# Copyright 2020 The HuggingFace Team, the AllenNLP library authors. 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.
"""
Utilities for working with the local dataset cache. Parts of this file is adapted from the AllenNLP library at
https://github.com/allenai/allennlp.
"""
import copy
import fnmatch
import functools
import importlib.util
import io
import json
import os
import re
import shutil
import subprocess
import sys
import tarfile
import tempfile
import types
from collections import OrderedDict, UserDict
from contextlib import contextmanager
from dataclasses import fields
from enum import Enum
from functools import partial, wraps
from hashlib import sha256
from pathlib import Path
from types import ModuleType
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from urllib.parse import urlparse
from uuid import uuid4
from zipfile import ZipFile, is_zipfile

import numpy as np
from tqdm.auto import tqdm

import requests
from filelock import FileLock
from huggingface_hub import HfApi, HfFolder, Repository

# from . import
__version__ = "4.10.0.dev0"

from .utils import logging

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})

# New default cache, shared with the Datasets library
hf_cache_home = os.path.expanduser(
    os.getenv(
        "HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface")
    )
)
TRANSFORMERS_CACHE = os.path.join(hf_cache_home, "transformers")

SESSION_ID = uuid4().hex
DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", False) in ENV_VARS_TRUE_VALUES

CONFIG_NAME = "config.json"
FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
MODEL_CARD_NAME = "modelcard.json"

MULTIPLE_CHOICE_DUMMY_INPUTS = [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"

_staging_mode = (
    os.environ.get("HUGGINGFACE_CO_STAGING", "NO").upper() in ENV_VARS_TRUE_VALUES
)
_default_endpoint = (
    "https://moon-staging.huggingface.co" if _staging_mode else "https://huggingface.co"
)

HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get(
    "HUGGINGFACE_CO_RESOLVE_ENDPOINT", _default_endpoint
)
HUGGINGFACE_CO_PREFIX = (
    HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/{model_id}/resolve/{revision}/{filename}"
)

PRESET_MIRROR_DICT = {
    "tuna": "https://mirrors.tuna.tsinghua.edu.cn/hugging-face-models",
    "bfsu": "https://mirrors.bfsu.edu.cn/hugging-face-models",
}

# This is the version of torch required to run torch.fx features and torch.onnx with dictionary inputs.

_is_offline_mode = (
    True
    if os.environ.get("TRANSFORMERS_OFFLINE", "0").upper() in ENV_VARS_TRUE_VALUES
    else False
)


def is_offline_mode():
    return _is_offline_mode


# docstyle-ignore
DATASETS_IMPORT_ERROR = """
{0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with:
```
pip install datasets
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install datasets
```
then restarting your kernel.

Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current
working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or
that python file if that's the case.
"""

# docstyle-ignore
TOKENIZERS_IMPORT_ERROR = """
{0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
```
pip install tokenizers
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install tokenizers
```
"""

# docstyle-ignore
SENTENCEPIECE_IMPORT_ERROR = """
{0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
that match your environment.
"""

# docstyle-ignore
PROTOBUF_IMPORT_ERROR = """
{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
that match your environment.
"""

# docstyle-ignore
FAISS_IMPORT_ERROR = """
{0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
that match your environment.
"""

# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""

# docstyle-ignore
SKLEARN_IMPORT_ERROR = """
{0} requires the scikit-learn library but it was not found in your environment. You can install it with:
```
pip install -U scikit-learn
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install -U scikit-learn
```
"""

# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = """
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
"""

# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""

# docstyle-ignore
SCATTER_IMPORT_ERROR = """
{0} requires the torch-scatter library but it was not found in your environment. You can install it with pip as
explained here: https://github.com/rusty1s/pytorch_scatter.
"""

# docstyle-ignore
PANDAS_IMPORT_ERROR = """
{0} requires the pandas library but it was not found in your environment. You can install it with pip as
explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html.
"""

# docstyle-ignore
SCIPY_IMPORT_ERROR = """
{0} requires the scipy library but it was not found in your environment. You can install it with pip:
`pip install scipy`
"""

# docstyle-ignore
SPEECH_IMPORT_ERROR = """
{0} requires the torchaudio library but it was not found in your environment. You can install it with pip:
`pip install torchaudio`
"""

# docstyle-ignore
TIMM_IMPORT_ERROR = """
{0} requires the timm library but it was not found in your environment. You can install it with pip:
`pip install timm`
"""

# docstyle-ignore
VISION_IMPORT_ERROR = """
{0} requires the PIL library but it was not found in your environment. You can install it with pip:
`pip install pillow`
"""


def _get_indent(t):
    """Returns the indentation in the first line of t"""
    search = re.search(r"^(\s*)\S", t)
    return "" if search is None else search.groups()[0]


def is_remote_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")


def hf_bucket_url(
    model_id: str,
    filename: str,
    subfolder: Optional[str] = None,
    revision: Optional[str] = None,
    mirror=None,
) -> str:
    """
    Resolve a model identifier, a file name, and an optional revision id, to a huggingface.co-hosted url, redirecting
    to Cloudfront (a Content Delivery Network, or CDN) for large files.

    Cloudfront is replicated over the globe so downloads are way faster for the end user (and it also lowers our
    bandwidth costs).

    Cloudfront aggressively caches files by default (default TTL is 24 hours), however this is not an issue here
    because we migrated to a git-based versioning system on huggingface.co, so we now store the files on S3/Cloudfront
    in a content-addressable way (i.e., the file name is its hash). Using content-addressable filenames means cache
    can't ever be stale.

    In terms of client-side caching from this library, we base our caching on the objects' ETag. An object' ETag is:
    its sha1 if stored in git, or its sha256 if stored in git-lfs. Files cached locally from transformers before v3.5.0
    are not shared with those new files, because the cached file's name contains a hash of the url (which changed).
    """
    if subfolder is not None:
        filename = f"{subfolder}/{filename}"

    if mirror:
        endpoint = PRESET_MIRROR_DICT.get(mirror, mirror)
        legacy_format = "/" not in model_id
        if legacy_format:
            return f"{endpoint}/{model_id}-{filename}"
        else:
            return f"{endpoint}/{model_id}/{filename}"

    if revision is None:
        revision = "main"
    return HUGGINGFACE_CO_PREFIX.format(
        model_id=model_id, revision=revision, filename=filename
    )


def url_to_filename(url: str, etag: Optional[str] = None) -> str:
    """
    Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's,
    delimited by a period. If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can
    identify it as a HDF5 file (see
    https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
    """
    url_bytes = url.encode("utf-8")
    filename = sha256(url_bytes).hexdigest()

    if etag:
        etag_bytes = etag.encode("utf-8")
        filename += "." + sha256(etag_bytes).hexdigest()

    if url.endswith(".h5"):
        filename += ".h5"

    return filename


def filename_to_url(filename, cache_dir=None):
    """
    Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or
    its stored metadata do not exist.
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    cache_path = os.path.join(cache_dir, filename)
    if not os.path.exists(cache_path):
        raise EnvironmentError(f"file {cache_path} not found")

    meta_path = cache_path + ".json"
    if not os.path.exists(meta_path):
        raise EnvironmentError(f"file {meta_path} not found")

    with open(meta_path, encoding="utf-8") as meta_file:
        metadata = json.load(meta_file)
    url = metadata["url"]
    etag = metadata["etag"]

    return url, etag


def get_cached_models(cache_dir: Union[str, Path] = None) -> List[Tuple]:
    """
    Returns a list of tuples representing model binaries that are cached locally. Each tuple has shape
    :obj:`(model_url, etag, size_MB)`. Filenames in :obj:`cache_dir` are use to get the metadata for each model, only
    urls ending with `.bin` are added.

    Args:
        cache_dir (:obj:`Union[str, Path]`, `optional`):
            The cache directory to search for models within. Will default to the transformers cache if unset.

    Returns:
        List[Tuple]: List of tuples each with shape :obj:`(model_url, etag, size_MB)`
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    elif isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    cached_models = []
    for file in os.listdir(cache_dir):
        if file.endswith(".json"):
            meta_path = os.path.join(cache_dir, file)
            with open(meta_path, encoding="utf-8") as meta_file:
                metadata = json.load(meta_file)
                url = metadata["url"]
                etag = metadata["etag"]
                if url.endswith(".bin"):
                    size_MB = os.path.getsize(meta_path.strip(".json")) / 1e6
                    cached_models.append((url, etag, size_MB))

    return cached_models


def cached_path(
    url_or_filename,
    cache_dir=None,
    force_download=False,
    proxies=None,
    resume_download=False,
    user_agent: Union[Dict, str, None] = None,
    extract_compressed_file=False,
    force_extract=False,
    use_auth_token: Union[bool, str, None] = None,
    local_files_only=False,
) -> Optional[str]:
    """
    Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file
    and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and
    then return the path

    Args:
        cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
        force_download: if True, re-download the file even if it's already cached in the cache dir.
        resume_download: if True, resume the download if incompletely received file is found.
        user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
        use_auth_token: Optional string or boolean to use as Bearer token for remote files. If True,
            will get token from ~/.huggingface.
        extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
            file in a folder along the archive.
        force_extract: if True when extract_compressed_file is True and the archive was already extracted,
            re-extract the archive and override the folder where it was extracted.

    Return:
        Local path (string) of file or if networking is off, last version of file cached on disk.

    Raises:
        In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(url_or_filename, Path):
        url_or_filename = str(url_or_filename)
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    if is_offline_mode() and not local_files_only:
        logger.info("Offline mode: forcing local_files_only=True")
        local_files_only = True

    if is_remote_url(url_or_filename):
        # URL, so get it from the cache (downloading if necessary)
        output_path = get_from_cache(
            url_or_filename,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            resume_download=resume_download,
            user_agent=user_agent,
            use_auth_token=use_auth_token,
            local_files_only=local_files_only,
        )
    elif os.path.exists(url_or_filename):
        # File, and it exists.
        output_path = url_or_filename
    elif urlparse(url_or_filename).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError(f"file {url_or_filename} not found")
    else:
        # Something unknown
        raise ValueError(
            f"unable to parse {url_or_filename} as a URL or as a local path"
        )

    if extract_compressed_file:
        if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        output_dir, output_file = os.path.split(output_path)
        output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
        output_path_extracted = os.path.join(output_dir, output_extract_dir_name)

        if (
            os.path.isdir(output_path_extracted)
            and os.listdir(output_path_extracted)
            and not force_extract
        ):
            return output_path_extracted

        # Prevent parallel extractions
        lock_path = output_path + ".lock"
        with FileLock(lock_path):
            shutil.rmtree(output_path_extracted, ignore_errors=True)
            os.makedirs(output_path_extracted)
            if is_zipfile(output_path):
                with ZipFile(output_path, "r") as zip_file:
                    zip_file.extractall(output_path_extracted)
                    zip_file.close()
            elif tarfile.is_tarfile(output_path):
                tar_file = tarfile.open(output_path)
                tar_file.extractall(output_path_extracted)
                tar_file.close()
            else:
                raise EnvironmentError(
                    f"Archive format of {output_path} could not be identified"
                )

        return output_path_extracted

    return output_path


def define_sagemaker_information():
    try:
        instance_data = requests.get(os.environ["ECS_CONTAINER_METADATA_URI"]).json()
        dlc_container_used = instance_data["Image"]
        dlc_tag = instance_data["Image"].split(":")[1]
    except Exception:
        dlc_container_used = None
        dlc_tag = None

    sagemaker_params = json.loads(os.getenv("SM_FRAMEWORK_PARAMS", "{}"))
    runs_distributed_training = (
        True
        if "sagemaker_distributed_dataparallel_enabled" in sagemaker_params
        else False
    )
    account_id = (
        os.getenv("TRAINING_JOB_ARN").split(":")[4]
        if "TRAINING_JOB_ARN" in os.environ
        else None
    )

    sagemaker_object = {
        "sm_framework": os.getenv("SM_FRAMEWORK_MODULE", None),
        "sm_region": os.getenv("AWS_REGION", None),
        "sm_number_gpu": os.getenv("SM_NUM_GPUS", 0),
        "sm_number_cpu": os.getenv("SM_NUM_CPUS", 0),
        "sm_distributed_training": runs_distributed_training,
        "sm_deep_learning_container": dlc_container_used,
        "sm_deep_learning_container_tag": dlc_tag,
        "sm_account_id": account_id,
    }
    return sagemaker_object


def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
    """
    Formats a user-agent string with basic info about a request.
    """
    ua = f"transformers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
    if DISABLE_TELEMETRY:
        return ua + "; telemetry/off"
    # CI will set this value to True
    if os.environ.get("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
        ua += "; is_ci/true"
    if isinstance(user_agent, dict):
        ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
    elif isinstance(user_agent, str):
        ua += "; " + user_agent
    return ua


def http_get(
    url: str,
    temp_file: BinaryIO,
    proxies=None,
    resume_size=0,
    headers: Optional[Dict[str, str]] = None,
):
    """
    Download remote file. Do not gobble up errors.
    """
    headers = copy.deepcopy(headers)
    if resume_size > 0:
        headers["Range"] = f"bytes={resume_size}-"
    r = requests.get(url, stream=True, proxies=proxies, headers=headers)
    r.raise_for_status()
    content_length = r.headers.get("Content-Length")
    total = resume_size + int(content_length) if content_length is not None else None
    progress = tqdm(
        unit="B",
        unit_scale=True,
        total=total,
        initial=resume_size,
        desc="Downloading",
        disable=bool(logging.get_verbosity() == logging.NOTSET),
    )
    for chunk in r.iter_content(chunk_size=1024):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(chunk))
            temp_file.write(chunk)
    progress.close()


def get_from_cache(
    url: str,
    cache_dir=None,
    force_download=False,
    proxies=None,
    etag_timeout=10,
    resume_download=False,
    user_agent: Union[Dict, str, None] = None,
    use_auth_token: Union[bool, str, None] = None,
    local_files_only=False,
) -> Optional[str]:
    """
    Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the
    path to the cached file.

    Return:
        Local path (string) of file or if networking is off, last version of file cached on disk.

    Raises:
        In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    os.makedirs(cache_dir, exist_ok=True)

    headers = {"user-agent": http_user_agent(user_agent)}
    if isinstance(use_auth_token, str):
        headers["authorization"] = f"Bearer {use_auth_token}"
    elif use_auth_token:
        token = HfFolder.get_token()
        if token is None:
            raise EnvironmentError(
                "You specified use_auth_token=True, but a huggingface token was not found."
            )
        headers["authorization"] = f"Bearer {token}"

    url_to_download = url
    etag = None
    if not local_files_only:
        try:
            r = requests.head(
                url,
                headers=headers,
                allow_redirects=False,
                proxies=proxies,
                timeout=etag_timeout,
            )
            r.raise_for_status()
            etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")
            # We favor a custom header indicating the etag of the linked resource, and
            # we fallback to the regular etag header.
            # If we don't have any of those, raise an error.
            if etag is None:
                raise OSError(
                    "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
                )
            # In case of a redirect,
            # save an extra redirect on the request.get call,
            # and ensure we download the exact atomic version even if it changed
            # between the HEAD and the GET (unlikely, but hey).
            if 300 <= r.status_code <= 399:
                url_to_download = r.headers["Location"]
        except (requests.exceptions.SSLError, requests.exceptions.ProxyError):
            # Actually raise for those subclasses of ConnectionError
            raise
        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
            # Otherwise, our Internet connection is down.
            # etag is None
            pass

    filename = url_to_filename(url, etag)

    # get cache path to put the file
    cache_path = os.path.join(cache_dir, filename)

    # etag is None == we don't have a connection or we passed local_files_only.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(cache_path):
            return cache_path
        else:
            matching_files = [
                file
                for file in fnmatch.filter(
                    os.listdir(cache_dir), filename.split(".")[0] + ".*"
                )
                if not file.endswith(".json") and not file.endswith(".lock")
            ]
            if len(matching_files) > 0:
                return os.path.join(cache_dir, matching_files[-1])
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise FileNotFoundError(
                        "Cannot find the requested files in the cached path and outgoing traffic has been"
                        " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
                        " to False."
                    )
                else:
                    raise ValueError(
                        "Connection error, and we cannot find the requested files in the cached path."
                        " Please try again or make sure your Internet connection is on."
                    )

    # From now on, etag is not None.
    if os.path.exists(cache_path) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    lock_path = cache_path + ".lock"
    with FileLock(lock_path):

        # If the download just completed while the lock was activated.
        if os.path.exists(cache_path) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            incomplete_path = cache_path + ".incomplete"

            @contextmanager
            def _resumable_file_manager() -> "io.BufferedWriter":
                with open(incomplete_path, "ab") as f:
                    yield f

            temp_file_manager = _resumable_file_manager
            if os.path.exists(incomplete_path):
                resume_size = os.stat(incomplete_path).st_size
            else:
                resume_size = 0
        else:
            temp_file_manager = partial(
                tempfile.NamedTemporaryFile, mode="wb", dir=cache_dir, delete=False
            )
            resume_size = 0

        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with temp_file_manager() as temp_file:
            logger.info(
                f"{url} not found in cache or force_download set to True, downloading to {temp_file.name}"
            )

            http_get(
                url_to_download,
                temp_file,
                proxies=proxies,
                resume_size=resume_size,
                headers=headers,
            )

        logger.info(f"storing {url} in cache at {cache_path}")
        os.replace(temp_file.name, cache_path)

        # NamedTemporaryFile creates a file with hardwired 0600 perms (ignoring umask), so fixing it.
        umask = os.umask(0o666)
        os.umask(umask)
        os.chmod(cache_path, 0o666 & ~umask)

        logger.info(f"creating metadata file for {cache_path}")
        meta = {"url": url, "etag": etag}
        meta_path = cache_path + ".json"
        with open(meta_path, "w") as meta_file:
            json.dump(meta, meta_file)

    return cache_path


def get_list_of_files(
    path_or_repo: Union[str, os.PathLike],
    revision: Optional[str] = None,
    use_auth_token: Optional[Union[bool, str]] = None,
) -> List[str]:
    """
    Gets the list of files inside :obj:`path_or_repo`.

    Args:
        path_or_repo (:obj:`str` or :obj:`os.PathLike`):
            Can be either the id of a repo on huggingface.co or a path to a `directory`.
        revision (:obj:`str`, `optional`, defaults to :obj:`"main"`):
            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
            git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
            identifier allowed by git.
        use_auth_token (:obj:`str` or `bool`, `optional`):
            The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
            generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).

    Returns:
        :obj:`List[str]`: The list of files available in :obj:`path_or_repo`.
    """
    path_or_repo = str(path_or_repo)
    # If path_or_repo is a folder, we just return what is inside (subdirectories included).
    if os.path.isdir(path_or_repo):
        list_of_files = []
        for path, dir_names, file_names in os.walk(path_or_repo):
            list_of_files.extend([os.path.join(path, f) for f in file_names])
        return list_of_files

    # Can't grab the files if we are on offline mode.
    if is_offline_mode():
        return []

    # Otherwise we grab the token and use the model_info method.
    if isinstance(use_auth_token, str):
        token = use_auth_token
    elif use_auth_token is True:
        token = HfFolder.get_token()
    else:
        token = None
    model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(
        path_or_repo, revision=revision, token=token
    )
    return [f.rfilename for f in model_info.siblings]


class cached_property(property):
    """
    Descriptor that mimics @property but caches output in member variable.

    From tensorflow_datasets

    Built-in in functools from Python 3.8.
    """

    def __get__(self, obj, objtype=None):
        # See docs.python.org/3/howto/descriptor.html#properties
        if obj is None:
            return self
        if self.fget is None:
            raise AttributeError("unreadable attribute")
        attr = "__cached_" + self.fget.__name__
        cached = getattr(obj, attr, None)
        if cached is None:
            cached = self.fget(obj)
            setattr(obj, attr, cached)
        return cached


def is_tensor(x):
    """
    Tests if ``x`` is :obj:`np.ndarray`.
    """

    return isinstance(x, np.ndarray)


def _is_numpy(x):
    return isinstance(x, np.ndarray)


def to_py_obj(obj):
    """
    Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
    """
    if isinstance(obj, (dict, UserDict)):
        return {k: to_py_obj(v) for k, v in obj.items()}
    elif isinstance(obj, (list, tuple)):
        return [to_py_obj(o) for o in obj]
    elif isinstance(obj, np.ndarray):
        return obj.tolist()
    else:
        return obj


class ModelOutput(OrderedDict):
    """
    Base class for all model outputs as dataclass. Has a ``__getitem__`` that allows indexing by integer or slice (like
    a tuple) or strings (like a dictionary) that will ignore the ``None`` attributes. Otherwise behaves like a regular
    python dictionary.

    .. warning::
        You can't unpack a :obj:`ModelOutput` directly. Use the :meth:`~transformers.file_utils.ModelOutput.to_tuple`
        method to convert it to a tuple before.
    """

    def __post_init__(self):
        class_fields = fields(self)

        # Safety and consistency checks
        assert len(class_fields), f"{self.__class__.__name__} has no fields."
        assert all(
            field.default is None for field in class_fields[1:]
        ), f"{self.__class__.__name__} should not have more than one required field."

        first_field = getattr(self, class_fields[0].name)
        other_fields_are_none = all(
            getattr(self, field.name) is None for field in class_fields[1:]
        )

        if other_fields_are_none and not is_tensor(first_field):
            try:
                iterator = iter(first_field)
                first_field_iterator = True
            except TypeError:
                first_field_iterator = False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
            if first_field_iterator:
                for element in iterator:
                    if (
                        not isinstance(element, (list, tuple))
                        or not len(element) == 2
                        or not isinstance(element[0], str)
                    ):
                        break
                    setattr(self, element[0], element[1])
                    if element[1] is not None:
                        self[element[0]] = element[1]
            elif first_field is not None:
                self[class_fields[0].name] = first_field
        else:
            for field in class_fields:
                v = getattr(self, field.name)
                if v is not None:
                    self[field.name] = v

    def __delitem__(self, *args, **kwargs):
        raise Exception(
            f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."
        )

    def setdefault(self, *args, **kwargs):
        raise Exception(
            f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."
        )

    def pop(self, *args, **kwargs):
        raise Exception(
            f"You cannot use ``pop`` on a {self.__class__.__name__} instance."
        )

    def update(self, *args, **kwargs):
        raise Exception(
            f"You cannot use ``update`` on a {self.__class__.__name__} instance."
        )

    def __getitem__(self, k):
        if isinstance(k, str):
            inner_dict = {k: v for (k, v) in self.items()}
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

    def __setattr__(self, name, value):
        if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(name, value)
        super().__setattr__(name, value)

    def __setitem__(self, key, value):
        # Will raise a KeyException if needed
        super().__setitem__(key, value)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(key, value)

    def to_tuple(self) -> Tuple[Any]:
        """
        Convert self to a tuple containing all the attributes/keys that are not ``None``.
        """
        return tuple(self[k] for k in self.keys())


class ExplicitEnum(Enum):
    """
    Enum with more explicit error message for missing values.
    """

    @classmethod
    def _missing_(cls, value):
        raise ValueError(
            f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
        )


class PaddingStrategy(ExplicitEnum):
    """
    Possible values for the ``padding`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for tab-completion
    in an IDE.
    """

    LONGEST = "longest"
    MAX_LENGTH = "max_length"
    DO_NOT_PAD = "do_not_pad"


class TensorType(ExplicitEnum):
    """
    Possible values for the ``return_tensors`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
    tab-completion in an IDE.
    """

    PYTORCH = "pt"
    TENSORFLOW = "tf"
    NUMPY = "np"
    JAX = "jax"


class _LazyModule(ModuleType):
    """
    Module class that surfaces all objects but only performs associated imports when the objects are requested.
    """

    # Very heavily inspired by optuna.integration._IntegrationModule
    # https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
    def __init__(self, name, module_file, import_structure, extra_objects=None):
        super().__init__(name)
        self._modules = set(import_structure.keys())
        self._class_to_module = {}
        for key, values in import_structure.items():
            for value in values:
                self._class_to_module[value] = key
        # Needed for autocompletion in an IDE
        self.__all__ = list(import_structure.keys()) + sum(
            import_structure.values(), []
        )
        self.__file__ = module_file
        self.__path__ = [os.path.dirname(module_file)]
        self._objects = {} if extra_objects is None else extra_objects
        self._name = name
        self._import_structure = import_structure

    # Needed for autocompletion in an IDE
    def __dir__(self):
        return super().__dir__() + self.__all__

    def __getattr__(self, name: str) -> Any:
        if name in self._objects:
            return self._objects[name]
        if name in self._modules:
            value = self._get_module(name)
        elif name in self._class_to_module.keys():
            module = self._get_module(self._class_to_module[name])
            value = getattr(module, name)
        else:
            raise AttributeError(f"module {self.__name__} has no attribute {name}")

        setattr(self, name, value)
        return value

    def _get_module(self, module_name: str):
        return importlib.import_module("." + module_name, self.__name__)

    def __reduce__(self):
        return (self.__class__, (self._name, self.__file__, self._import_structure))


def copy_func(f):
    """Returns a copy of a function f."""
    # Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)
    g = types.FunctionType(
        f.__code__,
        f.__globals__,
        name=f.__name__,
        argdefs=f.__defaults__,
        closure=f.__closure__,
    )
    g = functools.update_wrapper(g, f)
    g.__kwdefaults__ = f.__kwdefaults__
    return g


def is_local_clone(repo_path, repo_url):
    """
    Checks if the folder in `repo_path` is a local clone of `repo_url`.
    """
    # First double-check that `repo_path` is a git repo
    if not os.path.exists(os.path.join(repo_path, ".git")):
        return False
    test_git = subprocess.run("git branch".split(), cwd=repo_path)
    if test_git.returncode != 0:
        return False

    # Then look at its remotes
    remotes = subprocess.run(
        "git remote -v".split(),
        stderr=subprocess.PIPE,
        stdout=subprocess.PIPE,
        check=True,
        encoding="utf-8",
        cwd=repo_path,
    ).stdout

    return repo_url in remotes.split()
