# 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 Config class. """
import importlib
import re
import warnings
from collections import OrderedDict
from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...file_utils import CONFIG_NAME


CONFIG_MAPPING_NAMES = OrderedDict(
    [
        # Add configs here
        ("vision-encoder-decoder", "VisionEncoderDecoderConfig"),
        ("trocr", "TrOCRConfig"),
        ("fnet", "FNetConfig"),
        ("gptj", "GPTJConfig"),
        ("layoutlmv2", "LayoutLMv2Config"),
        ("beit", "BeitConfig"),
        ("rembert", "RemBertConfig"),
        ("visual_bert", "VisualBertConfig"),
        ("canine", "CanineConfig"),
        ("roformer", "RoFormerConfig"),
        ("clip", "CLIPConfig"),
        ("bigbird_pegasus", "BigBirdPegasusConfig"),
        ("deit", "DeiTConfig"),
        ("luke", "LukeConfig"),
        ("detr", "DetrConfig"),
        ("gpt_neo", "GPTNeoConfig"),
        ("big_bird", "BigBirdConfig"),
        ("speech_to_text_2", "Speech2Text2Config"),
        ("speech_to_text", "Speech2TextConfig"),
        ("vit", "ViTConfig"),
        ("wav2vec2", "Wav2Vec2Config"),
        ("m2m_100", "M2M100Config"),
        ("convbert", "ConvBertConfig"),
        ("led", "LEDConfig"),
        ("blenderbot-small", "BlenderbotSmallConfig"),
        ("retribert", "RetriBertConfig"),
        ("ibert", "IBertConfig"),
        ("mt5", "MT5Config"),
        ("t5", "T5Config"),
        ("mobilebert", "MobileBertConfig"),
        ("distilbert", "DistilBertConfig"),
        ("albert", "AlbertConfig"),
        ("bert-generation", "BertGenerationConfig"),
        ("camembert", "CamembertConfig"),
        ("xlm-roberta", "XLMRobertaConfig"),
        ("pegasus", "PegasusConfig"),
        ("marian", "MarianConfig"),
        ("mbart", "MBartConfig"),
        ("megatron-bert", "MegatronBertConfig"),
        ("mpnet", "MPNetConfig"),
        ("bart", "BartConfig"),
        ("blenderbot", "BlenderbotConfig"),
        ("reformer", "ReformerConfig"),
        ("longformer", "LongformerConfig"),
        ("roberta", "RobertaConfig"),
        ("deberta-v2", "DebertaV2Config"),
        ("deberta", "DebertaConfig"),
        ("flaubert", "FlaubertConfig"),
        ("fsmt", "FSMTConfig"),
        ("squeezebert", "SqueezeBertConfig"),
        ("hubert", "HubertConfig"),
        ("bert", "BertConfig"),
        ("openai-gpt", "OpenAIGPTConfig"),
        ("gpt2", "GPT2Config"),
        ("transfo-xl", "TransfoXLConfig"),
        ("xlnet", "XLNetConfig"),
        ("xlm-prophetnet", "XLMProphetNetConfig"),
        ("prophetnet", "ProphetNetConfig"),
        ("xlm", "XLMConfig"),
        ("ctrl", "CTRLConfig"),
        ("electra", "ElectraConfig"),
        ("speech-encoder-decoder", "SpeechEncoderDecoderConfig"),
        ("encoder-decoder", "EncoderDecoderConfig"),
        ("funnel", "FunnelConfig"),
        ("lxmert", "LxmertConfig"),
        ("dpr", "DPRConfig"),
        ("layoutlm", "LayoutLMConfig"),
        ("rag", "RagConfig"),
        ("tapas", "TapasConfig"),
        ("splinter", "SplinterConfig"),
        ("sew-d", "SEWDConfig"),
        ("sew", "SEWConfig"),
    ]
)

CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
    [
        # Add archive maps here
        ("fnet", "FNET_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("pegasus", "PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("gptj", "GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("layoutlmv2", "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("beit", "BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("rembert", "REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("visual_bert", "VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("canine", "CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("clip", "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("bigbird_pegasus", "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("deit", "DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("luke", "LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("detr", "DETR_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("gpt_neo", "GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("big_bird", "BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("megatron-bert", "MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("speech_to_text", "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("speech_to_text_2", "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("vit", "VIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("wav2vec2", "WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("m2m_100", "M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("convbert", "CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("led", "LED_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("blenderbot-small", "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("bert", "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("bart", "BART_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("blenderbot", "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("mbart", "MBART_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("openai-gpt", "OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("transfo-xl", "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("gpt2", "GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("ctrl", "CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("xlnet", "XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("xlm", "XLM_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("roberta", "ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("distilbert", "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("albert", "ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("camembert", "CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("t5", "T5_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("xlm-roberta", "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("flaubert", "FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("fsmt", "FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("electra", "ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("retribert", "RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("funnel", "FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("lxmert", "LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("layoutlm", "LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("dpr", "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("deberta", "DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("deberta-v2", "DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("squeezebert", "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("xlm-prophetnet", "XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("prophetnet", "PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("mpnet", "MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("tapas", "TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("ibert", "IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("hubert", "HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("splinter", "SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("sew-d", "SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("sew", "SEW_PRETRAINED_CONFIG_ARCHIVE_MAP"),
    ]
)

MODEL_NAMES_MAPPING = OrderedDict(
    [
        # Add full (and cased) model names here
        ("vision-encoder-decoder", "Vision Encoder decoder"),
        ("trocr", "TrOCR"),
        ("fnet", "FNet"),
        ("gptj", "GPT-J"),
        ("beit", "BeiT"),
        ("rembert", "RemBERT"),
        ("layoutlmv2", "LayoutLMv2"),
        ("visual_bert", "VisualBert"),
        ("canine", "Canine"),
        ("roformer", "RoFormer"),
        ("clip", "CLIP"),
        ("bigbird_pegasus", "BigBirdPegasus"),
        ("deit", "DeiT"),
        ("luke", "LUKE"),
        ("detr", "DETR"),
        ("gpt_neo", "GPT Neo"),
        ("big_bird", "BigBird"),
        ("speech_to_text_2", "Speech2Text2"),
        ("speech_to_text", "Speech2Text"),
        ("vit", "ViT"),
        ("wav2vec2", "Wav2Vec2"),
        ("m2m_100", "M2M100"),
        ("convbert", "ConvBERT"),
        ("led", "LED"),
        ("blenderbot-small", "BlenderbotSmall"),
        ("retribert", "RetriBERT"),
        ("ibert", "I-BERT"),
        ("t5", "T5"),
        ("mobilebert", "MobileBERT"),
        ("distilbert", "DistilBERT"),
        ("albert", "ALBERT"),
        ("bert-generation", "Bert Generation"),
        ("camembert", "CamemBERT"),
        ("xlm-roberta", "XLM-RoBERTa"),
        ("pegasus", "Pegasus"),
        ("blenderbot", "Blenderbot"),
        ("marian", "Marian"),
        ("mbart", "mBART"),
        ("megatron-bert", "MegatronBert"),
        ("bart", "BART"),
        ("reformer", "Reformer"),
        ("longformer", "Longformer"),
        ("roberta", "RoBERTa"),
        ("flaubert", "FlauBERT"),
        ("fsmt", "FairSeq Machine-Translation"),
        ("squeezebert", "SqueezeBERT"),
        ("bert", "BERT"),
        ("openai-gpt", "OpenAI GPT"),
        ("gpt2", "OpenAI GPT-2"),
        ("transfo-xl", "Transformer-XL"),
        ("xlnet", "XLNet"),
        ("xlm", "XLM"),
        ("ctrl", "CTRL"),
        ("electra", "ELECTRA"),
        ("encoder-decoder", "Encoder decoder"),
        ("speech-encoder-decoder", "Speech Encoder decoder"),
        ("funnel", "Funnel Transformer"),
        ("lxmert", "LXMERT"),
        ("deberta-v2", "DeBERTa-v2"),
        ("deberta", "DeBERTa"),
        ("layoutlm", "LayoutLM"),
        ("dpr", "DPR"),
        ("rag", "RAG"),
        ("xlm-prophetnet", "XLMProphetNet"),
        ("prophetnet", "ProphetNet"),
        ("mt5", "mT5"),
        ("mpnet", "MPNet"),
        ("tapas", "TAPAS"),
        ("hubert", "Hubert"),
        ("barthez", "BARThez"),
        ("phobert", "PhoBERT"),
        ("cpm", "CPM"),
        ("bertweet", "Bertweet"),
        ("bert-japanese", "BertJapanese"),
        ("byt5", "ByT5"),
        ("mbart50", "mBART-50"),
        ("splinter", "Splinter"),
        ("sew-d", "SEW-D"),
        ("sew", "SEW"),
    ]
)

SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])


def model_type_to_module_name(key):
    """Converts a config key to the corresponding module."""
    # Special treatment
    if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
        return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]

    return key.replace("-", "_")


def config_class_to_model_type(config):
    """Converts a config class name to the corresponding model type"""
    for key, cls in CONFIG_MAPPING_NAMES.items():
        if cls == config:
            return key
    return None


class _LazyConfigMapping(OrderedDict):
    """
    A dictionary that lazily load its values when they are requested.
    """

    def __init__(self, mapping):
        self._mapping = mapping
        self._modules = {}

    def __getitem__(self, key):
        if key not in self._mapping:
            raise KeyError(key)
        value = self._mapping[key]
        module_name = model_type_to_module_name(key)
        if module_name not in self._modules:
            self._modules[module_name] = importlib.import_module(f".{module_name}", "transformers.models")
        return getattr(self._modules[module_name], value)

    def keys(self):
        return self._mapping.keys()

    def values(self):
        return [self[k] for k in self._mapping.keys()]

    def items(self):
        return [(k, self[k]) for k in self._mapping.keys()]

    def __iter__(self):
        return iter(self._mapping.keys())

    def __contains__(self, item):
        return item in self._mapping


CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)


class _LazyLoadAllMappings(OrderedDict):
    """
    A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
    etc.)

    Args:
        mapping: The mapping to load.
    """

    def __init__(self, mapping):
        self._mapping = mapping
        self._initialized = False
        self._data = {}

    def _initialize(self):
        if self._initialized:
            return
        warnings.warn(
            "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
            "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
            FutureWarning,
        )

        for model_type, map_name in self._mapping.items():
            module_name = model_type_to_module_name(model_type)
            module = importlib.import_module(f".{module_name}", "transformers.models")
            mapping = getattr(module, map_name)
            self._data.update(mapping)

        self._initialized = True

    def __getitem__(self, key):
        self._initialize()
        return self._data[key]

    def keys(self):
        self._initialize()
        return self._data.keys()

    def values(self):
        self._initialize()
        return self._data.values()

    def items(self):
        self._initialize()
        return self._data.keys()

    def __iter__(self):
        self._initialize()
        return iter(self._data)

    def __contains__(self, item):
        self._initialize()
        return item in self._data


ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)


def _get_class_name(model_class: Union[str, List[str]]):
    if isinstance(model_class, (list, tuple)):
        return " or ".join([f":class:`~transformers.{c}`" for c in model_class if c is not None])
    return f":class:`~transformers.{model_class}`"


def _list_model_options(indent, config_to_class=None, use_model_types=True):
    if config_to_class is None and not use_model_types:
        raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
    if use_model_types:
        if config_to_class is None:
            model_type_to_name = {
                model_type: f":class:`~transformers.{config}`" for model_type, config in CONFIG_MAPPING_NAMES.items()
            }
        else:
            model_type_to_name = {
                model_type: _get_class_name(model_class)
                for model_type, model_class in config_to_class.items()
                if model_type in MODEL_NAMES_MAPPING
            }
        lines = [
            f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
            for model_type in sorted(model_type_to_name.keys())
        ]
    else:
        config_to_name = {
            CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
            for config, clas in config_to_class.items()
            if config in CONFIG_MAPPING_NAMES
        }
        config_to_model_name = {
            config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
        }
        lines = [
            f"{indent}- :class:`~transformers.{config_name}` configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
            for config_name in sorted(config_to_name.keys())
        ]
    return "\n".join(lines)


def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
    def docstring_decorator(fn):
        docstrings = fn.__doc__
        lines = docstrings.split("\n")
        i = 0
        while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
            i += 1
        if i < len(lines):
            indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
            if use_model_types:
                indent = f"{indent}    "
            lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
            docstrings = "\n".join(lines)
        else:
            raise ValueError(
                f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
            )
        fn.__doc__ = docstrings
        return fn

    return docstring_decorator


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

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

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

    @classmethod
    def for_model(cls, model_type: str, *args, **kwargs):
        if model_type in CONFIG_MAPPING:
            config_class = CONFIG_MAPPING[model_type]
            return config_class(*args, **kwargs)
        raise ValueError(
            f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
        )

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

        The configuration class to instantiate is selected based on the :obj:`model_type` property of the config object
        that is loaded, or when it's missing, by falling back to using pattern matching on
        :obj:`pretrained_model_name_or_path`:

        List options

        Args:
            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                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
                      :meth:`~transformers.PretrainedConfig.save_pretrained` method, or the
                      :meth:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
                    - A path or url to a saved configuration JSON `file`, e.g.,
                      ``./my_model_directory/configuration.json``.
            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.
            return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
                If :obj:`False`, then this function returns just the final configuration object.

                If :obj:`True`, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs`
                is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e.,
                the part of ``kwargs`` which has not been used to update ``config`` and is otherwise ignored.
            kwargs(additional keyword arguments, `optional`):
                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                by the ``return_unused_kwargs`` keyword parameter.

        Examples::

            >>> from transformers import AutoConfig

            >>> # Download configuration from huggingface.co and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')

            >>> # Download configuration from huggingface.co (user-uploaded) and cache.
            >>> config = AutoConfig.from_pretrained('dbmdz/bert-base-german-cased')

            >>> # If configuration file is in a directory (e.g., was saved using `save_pretrained('./test/saved_model/')`).
            >>> config = AutoConfig.from_pretrained('./test/bert_saved_model/')

            >>> # Load a specific configuration file.
            >>> config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')

            >>> # Change some config attributes when loading a pretrained config.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
            >>> config.output_attentions
            True
            >>> config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
            >>> config.output_attentions
            True
            >>> config.unused_kwargs
            {'foo': False}
        """
        kwargs["_from_auto"] = True
        config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
        if "model_type" in config_dict:
            config_class = CONFIG_MAPPING[config_dict["model_type"]]
            return config_class.from_dict(config_dict, **kwargs)
        else:
            # Fallback: use pattern matching on the string.
            for pattern, config_class in CONFIG_MAPPING.items():
                if pattern in str(pretrained_model_name_or_path):
                    return config_class.from_dict(config_dict, **kwargs)

        raise ValueError(
            f"Unrecognized model in {pretrained_model_name_or_path}. "
            f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
            f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
        )
