# coding=utf-8
# Copyright 2021 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.
"""Utilities to dynamically load objects from the Hub."""
import filecmp
import importlib
import os
import re
import shutil
import signal
import sys
from pathlib import Path
from typing import Dict, Optional, Union

from .utils import (
    HF_MODULES_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    cached_file,
    extract_commit_hash,
    is_offline_mode,
    logging,
    try_to_load_from_cache,
)


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


def init_hf_modules():
    """
    Creates the cache directory for modules with an init, and adds it to the Python path.
    """
    # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
    if HF_MODULES_CACHE in sys.path:
        return

    sys.path.append(HF_MODULES_CACHE)
    os.makedirs(HF_MODULES_CACHE, exist_ok=True)
    init_path = Path(HF_MODULES_CACHE) / "__init__.py"
    if not init_path.exists():
        init_path.touch()
        importlib.invalidate_caches()


def create_dynamic_module(name: Union[str, os.PathLike]):
    """
    Creates a dynamic module in the cache directory for modules.
    """
    init_hf_modules()
    dynamic_module_path = Path(HF_MODULES_CACHE) / name
    # If the parent module does not exist yet, recursively create it.
    if not dynamic_module_path.parent.exists():
        create_dynamic_module(dynamic_module_path.parent)
    os.makedirs(dynamic_module_path, exist_ok=True)
    init_path = dynamic_module_path / "__init__.py"
    if not init_path.exists():
        init_path.touch()
        importlib.invalidate_caches()


def get_relative_imports(module_file):
    """
    Get the list of modules that are relatively imported in a module file.

    Args:
        module_file (`str` or `os.PathLike`): The module file to inspect.
    """
    with open(module_file, "r", encoding="utf-8") as f:
        content = f.read()

    # Imports of the form `import .xxx`
    relative_imports = re.findall(r"^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
    # Imports of the form `from .xxx import yyy`
    relative_imports += re.findall(r"^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
    # Unique-ify
    return list(set(relative_imports))


def get_relative_import_files(module_file):
    """
    Get the list of all files that are needed for a given module. Note that this function recurses through the relative
    imports (if a imports b and b imports c, it will return module files for b and c).

    Args:
        module_file (`str` or `os.PathLike`): The module file to inspect.
    """
    no_change = False
    files_to_check = [module_file]
    all_relative_imports = []

    # Let's recurse through all relative imports
    while not no_change:
        new_imports = []
        for f in files_to_check:
            new_imports.extend(get_relative_imports(f))

        module_path = Path(module_file).parent
        new_import_files = [str(module_path / m) for m in new_imports]
        new_import_files = [f for f in new_import_files if f not in all_relative_imports]
        files_to_check = [f"{f}.py" for f in new_import_files]

        no_change = len(new_import_files) == 0
        all_relative_imports.extend(files_to_check)

    return all_relative_imports


def get_imports(filename):
    """
    Extracts all the libraries that are imported in a file.
    """
    with open(filename, "r", encoding="utf-8") as f:
        content = f.read()

    # filter out try/except block so in custom code we can have try/except imports
    content = re.sub(r"\s*try\s*:\s*.*?\s*except\s*.*?:", "", content, flags=re.MULTILINE | re.DOTALL)

    # Imports of the form `import xxx`
    imports = re.findall(r"^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
    # Imports of the form `from xxx import yyy`
    imports += re.findall(r"^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
    # Only keep the top-level module
    imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
    return list(set(imports))


def check_imports(filename):
    """
    Check if the current Python environment contains all the libraries that are imported in a file.
    """
    imports = get_imports(filename)
    missing_packages = []
    for imp in imports:
        try:
            importlib.import_module(imp)
        except ImportError:
            missing_packages.append(imp)

    if len(missing_packages) > 0:
        raise ImportError(
            "This modeling file requires the following packages that were not found in your environment: "
            f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
        )

    return get_relative_imports(filename)


def get_class_in_module(class_name, module_path):
    """
    Import a module on the cache directory for modules and extract a class from it.
    """
    module_path = module_path.replace(os.path.sep, ".")
    module = importlib.import_module(module_path)
    return getattr(module, class_name)


def get_cached_module_file(
    pretrained_model_name_or_path: Union[str, os.PathLike],
    module_file: str,
    cache_dir: Optional[Union[str, os.PathLike]] = None,
    force_download: bool = False,
    resume_download: bool = False,
    proxies: Optional[Dict[str, str]] = None,
    use_auth_token: Optional[Union[bool, str]] = None,
    revision: Optional[str] = None,
    local_files_only: bool = False,
    repo_type: Optional[str] = None,
    _commit_hash: Optional[str] = None,
):
    """
    Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
    Transformers module.

    Args:
        pretrained_model_name_or_path (`str` or `os.PathLike`):
            This can be either:

            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
              under a user or organization name, like `dbmdz/bert-base-german-cased`.
            - a path to a *directory* containing a configuration file saved using the
              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.

        module_file (`str`):
            The name of the module file containing the class to look for.
        cache_dir (`str` or `os.PathLike`, *optional*):
            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
            cache should not be used.
        force_download (`bool`, *optional*, defaults to `False`):
            Whether or not to force to (re-)download the configuration files and override the cached versions if they
            exist.
        resume_download (`bool`, *optional*, defaults to `False`):
            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
        proxies (`Dict[str, str]`, *optional*):
            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
        use_auth_token (`str` or *bool*, *optional*):
            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
            when running `huggingface-cli login` (stored in `~/.huggingface`).
        revision (`str`, *optional*, defaults to `"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.
        local_files_only (`bool`, *optional*, defaults to `False`):
            If `True`, will only try to load the tokenizer configuration from local files.
        repo_type (`str`, *optional*):
            Specify the repo type (useful when downloading from a space for instance).

    <Tip>

    Passing `use_auth_token=True` is required when you want to use a private model.

    </Tip>

    Returns:
        `str`: The path to the module inside the cache.
    """
    if is_offline_mode() and not local_files_only:
        logger.info("Offline mode: forcing local_files_only=True")
        local_files_only = True

    # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
    is_local = os.path.isdir(pretrained_model_name_or_path)
    if is_local:
        submodule = os.path.basename(pretrained_model_name_or_path)
    else:
        submodule = pretrained_model_name_or_path.replace("/", os.path.sep)
        cached_module = try_to_load_from_cache(
            pretrained_model_name_or_path, module_file, cache_dir=cache_dir, revision=_commit_hash, repo_type=repo_type
        )

    new_files = []
    try:
        # Load from URL or cache if already cached
        resolved_module_file = cached_file(
            pretrained_model_name_or_path,
            module_file,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            resume_download=resume_download,
            local_files_only=local_files_only,
            use_auth_token=use_auth_token,
            revision=revision,
            repo_type=repo_type,
            _commit_hash=_commit_hash,
        )
        if not is_local and cached_module != resolved_module_file:
            new_files.append(module_file)

    except EnvironmentError:
        logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
        raise

    # Check we have all the requirements in our environment
    modules_needed = check_imports(resolved_module_file)

    # Now we move the module inside our cached dynamic modules.
    full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
    create_dynamic_module(full_submodule)
    submodule_path = Path(HF_MODULES_CACHE) / full_submodule
    if submodule == os.path.basename(pretrained_model_name_or_path):
        # We copy local files to avoid putting too many folders in sys.path. This copy is done when the file is new or
        # has changed since last copy.
        if not (submodule_path / module_file).exists() or not filecmp.cmp(
            resolved_module_file, str(submodule_path / module_file)
        ):
            shutil.copy(resolved_module_file, submodule_path / module_file)
            importlib.invalidate_caches()
        for module_needed in modules_needed:
            module_needed = f"{module_needed}.py"
            module_needed_file = os.path.join(pretrained_model_name_or_path, module_needed)
            if not (submodule_path / module_needed).exists() or not filecmp.cmp(
                module_needed_file, str(submodule_path / module_needed)
            ):
                shutil.copy(module_needed_file, submodule_path / module_needed)
                importlib.invalidate_caches()
    else:
        # Get the commit hash
        commit_hash = extract_commit_hash(resolved_module_file, _commit_hash)

        # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
        # benefit of versioning.
        submodule_path = submodule_path / commit_hash
        full_submodule = full_submodule + os.path.sep + commit_hash
        create_dynamic_module(full_submodule)

        if not (submodule_path / module_file).exists():
            shutil.copy(resolved_module_file, submodule_path / module_file)
            importlib.invalidate_caches()
        # Make sure we also have every file with relative
        for module_needed in modules_needed:
            if not (submodule_path / f"{module_needed}.py").exists():
                get_cached_module_file(
                    pretrained_model_name_or_path,
                    f"{module_needed}.py",
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    use_auth_token=use_auth_token,
                    revision=revision,
                    local_files_only=local_files_only,
                    _commit_hash=commit_hash,
                )
                new_files.append(f"{module_needed}.py")

    if len(new_files) > 0 and revision is None:
        new_files = "\n".join([f"- {f}" for f in new_files])
        repo_type_str = "" if repo_type is None else f"{repo_type}s/"
        url = f"https://huggingface.co/{repo_type_str}{pretrained_model_name_or_path}"
        logger.warning(
            f"A new version of the following files was downloaded from {url}:\n{new_files}"
            "\n. Make sure to double-check they do not contain any added malicious code. To avoid downloading new "
            "versions of the code file, you can pin a revision."
        )

    return os.path.join(full_submodule, module_file)


def get_class_from_dynamic_module(
    class_reference: str,
    pretrained_model_name_or_path: Union[str, os.PathLike],
    cache_dir: Optional[Union[str, os.PathLike]] = None,
    force_download: bool = False,
    resume_download: bool = False,
    proxies: Optional[Dict[str, str]] = None,
    use_auth_token: Optional[Union[bool, str]] = None,
    revision: Optional[str] = None,
    local_files_only: bool = False,
    repo_type: Optional[str] = None,
    code_revision: Optional[str] = None,
    **kwargs,
):
    """
    Extracts a class from a module file, present in the local folder or repository of a model.

    <Tip warning={true}>

    Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
    therefore only be called on trusted repos.

    </Tip>

    Args:
        class_reference (`str`):
            The full name of the class to load, including its module and optionally its repo.
        pretrained_model_name_or_path (`str` or `os.PathLike`):
            This can be either:

            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
              under a user or organization name, like `dbmdz/bert-base-german-cased`.
            - a path to a *directory* containing a configuration file saved using the
              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.

            This is used when `class_reference` does not specify another repo.
        module_file (`str`):
            The name of the module file containing the class to look for.
        class_name (`str`):
            The name of the class to import in the module.
        cache_dir (`str` or `os.PathLike`, *optional*):
            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
            cache should not be used.
        force_download (`bool`, *optional*, defaults to `False`):
            Whether or not to force to (re-)download the configuration files and override the cached versions if they
            exist.
        resume_download (`bool`, *optional*, defaults to `False`):
            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
        proxies (`Dict[str, str]`, *optional*):
            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
        use_auth_token (`str` or `bool`, *optional*):
            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
            when running `huggingface-cli login` (stored in `~/.huggingface`).
        revision (`str`, *optional*, defaults to `"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.
        local_files_only (`bool`, *optional*, defaults to `False`):
            If `True`, will only try to load the tokenizer configuration from local files.
        repo_type (`str`, *optional*):
            Specify the repo type (useful when downloading from a space for instance).
        code_revision (`str`, *optional*, defaults to `"main"`):
            The specific revision to use for the code on the Hub, if the code leaves in a different repository than the
            rest of the model. 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.

    <Tip>

    Passing `use_auth_token=True` is required when you want to use a private model.

    </Tip>

    Returns:
        `type`: The class, dynamically imported from the module.

    Examples:

    ```python
    # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
    # module.
    cls = get_class_from_dynamic_module("modeling.MyBertModel", "sgugger/my-bert-model")

    # Download module `modeling.py` from a given repo and cache then extract the class `MyBertModel` from this
    # module.
    cls = get_class_from_dynamic_module("sgugger/my-bert-model--modeling.MyBertModel", "sgugger/another-bert-model")
    ```"""
    # Catch the name of the repo if it's specified in `class_reference`
    if "--" in class_reference:
        repo_id, class_reference = class_reference.split("--")
    else:
        repo_id = pretrained_model_name_or_path
    module_file, class_name = class_reference.split(".")

    if code_revision is None and pretrained_model_name_or_path == repo_id:
        code_revision = revision
    # And lastly we get the class inside our newly created module
    final_module = get_cached_module_file(
        repo_id,
        module_file + ".py",
        cache_dir=cache_dir,
        force_download=force_download,
        resume_download=resume_download,
        proxies=proxies,
        use_auth_token=use_auth_token,
        revision=code_revision,
        local_files_only=local_files_only,
        repo_type=repo_type,
    )
    return get_class_in_module(class_name, final_module.replace(".py", ""))


def custom_object_save(obj, folder, config=None):
    """
    Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally
    adds the proper fields in a config.

    Args:
        obj (`Any`): The object for which to save the module files.
        folder (`str` or `os.PathLike`): The folder where to save.
        config (`PretrainedConfig` or dictionary, `optional`):
            A config in which to register the auto_map corresponding to this custom object.
    """
    if obj.__module__ == "__main__":
        logger.warning(
            f"We can't save the code defining {obj} in {folder} as it's been defined in __main__. You should put "
            "this code in a separate module so we can include it in the saved folder and make it easier to share via "
            "the Hub."
        )
        return

    def _set_auto_map_in_config(_config):
        module_name = obj.__class__.__module__
        last_module = module_name.split(".")[-1]
        full_name = f"{last_module}.{obj.__class__.__name__}"
        # Special handling for tokenizers
        if "Tokenizer" in full_name:
            slow_tokenizer_class = None
            fast_tokenizer_class = None
            if obj.__class__.__name__.endswith("Fast"):
                # Fast tokenizer: we have the fast tokenizer class and we may have the slow one has an attribute.
                fast_tokenizer_class = f"{last_module}.{obj.__class__.__name__}"
                if getattr(obj, "slow_tokenizer_class", None) is not None:
                    slow_tokenizer = getattr(obj, "slow_tokenizer_class")
                    slow_tok_module_name = slow_tokenizer.__module__
                    last_slow_tok_module = slow_tok_module_name.split(".")[-1]
                    slow_tokenizer_class = f"{last_slow_tok_module}.{slow_tokenizer.__name__}"
            else:
                # Slow tokenizer: no way to have the fast class
                slow_tokenizer_class = f"{last_module}.{obj.__class__.__name__}"

            full_name = (slow_tokenizer_class, fast_tokenizer_class)

        if isinstance(_config, dict):
            auto_map = _config.get("auto_map", {})
            auto_map[obj._auto_class] = full_name
            _config["auto_map"] = auto_map
        elif getattr(_config, "auto_map", None) is not None:
            _config.auto_map[obj._auto_class] = full_name
        else:
            _config.auto_map = {obj._auto_class: full_name}

    # Add object class to the config auto_map
    if isinstance(config, (list, tuple)):
        for cfg in config:
            _set_auto_map_in_config(cfg)
    elif config is not None:
        _set_auto_map_in_config(config)

    result = []
    # Copy module file to the output folder.
    object_file = sys.modules[obj.__module__].__file__
    dest_file = Path(folder) / (Path(object_file).name)
    shutil.copy(object_file, dest_file)
    result.append(dest_file)

    # Gather all relative imports recursively and make sure they are copied as well.
    for needed_file in get_relative_import_files(object_file):
        dest_file = Path(folder) / (Path(needed_file).name)
        shutil.copy(needed_file, dest_file)
        result.append(dest_file)

    return result


def _raise_timeout_error(signum, frame):
    raise ValueError(
        "Loading this model requires you to execute the configuration file in that repo on your local machine. We "
        "asked if it was okay but did not get an answer. Make sure you have read the code there to avoid malicious "
        "use, then set the option `trust_remote_code=True` to remove this error."
    )


TIME_OUT_REMOTE_CODE = 15


def resolve_trust_remote_code(trust_remote_code, model_name, has_local_code, has_remote_code):
    if trust_remote_code is None:
        if has_local_code:
            trust_remote_code = False
        elif has_remote_code and TIME_OUT_REMOTE_CODE > 0:
            try:
                signal.signal(signal.SIGALRM, _raise_timeout_error)
                signal.alarm(TIME_OUT_REMOTE_CODE)
                while trust_remote_code is None:
                    answer = input(
                        f"Loading {model_name} requires to execute some code in that repo, you can inspect the content of "
                        f"the repository at https://hf.co/{model_name}. You can dismiss this prompt by passing "
                        "`trust_remote_code=True`.\nDo you accept? [y/N] "
                    )
                    if answer.lower() in ["yes", "y", "1"]:
                        trust_remote_code = True
                    elif answer.lower() in ["no", "n", "0", ""]:
                        trust_remote_code = False
                signal.alarm(0)
            except AttributeError:
                # OS which does not support signal.SIGALRM
                raise ValueError(
                    "Loading this model requires you to execute execute some code in that repo on your local machine. "
                    f"Make sure you have read the code at https://hf.co/{model_name} to avoid malicious use, then set "
                    "the option `trust_remote_code=True` to remove this error."
                )
        elif has_remote_code:
            # For the CI which puts the timeout at 0
            _raise_timeout_error(None, None)

    if has_remote_code and not has_local_code and not trust_remote_code:
        raise ValueError(
            f"Loading {model_name} requires you to execute the configuration file in that"
            " repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
            " set the option `trust_remote_code=True` to remove this error."
        )

    return trust_remote_code
