomaticSpeechRecognitionParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Automatic Speech Recognition
+    """
+
+    generate: Optional[AutomaticSpeechRecognitionGenerationParameters] = None
+    """Parametrization of the text generation process"""
+    return_timestamps: Optional[bool] = None
+    """Whether to output corresponding timestamps with the generated text"""
+
+
+@dataclass
+class AutomaticSpeechRecognitionInput(BaseInferenceType):
+    """Inputs for Automatic Speech Recognition inference"""
+
+    inputs: Any
+    """The input audio data"""
+    parameters: Optional[AutomaticSpeechRecognitionParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class AutomaticSpeechRecognitionOutputChunk(BaseInferenceType):
+    text: str
+    """A chunk of text identified by the model"""
+    timestamps: List[float]
+    """The start and end timestamps corresponding with the text"""
+
+
+@dataclass
+class AutomaticSpeechRecognitionOutput(BaseInferenceType):
+    """Outputs of inference for the Automatic Speech Recognition task"""
+
+    text: str
+    """The recognized text."""
+    chunks: Optional[List[AutomaticSpeechRecognitionOutputChunk]] = None
+    """When returnTimestamps is enabled, chunks contains a list of audio chunks identified by
+    the model.
+    """

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/chat_completion.py

@@ -0,0 +1,260 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Literal, Optional, Union
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ChatCompletionInputFunctionDefinition(BaseInferenceType):
+    arguments: Any
+    name: str
+    description: Optional[str] = None
+
+
+@dataclass
+class ChatCompletionInputToolCall(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    id: int
+    type: str
+
+
+@dataclass
+class ChatCompletionInputMessage(BaseInferenceType):
+    role: str
+    content: Optional[str] = None
+    name: Optional[str] = None
+    tool_calls: Optional[List[ChatCompletionInputToolCall]] = None
+
+
+@dataclass
+class ChatCompletionInputToolTypeClass(BaseInferenceType):
+    function_name: str
+
+
+ChatCompletionInputToolTypeEnum = Literal["OneOf"]
+
+
+@dataclass
+class ChatCompletionInputTool(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    type: str
+
+
+@dataclass
+class ChatCompletionInput(BaseInferenceType):
+    """Chat Completion Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    messages: List[ChatCompletionInputMessage]
+    """A list of messages comprising the conversation so far."""
+    model: str
+    """[UNUSED] ID of the model to use. See the model endpoint compatibility table for details
+    on which models work with the Chat API.
+    """
+    frequency_penalty: Optional[float] = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing
+    frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    logit_bias: Optional[List[float]] = None
+    """UNUSED
+    Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON
+    object that maps tokens
+    (specified by their token ID in the tokenizer) to an associated bias value from -100 to
+    100. Mathematically,
+    the bias is added to the logits generated by the model prior to sampling. The exact
+    effect will vary per model,
+    but values between -1 and 1 should decrease or increase likelihood of selection; values
+    like -100 or 100 should
+    result in a ban or exclusive selection of the relevant token.
+    """
+    logprobs: Optional[bool] = None
+    """Whether to return log probabilities of the output tokens or not. If true, returns the log
+    probabilities of each
+    output token returned in the content of message.
+    """
+    max_tokens: Optional[int] = None
+    """The maximum number of tokens that can be generated in the chat completion."""
+    n: Optional[int] = None
+    """UNUSED
+    How many chat completion choices to generate for each input message. Note that you will
+    be charged based on the
+    number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
+    """
+    presence_penalty: Optional[float] = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they
+    appear in the text so far,
+    increasing the model's likelihood to talk about new topics
+    """
+    seed: Optional[int] = None
+    stop: Optional[List[str]] = None
+    """Up to 4 sequences where the API will stop generating further tokens."""
+    stream: Optional[bool] = None
+    temperature: Optional[float] = None
+    """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the
+    output more random, while
+    lower values like 0.2 will make it more focused and deterministic.
+    We generally recommend altering this or `top_p` but not both.
+    """
+    tool_choice: Optional[Union[ChatCompletionInputToolTypeClass, "ChatCompletionInputToolTypeEnum"]] = None
+    tool_prompt: Optional[str] = None
+    """A prompt to be appended before the tools"""
+    tools: Optional[List[ChatCompletionInputTool]] = None
+    """A list of tools the model may call. Currently, only functions are supported as a tool.
+    Use this to provide a list of
+    functions the model may generate JSON inputs for.
+    """
+    top_logprobs: Optional[int] = None
+    """An integer between 0 and 5 specifying the number of most likely tokens to return at each
+    token position, each with
+    an associated log probability. logprobs must be set to true if this parameter is used.
+    """
+    top_p: Optional[float] = None
+    """An alternative to sampling with temperature, called nucleus sampling, where the model
+    considers the results of the
+    tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%
+    probability mass are considered.
+    """
+
+
+@dataclass
+class ChatCompletionOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass
+class ChatCompletionOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: List[ChatCompletionOutputTopLogprob]
+
+
+@dataclass
+class ChatCompletionOutputLogprobs(BaseInferenceType):
+    content: List[ChatCompletionOutputLogprob]
+
+
+@dataclass
+class ChatCompletionOutputFunctionDefinition(BaseInferenceType):
+    arguments: Any
+    name: str
+    description: Optional[str] = None
+
+
+@dataclass
+class ChatCompletionOutputToolCall(BaseInferenceType):
+    function: ChatCompletionOutputFunctionDefinition
+    id: int
+    type: str
+
+
+@dataclass
+class ChatCompletionOutputMessage(BaseInferenceType):
+    role: str
+    content: Optional[str] = None
+    name: Optional[str] = None
+    tool_calls: Optional[List[ChatCompletionOutputToolCall]] = None
+
+
+@dataclass
+class ChatCompletionOutputComplete(BaseInferenceType):
+    finish_reason: str
+    index: int
+    message: ChatCompletionOutputMessage
+    logprobs: Optional[ChatCompletionOutputLogprobs] = None
+
+
+@dataclass
+class ChatCompletionOutputUsage(BaseInferenceType):
+    completion_tokens: int
+    prompt_tokens: int
+    total_tokens: int
+
+
+@dataclass
+class ChatCompletionOutput(BaseInferenceType):
+    """Chat Completion Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: List[ChatCompletionOutputComplete]
+    created: int
+    id: str
+    model: str
+    object: str
+    system_fingerprint: str
+    usage: ChatCompletionOutputUsage
+
+
+@dataclass
+class ChatCompletionStreamOutputFunction(BaseInferenceType):
+    arguments: str
+    name: Optional[str] = None
+
+
+@dataclass
+class ChatCompletionStreamOutputDeltaToolCall(BaseInferenceType):
+    function: ChatCompletionStreamOutputFunction
+    id: str
+    index: int
+    type: str
+
+
+@dataclass
+class ChatCompletionStreamOutputDelta(BaseInferenceType):
+    role: str
+    content: Optional[str] = None
+    tool_calls: Optional[ChatCompletionStreamOutputDeltaToolCall] = None
+
+
+@dataclass
+class ChatCompletionStreamOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass
+class ChatCompletionStreamOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: List[ChatCompletionStreamOutputTopLogprob]
+
+
+@dataclass
+class ChatCompletionStreamOutputLogprobs(BaseInferenceType):
+    content: List[ChatCompletionStreamOutputLogprob]
+
+
+@dataclass
+class ChatCompletionStreamOutputChoice(BaseInferenceType):
+    delta: ChatCompletionStreamOutputDelta
+    index: int
+    finish_reason: Optional[str] = None
+    logprobs: Optional[ChatCompletionStreamOutputLogprobs] = None
+
+
+@dataclass
+class ChatCompletionStreamOutput(BaseInferenceType):
+    """Chat Completion Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: List[ChatCompletionStreamOutputChoice]
+    created: int
+    id: str
+    model: str
+    object: str
+    system_fingerprint: str

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/depth_estimation.py

@@ -0,0 +1,29 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class DepthEstimationInput(BaseInferenceType):
+    """Inputs for Depth Estimation inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class DepthEstimationOutput(BaseInferenceType):
+    """Outputs of inference for the Depth Estimation task"""
+
+    depth: Any
+    """The predicted depth as an image"""
+    predicted_depth: Any
+    """The predicted depth as a tensor"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py

@@ -0,0 +1,85 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Optional, Union
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class DocumentQuestionAnsweringInputData(BaseInferenceType):
+    """One (document, question) pair to answer"""
+
+    image: Any
+    """The image on which the question is asked"""
+    question: str
+    """A question to ask of the document"""
+
+
+@dataclass
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Document Question Answering
+    """
+
+    doc_stride: Optional[int] = None
+    """If the words in the document are too long to fit with the question for the model, it will
+    be split in several chunks with some overlap. This argument controls the size of that
+    overlap.
+    """
+    handle_impossible_answer: Optional[bool] = None
+    """Whether to accept impossible as an answer"""
+    lang: Optional[str] = None
+    """Language to use while running OCR. Defaults to english."""
+    max_answer_len: Optional[int] = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: Optional[int] = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: Optional[int] = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using doc_stride as
+    overlap) if needed.
+    """
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Can return less
+    than top_k answers if there are not enough options available within the context.
+    """
+    word_boxes: Optional[List[Union[List[float], str]]] = None
+    """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will
+    skip the OCR step and use the provided bounding boxes instead.
+    """
+
+
+@dataclass
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Document Question Answering inference"""
+
+    inputs: DocumentQuestionAnsweringInputData
+    """One (document, question) pair to answer"""
+    parameters: Optional[DocumentQuestionAnsweringParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class DocumentQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Document Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The end word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The start word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    words: List[int]
+    """The index of each word/box pair that is in the answer"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py

@@ -0,0 +1,19 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class FeatureExtractionInput(BaseInferenceType):
+    """Inputs for Text Embedding inference"""
+
+    inputs: str
+    """The text to get the embeddings of"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py

@@ -0,0 +1,50 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class FillMaskParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Fill Mask
+    """
+
+    targets: Optional[List[str]] = None
+    """When passed, the model will limit the scores to the passed targets instead of looking up
+    in the whole vocabulary. If the provided targets are not in the model vocab, they will be
+    tokenized and the first resulting token will be used (with a warning, and that might be
+    slower).
+    """
+    top_k: Optional[int] = None
+    """When passed, overrides the number of predictions to return."""
+
+
+@dataclass
+class FillMaskInput(BaseInferenceType):
+    """Inputs for Fill Mask inference"""
+
+    inputs: str
+    """The text with masked tokens"""
+    parameters: Optional[FillMaskParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class FillMaskOutputElement(BaseInferenceType):
+    """Outputs of inference for the Fill Mask task"""
+
+    score: float
+    """The corresponding probability"""
+    sequence: str
+    """The corresponding input with the mask token prediction."""
+    token: int
+    """The predicted token id (to replace the masked one)."""
+    token_str: Any
+    fill_mask_output_token_str: Optional[str] = None
+    """The predicted token (to replace the masked one)."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_classification.py

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class ImageClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image Classification
+    """
+
+    function_to_apply: Optional["ClassificationOutputTransform"] = None
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class ImageClassificationInput(BaseInferenceType):
+    """Inputs for Image Classification inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py

@@ -0,0 +1,52 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass
+class ImageSegmentationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image Segmentation
+    """
+
+    mask_threshold: Optional[float] = None
+    """Threshold to use when turning the predicted masks into binary values."""
+    overlap_mask_area_threshold: Optional[float] = None
+    """Mask overlap threshold to eliminate small, disconnected segments."""
+    subtask: Optional["ImageSegmentationSubtask"] = None
+    """Segmentation task to be performed, depending on model capabilities."""
+    threshold: Optional[float] = None
+    """Probability threshold to filter out predicted masks."""
+
+
+@dataclass
+class ImageSegmentationInput(BaseInferenceType):
+    """Inputs for Image Segmentation inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageSegmentationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageSegmentationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Segmentation task
+    A predicted mask / segment
+    """
+
+    label: str
+    """The label of the predicted segment"""
+    mask: Any
+    """The corresponding mask as a black-and-white image"""
+    score: Optional[float] = None
+    """The score or confidence degreee the model has"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py

@@ -0,0 +1,55 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ImageToImageTargetSize(BaseInferenceType):
+    """The size in pixel of the output image"""
+
+    height: int
+    width: int
+
+
+@dataclass
+class ImageToImageParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image To Image
+    """
+
+    guidance_scale: Optional[float] = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    images closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: Optional[List[str]] = None
+    """One or several prompt to guide what NOT to include in image generation."""
+    num_inference_steps: Optional[int] = None
+    """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+    a higher quality image at the expense of slower inference.
+    """
+    target_size: Optional[ImageToImageTargetSize] = None
+    """The size in pixel of the output image"""
+
+
+@dataclass
+class ImageToImageInput(BaseInferenceType):
+    """Inputs for Image To Image inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageToImageParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Image task"""
+
+    image: Any
+    """The output image"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/object_detection.py

@@ -0,0 +1,55 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ObjectDetectionParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Object Detection
+    """
+
+    threshold: Optional[float] = None
+    """The probability necessary to make a prediction."""
+
+
+@dataclass
+class ObjectDetectionInput(BaseInferenceType):
+    """Inputs for Object Detection inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ObjectDetectionParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ObjectDetectionBoundingBox(BaseInferenceType):
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+
+    xmax: int
+    xmin: int
+    ymax: int
+    ymin: int
+
+
+@dataclass
+class ObjectDetectionOutputElement(BaseInferenceType):
+    """Outputs of inference for the Object Detection task"""
+
+    box: ObjectDetectionBoundingBox
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+    label: str
+    """The predicted label for the bounding box"""
+    score: float
+    """The associated score / probability"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/question_answering.py

@@ -0,0 +1,77 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class QuestionAnsweringInputData(BaseInferenceType):
+    """One (context, question) pair to answer"""
+
+    context: str
+    """The context to be used for answering the question"""
+    question: str
+    """The question to be answered"""
+
+
+@dataclass
+class QuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Question Answering
+    """
+
+    align_to_words: Optional[bool] = None
+    """Attempts to align the answer to real words. Improves quality on space separated
+    languages. Might hurt on non-space-separated languages (like Japanese or Chinese)
+    """
+    doc_stride: Optional[int] = None
+    """If the context is too long to fit with the question for the model, it will be split in
+    several chunks with some overlap. This argument controls the size of that overlap.
+    """
+    handle_impossible_answer: Optional[bool] = None
+    """Whether to accept impossible as an answer."""
+    max_answer_len: Optional[int] = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: Optional[int] = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: Optional[int] = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using docStride as
+    overlap) if needed.
+    """
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Note that we
+    return less than topk answers if there are not enough options available within the
+    context.
+    """
+
+
+@dataclass
+class QuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Question Answering inference"""
+
+    inputs: QuestionAnsweringInputData
+    """One (context, question) pair to answer"""
+    parameters: Optional[QuestionAnsweringParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class QuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The character position in the input where the answer ends."""
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The character position in the input where the answer begins."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/sentence_similarity.py

@@ -0,0 +1,28 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class SentenceSimilarityInputData(BaseInferenceType):
+    sentences: List[str]
+    """A list of strings which will be compared against the source_sentence."""
+    source_sentence: str
+    """The string that you wish to compare the other strings with. This can be a phrase,
+    sentence, or longer passage, depending on the model being used.
+    """
+
+
+@dataclass
+class SentenceSimilarityInput(BaseInferenceType):
+    """Inputs for Sentence similarity inference"""
+
+    inputs: SentenceSimilarityInputData
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class TableQuestionAnsweringInputData(BaseInferenceType):
+    """One (table, question) pair to answer"""
+
+    question: str
+    """The question to be answered about the table"""
+    table: Dict[str, List[str]]
+    """The table to serve as context for the questions"""
+
+
+@dataclass
+class TableQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Table Question Answering inference"""
+
+    inputs: TableQuestionAnsweringInputData
+    """One (table, question) pair to answer"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TableQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Table Question Answering task"""
+
+    answer: str
+    """The answer of the question given the table. If there is an aggregator, the answer will be
+    preceded by `AGGREGATOR >`.
+    """
+    cells: List[str]
+    """List of strings made up of the answer cell values."""
+    coordinates: List[List[int]]
+    """Coordinates of the cells of the answers."""
+    aggregator: Optional[str] = None
+    """If the model has an aggregator, this returns the aggregator."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass
+class Text2TextGenerationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text2text Generation
+    """
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm"""
+    truncation: Optional["Text2TextGenerationTruncationStrategy"] = None
+    """The truncation strategy to use"""
+
+
+@dataclass
+class Text2TextGenerationInput(BaseInferenceType):
+    """Inputs for Text2text Generation inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[Text2TextGenerationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class Text2TextGenerationOutput(BaseInferenceType):
+    """Outputs of inference for the Text2text Generation task"""
+
+    generated_text: Any
+    text2_text_generation_output_generated_text: Optional[str] = None
+    """The generated text."""

llmeval-env/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_classification.py

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class TextClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text Classification
+    """
+
+    function_to_apply: Optional["ClassificationOutputTransform"] = None
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class TextClassificationInput(BaseInferenceType):
+    """Inputs for Text Classification inference"""
+
+    inputs: str
+    """The text to classify"""
+    parameters: Optional[TextClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TextClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Text Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""