# coding=utf-8
# Copyright 2018 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.
""" Auto Tokenizer class. """
import importlib
import json
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union

from ...configuration_utils import PretrainedConfig
from ...file_utils import (
    cached_path,
    hf_bucket_url,
    is_offline_mode,
    is_sentencepiece_available,
    is_tokenizers_available,
)
from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
from ..encoder_decoder import EncoderDecoderConfig
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    config_class_to_model_type,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from fastNLP.core.log import logger

if TYPE_CHECKING:
    # This significantly improves completion suggestion performance when
    # the transformers package is used with Microsoft's Pylance language server.
    TOKENIZER_MAPPING_NAMES: OrderedDict[str, Tuple[Optional[str], Optional[str]]] = OrderedDict()
else:
    TOKENIZER_MAPPING_NAMES = OrderedDict(
        [
            ("bart", ("BartTokenizer", None)),
            ("roberta", ("RobertaTokenizer", None)),
            ("bert", ("BertTokenizer", None)),
            ("gpt2", ("GPT2Tokenizer", None)),
        ]
    )

TOKENIZER_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TOKENIZER_MAPPING_NAMES)

CONFIG_TO_TYPE = {v: k for k, v in CONFIG_MAPPING_NAMES.items()}


def tokenizer_class_from_name(class_name: str):
    if class_name == "PreTrainedTokenizerFast":
        raise RuntimeError("fastNLP does not support TokenizerFast now.")

    for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items():
        if class_name in tokenizers:
            module_name = model_type_to_module_name(module_name)

            try:
                module = importlib.import_module(f".{module_name}", "fastNLP.transformers.torch.models")
            except ImportError:
                raise ImportError(f"fastNLP transformers does not support {module_name} now, please install and import `transformers` to use it.")
            return getattr(module, class_name)

    return None


def get_tokenizer_config(
    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,
    **kwargs,
):
    """
    Loads the tokenizer configuration from a pretrained model tokenizer configuration.

    Args:
        pretrained_model_name_or_path (:obj:`str` or :obj:`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
              :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g., ``./my_model_directory/``.

        cache_dir (:obj:`str` or :obj:`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 (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to force to (re-)download the configuration files and override the cached versions if they
            exist.
        resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
        proxies (:obj:`Dict[str, str]`, `optional`):
            A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
        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`).
        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.
        local_files_only (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If :obj:`True`, will only try to load the tokenizer configuration from local files.

    .. note::

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


    Returns:
        :obj:`Dict`: The configuration of the tokenizer.

    Examples::

        # Download configuration from huggingface.co and cache.
        tokenizer_config = get_tokenizer_config("bert-base-uncased")
        # This model does not have a tokenizer config so the result will be an empty dict.
        tokenizer_config = get_tokenizer_config("xlm-roberta-base")

        # Save a pretrained tokenizer locally and you can reload its config
        from transformers import AutoTokenizer

        tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
        tokenizer.save_pretrained("tokenizer-test")
        tokenizer_config = get_tokenizer_config("tokenizer-test")
    """
    if is_offline_mode() and not local_files_only:
        logger.info("Offline mode: forcing local_files_only=True")
        local_files_only = True

    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
    if os.path.isdir(pretrained_model_name_or_path):
        config_file = os.path.join(pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE)
    else:
        config_file = hf_bucket_url(
            pretrained_model_name_or_path, filename=TOKENIZER_CONFIG_FILE, revision=revision, mirror=None
        )

    try:
        # Load from URL or cache if already cached
        resolved_config_file = cached_path(
            config_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,
        )

    except EnvironmentError:
        logger.info("Could not locate the tokenizer configuration file, will try to use the model config instead.")
        return {}

    with open(resolved_config_file, encoding="utf-8") as reader:
        return json.load(reader)


class AutoTokenizer:
    r"""
    This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
    created with the :meth:`AutoTokenizer.from_pretrained` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoTokenizer is designed to be instantiated "
            "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
        )

    @classmethod
    @replace_list_option_in_docstrings(TOKENIZER_MAPPING_NAMES)
    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
        r"""
        Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.

        The tokenizer class to instantiate is selected based on the :obj:`model_type` property of the config object
        (either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's
        missing, by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:

        List options

        Params:
            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                Can be either:

                    - A string, the `model id` of a predefined tokenizer 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 vocabulary files required by the tokenizer, for instance saved
                      using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.,
                      ``./my_model_directory/``.
                    - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
                      single vocabulary file (like Bert or XLNet), e.g.: ``./my_model_directory/vocab.txt``. (Not
                      applicable to all derived classes)
            inputs (additional positional arguments, `optional`):
                Will be passed along to the Tokenizer ``__init__()`` method.
            config (:class:`~transformers.PretrainedConfig`, `optional`)
                The configuration object used to dertermine the tokenizer class to instantiate.
            cache_dir (:obj:`str` or :obj:`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 (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force the (re-)download the model weights and configuration files and override the
                cached versions if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (:obj:`Dict[str, str]`, `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            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.
            subfolder (:obj:`str`, `optional`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
                facebook/rag-token-base), specify it here.
            use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to try to load the fast version of the tokenizer.
            tokenizer_type (:obj:`str`, `optional`):
                Tokenizer type to be loaded.
            kwargs (additional keyword arguments, `optional`):
                Will be passed to the Tokenizer ``__init__()`` method. Can be used to set special tokens like
                ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``,
                ``mask_token``, ``additional_special_tokens``. See parameters in the ``__init__()`` for more details.

        Examples::

            >>> from transformers import AutoTokenizer

            >>> # Download vocabulary from huggingface.co and cache.
            >>> tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

            >>> # Download vocabulary from huggingface.co (user-uploaded) and cache.
            >>> tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased')

            >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
            >>> tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/')

        """
        config = kwargs.pop("config", None)
        kwargs["_from_auto"] = True

        use_fast = kwargs.pop("use_fast", True)
        tokenizer_type = kwargs.pop("tokenizer_type", None)

        # First, let's see whether the tokenizer_type is passed so that we can leverage it
        if tokenizer_type is not None:
            tokenizer_class = None
            tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None)

            if tokenizer_class_tuple is None:
                raise ValueError(
                    f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of "
                    f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}."
                )

            tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple

            if use_fast and tokenizer_fast_class_name is not None:
                tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)

            if tokenizer_class is None:
                tokenizer_class = tokenizer_class_from_name(tokenizer_class_name)

            if tokenizer_class is None:
                raise ValueError(f"Tokenizer class {tokenizer_class_name} is not currently imported.")

            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)

        # Next, let's try to use the tokenizer_config file to get the tokenizer class.
        tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
        config_tokenizer_class = tokenizer_config.get("tokenizer_class")

        # If that did not work, let's try to use the config.
        if config_tokenizer_class is None:
            if not isinstance(config, PretrainedConfig):
                config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
            config_tokenizer_class = config.tokenizer_class

        # If we have the tokenizer class from the tokenizer config or the model config we're good!
        if config_tokenizer_class is not None:
            tokenizer_class = None
            if use_fast and not config_tokenizer_class.endswith("Fast"):
                tokenizer_class_candidate = f"{config_tokenizer_class}Fast"
                tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
            if tokenizer_class is None:
                tokenizer_class_candidate = config_tokenizer_class
                tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)

            if tokenizer_class is None:
                raise ValueError(
                    f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
                )
            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)

        # Otherwise we have to be creative.
        # if model is an encoder decoder, the encoder tokenizer class is used by default
        if isinstance(config, EncoderDecoderConfig):
            if type(config.decoder) is not type(config.encoder):  # noqa: E721
                logger.warning(
                    f"The encoder model config class: {config.encoder.__class__} is different from the decoder model "
                    f"config class: {config.decoder.__class__}. It is not recommended to use the "
                    "`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder "
                    "specific tokenizer classes."
                )
            config = config.encoder

        model_type = config_class_to_model_type(type(config).__name__)
        if model_type is not None:
            tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
            if tokenizer_class_fast and (use_fast or tokenizer_class_py is None):
                return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
            else:
                if tokenizer_class_py is not None:
                    return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
                else:
                    raise ValueError(
                        "This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed "
                        "in order to use this tokenizer."
                    )

        raise ValueError(
            f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n"
            f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}."
        )
