# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""Configuration base class and utilities."""

import copy
import json
import math
import os
from collections.abc import Sequence
from dataclasses import MISSING, dataclass, fields
from functools import wraps
from typing import TYPE_CHECKING, Any, ClassVar, Literal, TypeVar, Union

from huggingface_hub.dataclasses import strict
from packaging import version
from typing_extensions import dataclass_transform

from . import __version__
from .dynamic_module_utils import custom_object_save
from .generation.configuration_utils import GenerationConfig
from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
from .modeling_rope_utils import RotaryEmbeddingConfigMixin
from .utils import (
    CONFIG_NAME,
    PushToHubMixin,
    cached_file,
    copy_func,
    extract_commit_hash,
    hf_api,
    is_torch_available,
    logging,
)
from .utils.generic import is_timm_config_dict


if TYPE_CHECKING:
    import torch


logger = logging.get_logger(__name__)


# type hinting: specifying the type of config class that inherits from PreTrainedConfig
SpecificPreTrainedConfigType = TypeVar("SpecificPreTrainedConfigType", bound="PreTrainedConfig")

_FLOAT_TAG_KEY = "__float__"
_FLOAT_TAG_VALUES = {"Infinity": float("inf"), "-Infinity": float("-inf"), "NaN": float("nan")}


ALLOWED_LAYER_TYPES = (
    "full_attention",
    "sliding_attention",
    "chunked_attention",
    "compressed_sparse_attention",  # CSA, used in deepseek_v4
    "heavily_compressed_attention",  # HCA, used in deepseek_v4
    "minimax_m3_sparse",  # lightning-index sparse attention, used in minimax_m3_vl
    "linear_attention",  # used in minimax
    "conv",  # used in LFMv2
    "mamba",
    "attention",
    "sparse",
    "dense",
    "hybrid",  # for layers that have both mamba and attention in zamba and zamba2
    "moe",  # for nemotron_h, which uses either attention, mamba or moe
    "deepseek_sparse_attention",  # for models with DSA indexer (GLM MoE DSA, DeepSeek V32)
)


# copied from huggingface_hub.dataclasses.strict when `accept_kwargs=True`
def wrap_init_to_accept_kwargs(cls: dataclass):
    # Get the original dataclass-generated __init__
    original_init = cls.__init__

    @wraps(original_init)
    def __init__(self, *args, **kwargs: Any) -> None:
        # Extract only the fields that are part of the dataclass
        dataclass_fields = {f.name for f in fields(cls)}
        standard_kwargs = {k: v for k, v in kwargs.items() if k in dataclass_fields}

        # We need to call bare `__init__` without `__post_init__` but the `original_init` of
        # any dataclas contains a call to post-init at the end (without kwargs)
        if len(args) > 0:
            raise ValueError(
                f"{cls.__name__} accepts only keyword arguments, but found `{len(args)}` positional args."
            )

        for f in fields(cls):  # type: ignore
            if f.name in standard_kwargs:
                setattr(self, f.name, standard_kwargs[f.name])
            elif f.default is not MISSING:
                setattr(self, f.name, f.default)
            elif f.default_factory is not MISSING:
                setattr(self, f.name, f.default_factory())
            else:
                raise TypeError(f"Missing required field - '{f.name}'")

        # Pass any additional kwargs to `__post_init__` and let the object
        # decide whether to set the attr or use for different purposes (e.g. BC checks)
        additional_kwargs = {}
        for name, value in kwargs.items():
            if name not in dataclass_fields:
                additional_kwargs[name] = value

        self.__post_init__(**additional_kwargs)

    cls.__init__ = __init__
    return cls


@dataclass_transform(kw_only_default=True)
@strict(accept_kwargs=True)
@dataclass(repr=False)
class PreTrainedConfig(PushToHubMixin, RotaryEmbeddingConfigMixin):
    # no-format
    r"""
    Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
    methods for loading/downloading/saving configurations.

    <Tip>

    A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
    initialize a model does **not** load the model weights. It only affects the model's configuration.

    </Tip>

    Class attributes (overridden by derived classes):

    - **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
      the correct object in [`~transformers.AutoConfig`].
    - **has_no_defaults_at_init** (`bool`) -- Whether the config class can be initialized without providing input arguments.
      Some configurations requires inputs to be defined at init and have no default values, usually these are composite configs,
      (but not necessarily) such as [`~transformers.EncoderDecoderConfig`] or [`~RagConfig`]. They have to be initialized from
      two or more configs of type [`~transformers.PreTrainedConfig`].
    - **keys_to_ignore_at_inference** (`list[str]`) -- A list of keys to ignore by default when looking at dictionary
      outputs of the model during inference.
    - **attribute_map** (`dict[str, str]`) -- A dict that maps model specific attribute names to the standardized
      naming of attributes.
    - **base_model_tp_plan** (`dict[str, Any]`) -- A dict that maps sub-modules FQNs of a base model to a tensor
      parallel plan applied to the sub-module when `model.tensor_parallel` is called.
    - **base_model_fsdp_plan** (`dict[Any, str]`) -- A dict that maps sub-modules of a base model to an FSDP2
      sharding strategy (e.g. `"free_full_weight"` / `"keep_full_weight"`). Keys can be wildcard module paths
      (e.g. `"layers.*"`) or tuples of paths (grouped into a single `fully_shard` call).
    - **base_model_pp_plan** (`dict[str, tuple[list[str]]]`) -- A dict that maps child-modules of a base model to a
      pipeline parallel plan that enables users to place the child-module on the appropriate device.

    Common attributes (present in all subclasses):

    - **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
      embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
    - **hidden_size** (`int`) -- The hidden size of the model.
    - **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the
      model.
    - **num_hidden_layers** (`int`) -- The number of blocks in the model.

    <Tip warning={true}>

    Setting parameters for sequence generation in the model config is deprecated. For backward compatibility, loading
    some of them will still be possible, but attempting to overwrite them will throw an exception -- you should set
    them in a [~transformers.GenerationConfig]. Check the documentation of [~transformers.GenerationConfig] for more
    information about the individual parameters.

    </Tip>

    Arg:
        name_or_path (`str`, *optional*, defaults to `""`):
            Store the string that was passed to [`PreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path`
            if the configuration was created with such a method.
        output_hidden_states (`bool`, *optional*, defaults to `False`):
            Whether or not the model should return all hidden-states.
        output_attentions (`bool`, *optional*, defaults to `False`):
            Whether or not the model should returns all attentions.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple.
        is_encoder_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as an encoder/decoder or not.
        chunk_size_feed_forward (`int`, *optional*, defaults to `0`):
            The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
            the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` <
            sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
            Forward Chunking work?](../glossary.html#feed-forward-chunking).

        > Parameters for fine-tuning tasks

        architectures (`list[str]`, *optional*):
            Model architectures that can be used with the model pretrained weights.
        id2label (`dict[int, str]`, *optional*):
            A map from index (for instance prediction index, or target index) to label.
        label2id (`dict[str, int]`, *optional*):
            A map from label to index for the model.
        num_labels (`int`, *optional*):
            Number of labels to use in the last layer added to the model, typically for a classification task.
        problem_type (`str`, *optional*):
            Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
            `"single_label_classification"` or `"multi_label_classification"`.

        > PyTorch specific parameters

        dtype (`str`, *optional*):
            The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype`
            (which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved
            model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load
            `float16` weights.
    """

    # Class attributes that we don't want to save or have in `self.__dict__`
    # They are not supposed to be set/changed by users. Each field is set when
    # creating a model class
    base_config_key: ClassVar[str] = ""
    sub_configs: ClassVar[dict[str, type["PreTrainedConfig"]]] = {}
    has_no_defaults_at_init: ClassVar[bool] = False
    keys_to_ignore_at_inference: ClassVar[list[str]] = []
    attribute_map: ClassVar[dict[str, str]] = {}
    base_model_tp_plan: ClassVar[dict[str, Any] | None] = None
    base_model_fsdp_plan: ClassVar[dict[Any, str] | None] = None
    base_model_pp_plan: ClassVar[dict[str, Sequence[list[str]]] | None] = None
    base_model_ep_plan: ClassVar[dict[str, Sequence[list[str]]] | None] = None
    _auto_class: ClassVar[str | None] = None

    # Attributes set internally when saving and used to infer model
    # class for `Auto` mapping
    model_type: ClassVar[str] = ""
    transformers_version: str | None = None
    architectures: list[str] | None = None

    # Common attributes for all models
    output_hidden_states: bool | None = False
    return_dict: bool | None = True
    dtype: Union[str, "torch.dtype"] | None = None
    chunk_size_feed_forward: int = 0
    is_encoder_decoder: bool = False

    # Fine-tuning task arguments
    id2label: dict[int, str] | dict[str, str] | None = None
    label2id: dict[str, int] | dict[str, str] | None = None
    problem_type: Literal["regression", "single_label_classification", "multi_label_classification"] | None = None

    def __post_init__(self, **kwargs):
        # BC for the `torch_dtype` argument instead of the simpler `dtype`
        # Do not warn, as it would otherwise always be triggered since most configs on the hub have `torch_dtype`
        if (torch_dtype := kwargs.pop("torch_dtype", None)) is not None:
            # If both are provided, keep `dtype`
            self.dtype = self.dtype if self.dtype is not None else torch_dtype
        if self.dtype is not None and isinstance(self.dtype, str) and is_torch_available():
            # we will start using self.dtype in v5, but to be consistent with
            # from_pretrained's dtype arg convert it to an actual torch.dtype object
            import torch

            self.dtype = getattr(torch, self.dtype)

        # Keep the default value of `num_labels=2` in case users have saved a classifier with 2 labels
        # Our configs prev wouldn't save `id2label` for 2 labels because it is the default. In all other
        # cases we expect the config dict to have an `id2label` field if it's a clf model, or not otherwise
        if self.id2label is None:
            self.num_labels = kwargs.get("num_labels", self.num_labels if self.num_labels is not None else 2)
        else:
            if kwargs.get("num_labels") is not None and len(self.id2label) != kwargs.get("num_labels"):
                logger.warning(
                    f"You passed `num_labels={kwargs.get('num_labels')}` which is incompatible to "
                    f"the `id2label` map of length `{len(self.id2label)}`."
                )
            # Keys are always strings in JSON so convert ids to int
            self.id2label = {int(key): value for key, value in self.id2label.items()}

        if self.problem_type == "single_label_classification" and self.num_labels == 1:
            raise ValueError(
                '`problem_type="single_label_classification"` requires `num_labels > 1`. For binary '
                'classification use `num_labels=2`, or use `problem_type="regression"` for a '
                "single-output regression head."
            )

        # BC for rotary embeddings. We will pop out legacy keys from kwargs and rename to new format
        if hasattr(self, "rope_parameters"):
            kwargs = self.convert_rope_params_to_dict(**kwargs)
        elif kwargs.get("rope_scaling") and kwargs.get("rope_theta"):
            logger.warning(
                f"{self.__class__.__name__} got `key=rope_scaling` in kwargs but hasn't set it as attribute. "
                "For RoPE standardization you need to set `self.rope_parameters` in model's config. "
            )
            kwargs = self.convert_rope_params_to_dict(**kwargs)

        # Parameters for sequence generation saved in the config are popped instead of loading them.
        for parameter_name in GenerationConfig._get_default_generation_params().keys():
            kwargs.pop(parameter_name, None)

        # Name or path to the pretrained checkpoint
        self._name_or_path = str(kwargs.pop("name_or_path", ""))
        self._commit_hash = kwargs.pop("_commit_hash", None)

        # Attention/Experts implementation to use, if relevant (it sets it recursively on sub-configs)
        self._output_attentions: bool | None = kwargs.pop("output_attentions", False)
        self._attn_implementation: str | None = kwargs.pop("attn_implementation", None)
        self._experts_implementation: str | None = kwargs.pop("experts_implementation", None)

        # Additional attributes without default values
        for key, value in kwargs.items():
            # Check this to avoid deserializing problematic fields from hub configs - they should use the public field
            if key not in ("_attn_implementation_internal", "_experts_implementation_internal"):
                try:
                    setattr(self, key, value)
                except AttributeError as err:
                    logger.error(f"Can't set {key} with value {value} for {self}")
                    raise err

    def __init_subclass__(cls, *args, **kwargs):
        super().__init_subclass__(*args, **kwargs)
        cls_has_custom_init = "__init__" in cls.__dict__
        # kw_only=True ensures fields without defaults in subclasses can follow
        # parent fields that have defaults (Python dataclass ordering rule).
        # Config fields are always passed as keyword arguments, so this is safe.
        cls = dataclass(cls, repr=False, kw_only=True)

        if not cls_has_custom_init:
            # Wrap all subclasses to accept arbitrary kwargs for BC
            # only if the subclass has no custom `__init__`. Most
            # remote code has an init defined, but some model are not
            # See https://huggingface.co/hmellor/Ilama-3.2-1B/blob/main/configuration_ilama.py
            cls = wrap_init_to_accept_kwargs(cls)

    @property
    def name_or_path(self) -> str | None:
        return getattr(self, "_name_or_path", None)

    @name_or_path.setter
    def name_or_path(self, value):
        self._name_or_path = str(value)  # Make sure that name_or_path is a string (for JSON encoding)

    @property
    def num_labels(self) -> int:
        """
        `int`: The number of labels for classification models.
        """
        return len(self.id2label) if self.id2label is not None else None

    @num_labels.setter
    def num_labels(self, num_labels: int):
        # we do not store `num_labels` attribute in config, but instead
        # compute it based on the length of the `id2label` map
        if self.id2label is None or self.num_labels != num_labels:
            self.id2label = {i: f"LABEL_{i}" for i in range(num_labels)}
            self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))

    @property
    def output_attentions(self):
        """
        `bool`: Whether or not the model should returns all attentions.
        """
        return self._output_attentions

    @output_attentions.setter
    def output_attentions(self, value: bool):
        # If we set `output_attentions` explicitly before the attn implementation, dispatch eager
        if value and self._attn_implementation is None:
            self._attn_implementation = "eager"
        if value and self._attn_implementation != "eager":
            raise ValueError(
                "The `output_attentions` attribute is not supported when using the `attn_implementation` set to "
                f"{self._attn_implementation}. Please set it to 'eager' instead."
            )
        self._output_attentions = value

    @property
    def _attn_implementation(self):
        return self._attn_implementation_internal

    @_attn_implementation.setter
    def _attn_implementation(self, value: str | dict | None):
        """We set it recursively on the sub-configs as well"""
        # Set if for current config
        current_attn = getattr(self, "_attn_implementation", None)
        attn_implementation = value if not isinstance(value, dict) else value.get("", current_attn)
        self._attn_implementation_internal = attn_implementation

        # Set it recursively on the subconfigs
        for subconfig_key in self.sub_configs:
            subconfig = getattr(self, subconfig_key, None)
            if subconfig is not None:
                current_subconfig_attn = getattr(subconfig, "_attn_implementation", None)
                sub_implementation = (
                    value if not isinstance(value, dict) else value.get(subconfig_key, current_subconfig_attn)
                )
                subconfig._attn_implementation = sub_implementation

    @property
    def _experts_implementation(self):
        return self._experts_implementation_internal

    @_experts_implementation.setter
    def _experts_implementation(self, value: str | dict | None):
        """We set it recursively on the sub-configs as well"""
        # Set if for current config
        current_moe = getattr(self, "_experts_implementation", None)
        experts_implementation = value if not isinstance(value, dict) else value.get("", current_moe)
        self._experts_implementation_internal = experts_implementation

        # Set it recursively on the subconfigs
        for subconfig_key in self.sub_configs:
            subconfig = getattr(self, subconfig_key, None)
            if subconfig is not None:
                current_subconfig_moe = getattr(subconfig, "_experts_implementation", None)
                sub_implementation = (
                    value if not isinstance(value, dict) else value.get(subconfig_key, current_subconfig_moe)
                )
                subconfig._experts_implementation = sub_implementation

    @property
    def torch_dtype(self):
        logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
        return self.dtype

    @property
    def use_return_dict(self):
        logger.warning_once("`use_return_dict` is deprecated! Use `return_dict` instead!")
        return self.return_dict

    @torch_dtype.setter
    def torch_dtype(self, value):
        logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
        self.dtype = value

    def __setattr__(self, key, value):
        if key in super().__getattribute__("attribute_map"):
            key = super().__getattribute__("attribute_map")[key]
        super().__setattr__(key, value)

    def __getattribute__(self, key):
        if key != "attribute_map" and key in super().__getattribute__("attribute_map"):
            key = super().__getattribute__("attribute_map")[key]
        return super().__getattribute__(key)

    def validate_output_attentions(self):
        if self.output_attentions and self._attn_implementation not in ["eager", None]:
            raise ValueError(
                "The `output_attentions` attribute is not supported when using the `attn_implementation` set to "
                f"{self._attn_implementation}. Please set it to 'eager' instead."
            )

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if (
            hasattr(self, "head_dim")
            and hasattr(self, "num_heads")
            and hasattr(self, "embed_dim")
            and self.head_dim * self.num_heads != self.embed_dim
        ):
            raise ValueError(
                f"The embed_dim ({self.embed_dim}) is not a multiple of the number of attention "
                f"heads ({self.num_heads})."
            )

    def validate_token_ids(self):
        """Part of `@strict`-powered validation. Validates the contents of the special tokens."""
        text_config = self.get_text_config(decoder=True)
        vocab_size = getattr(text_config, "vocab_size", None)
        if vocab_size is not None:
            # Check for all special tokens, e..g. pad_token_id, image_token_id, audio_token_id
            for name in text_config:
                value = getattr(text_config, name)
                if name.endswith("_token_id") and isinstance(value, int) and not 0 <= value < vocab_size:
                    # Can't be an exception until we can load configs that fail validation: several configs on the Hub
                    # store invalid special tokens, e.g. `pad_token_id=-1`
                    logger.warning_once(
                        f"Model config: {name} must be `None` or an integer within the vocabulary (between 0 "
                        f"and {vocab_size - 1}), got {value}. This may result in unexpected behavior."
                    )

    def validate_layer_type(self):
        """Check that `layer_types` is correctly defined."""
        for layer_types in ["layer_types", "mlp_layer_types"]:
            layers = getattr(self, layer_types, None)
            if not (layers is not None and hasattr(self, "num_hidden_layers")):
                return
            elif not all(layer_type in ALLOWED_LAYER_TYPES for layer_type in layers):
                raise ValueError(f"The `{layer_types}` entries must be in {ALLOWED_LAYER_TYPES} but got {layers}")
            elif self.num_hidden_layers is not None and self.num_hidden_layers != len(layers):
                raise ValueError(
                    f"`num_hidden_layers` ({self.num_hidden_layers}) must be equal to the number of `{layer_types}` "
                    f"({len(layers)})"
                )

    @property
    def rope_scaling(self):
        return self.rope_parameters

    @rope_scaling.setter
    def rope_scaling(self, value):
        self.rope_parameters = value

    def save_pretrained(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
        """
        Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
        [`~PreTrainedConfig.from_pretrained`] class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the configuration JSON file will be saved (will be created if it does not exist).
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`dict[str, Any]`, *optional*):
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

        generation_parameters = self._get_generation_parameters()
        if len(generation_parameters) > 0:
            raise ValueError(
                "Some generation parameters are set in the model config. These should go into `model.generation_config`"
                f"as opposed to `model.config`. \nGeneration parameters found: {str(generation_parameters)}",
            )

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = hf_api().create_repo(repo_id, exist_ok=True, **kwargs).repo_id
            files_timestamps = self._get_files_timestamps(save_directory)

        # This attribute is important to know on load, but should not be serialized on save.
        if "transformers_weights" in self:
            delattr(self, "transformers_weights")

        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
            custom_object_save(self, save_directory, config=self)

        # If we save using the predefined names, we can load using `from_pretrained`
        output_config_file = os.path.join(save_directory, CONFIG_NAME)

        # Strict validation at save-time: prevent bad patterns from propagating
        # Using `strict` decorator guarantees that `self.validate` exists , but not all
        # model config might have the decorator added
        if hasattr(self, "validate"):
            self.validate()
        self.to_json_file(output_config_file, use_diff=True)
        logger.info(f"Configuration saved in {output_config_file}")

        if push_to_hub:
            self._upload_modified_files(
                save_directory,
                repo_id,
                files_timestamps,
                commit_message=commit_message,
                token=kwargs.get("token"),
            )

    @classmethod
    def from_pretrained(
        cls: type[SpecificPreTrainedConfigType],
        pretrained_model_name_or_path: str | os.PathLike,
        cache_dir: str | os.PathLike | None = None,
        force_download: bool = False,
        local_files_only: bool = False,
        token: str | bool | None = None,
        revision: str = "main",
        **kwargs,
    ) -> SpecificPreTrainedConfigType:
        r"""
        Instantiate a [`PreTrainedConfig`] (or a derived class) from a pretrained model configuration.

        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.
                - a path to a *directory* containing a configuration file saved using the
                  [`~PreTrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
                - a path to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`.
            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.
            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.
            token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `hf auth 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.

                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.

                </Tip>

            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final configuration object.

                If `True`, then this functions returns a `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.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            kwargs (`dict[str, Any]`, *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.

        Returns:
            [`PreTrainedConfig`]: The configuration object instantiated from this pretrained model.

        Examples:

        ```python
        # We can't instantiate directly the base class *PreTrainedConfig* so let's show the examples on a
        # derived class: BertConfig
        config = BertConfig.from_pretrained(
            "google-bert/bert-base-uncased"
        )  # Download configuration from huggingface.co and cache.
        config = BertConfig.from_pretrained(
            "./test/saved_model/"
        )  # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
        config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
        config = BertConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
        assert config.output_attentions == True
        config, unused_kwargs = BertConfig.from_pretrained(
            "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
        )
        assert config.output_attentions == True
        assert unused_kwargs == {"foo": False}
        ```"""
        kwargs["cache_dir"] = cache_dir
        kwargs["force_download"] = force_download
        kwargs["local_files_only"] = local_files_only
        kwargs["revision"] = revision

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
        if cls.base_config_key and cls.base_config_key in config_dict:
            config_dict = config_dict[cls.base_config_key]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            # sometimes the config has no `base_config_key` if the config is used in several composite models
            # e.g. LlamaConfig. In that case we try to see if there is match in `model_type` before raising a warning
            for v in config_dict.values():
                if isinstance(v, dict) and v.get("model_type") == cls.model_type:
                    config_dict = v

            # raise warning only if we still can't see a match in `model_type`
            if config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f"You are using a model of type `{config_dict['model_type']}` to instantiate a model of type "
                    f"`{cls.model_type}`. This may be expected if you are loading a checkpoint that shares a subset "
                    f"of the architecture (e.g., loading a `sam2_video` checkpoint into `Sam2Model`), but is otherwise "
                    f"not supported and can yield errors. Please verify that the checkpoint is compatible with the "
                    f"model you are instantiating."
                )

        return cls.from_dict(config_dict, **kwargs)

    @classmethod
    def get_config_dict(
        cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        """
        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
        [`PreTrainedConfig`] using `from_dict`.

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.

        Returns:
            `tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.

        """
        original_kwargs = copy.deepcopy(kwargs)
        # Get config dict associated with the base config file
        config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
        if config_dict is None:
            return {}, kwargs
        if "_commit_hash" in config_dict:
            original_kwargs["_commit_hash"] = config_dict["_commit_hash"]

        # That config file may point us toward another config file to use.
        if "configuration_files" in config_dict:
            configuration_file = get_configuration_file(config_dict["configuration_files"])
            config_dict, kwargs = cls._get_config_dict(
                pretrained_model_name_or_path, _configuration_file=configuration_file, **original_kwargs
            )

        return config_dict, kwargs

    @classmethod
    def _get_config_dict(
        cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)
        trust_remote_code = kwargs.pop("trust_remote_code", None)
        subfolder = kwargs.pop("subfolder", "")
        from_pipeline = kwargs.pop("_from_pipeline", None)
        from_auto_class = kwargs.pop("_from_auto", False)
        commit_hash = kwargs.pop("_commit_hash", None)

        gguf_file = kwargs.get("gguf_file")

        if trust_remote_code is True:
            logger.warning(
                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
                " ignored."
            )

        user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
        if from_pipeline is not None:
            user_agent["using_pipeline"] = from_pipeline

        pretrained_model_name_or_path = str(pretrained_model_name_or_path)

        is_local = os.path.isdir(pretrained_model_name_or_path)
        if os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
            # Special case when pretrained_model_name_or_path is a local file
            resolved_config_file = pretrained_model_name_or_path
            is_local = True
        else:
            configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME) if gguf_file is None else gguf_file

            try:
                # Load from local folder or from cache or download from model Hub and cache
                resolved_config_file = cached_file(
                    pretrained_model_name_or_path,
                    configuration_file,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    revision=revision,
                    subfolder=subfolder,
                    _commit_hash=commit_hash,
                )
                if resolved_config_file is None:
                    return None, kwargs
                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
            except OSError:
                # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
                # the original exception.
                raise
            except Exception:
                # For any other exception, we throw a generic error.
                raise OSError(
                    f"Can't load the configuration of '{pretrained_model_name_or_path}'. If you were trying to load it"
                    " from 'https://huggingface.co/models', make sure you don't have a local directory with the same"
                    f" name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory"
                    f" containing a {configuration_file} file"
                )

        try:
            if gguf_file:
                config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
            else:
                # Load config dict
                config_dict = cls._dict_from_json_file(resolved_config_file)

            config_dict["_commit_hash"] = commit_hash
        except (json.JSONDecodeError, UnicodeDecodeError):
            raise OSError(f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file.")

        if is_local:
            logger.info(f"loading configuration file {resolved_config_file}")
        else:
            logger.info(f"loading configuration file {configuration_file} from cache at {resolved_config_file}")

        # timm models are not saved with the model_type in the config file
        if "model_type" not in config_dict and is_timm_config_dict(config_dict):
            config_dict["model_type"] = "timm_wrapper"

        # Some checkpoints may contain the wrong model_type in the config file.
        # Allow the user to override it but warn them that it might not work.
        if "model_type" in kwargs and config_dict["model_type"] != kwargs["model_type"]:
            logger.warning(
                f"{configuration_file} has 'model_type={config_dict['model_type']}' but you overrode "
                f"it with 'model_type={kwargs['model_type']}'. This may lead to unexpected behavior."
            )
            config_dict["model_type"] = kwargs["model_type"]

        return config_dict, kwargs

    @classmethod
    def from_dict(
        cls: type[SpecificPreTrainedConfigType], config_dict: dict[str, Any], **kwargs
    ) -> SpecificPreTrainedConfigType:
        """
        Instantiates a [`PreTrainedConfig`] from a Python dictionary of parameters.

        Args:
            config_dict (`dict[str, Any]`):
                Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
                retrieved from a pretrained checkpoint by leveraging the [`~PreTrainedConfig.get_config_dict`] method.
            kwargs (`dict[str, Any]`):
                Additional parameters from which to initialize the configuration object.

        Returns:
            [`PreTrainedConfig`]: The configuration object instantiated from those parameters.
        """
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)

        # The commit hash might have been updated in the `config_dict`, we don't want the kwargs to erase that update.
        if "_commit_hash" in kwargs and "_commit_hash" in config_dict:
            kwargs.setdefault("_commit_hash", config_dict["_commit_hash"])

        # To remove arg here are those passed along for our internal telemetry but we still need to remove them
        to_remove = ["_from_auto", "_from_pipeline"]
        valid_fields = [
            "num_labels",
            "attn_implementation",
            "experts_implementation",
            "output_attentions",
            "torch_dtype",
            "dtype",
            "name_or_path",
        ]
        for key, value in kwargs.items():
            if key in valid_fields:
                if key not in ["torch_dtype", "dtype"]:
                    config_dict[key] = value
                    to_remove.append(key)
                elif value != "auto":
                    config_dict[key] = value

        config = cls(**config_dict)

        for key, value in kwargs.items():
            if hasattr(config, key):
                current_attr = getattr(config, key)
                # To authorize passing a custom subconfig as kwarg in models that have nested configs.
                # We need to update only custom kwarg values instead and keep other attr in subconfig.
                if isinstance(current_attr, PreTrainedConfig) and isinstance(value, dict):
                    current_attr_updated = current_attr.to_dict()
                    current_attr_updated.update(value)
                    value = current_attr.__class__(**current_attr_updated)
                setattr(config, key, value)
                to_remove.append(key)

        for key in to_remove:
            kwargs.pop(key, None)

        logger.info(f"Model config {config}")
        if return_unused_kwargs:
            return config, kwargs
        else:
            return config

    @classmethod
    def from_json_file(
        cls: type[SpecificPreTrainedConfigType], json_file: str | os.PathLike
    ) -> SpecificPreTrainedConfigType:
        """
        Instantiates a [`PreTrainedConfig`] from the path to a JSON file of parameters.

        Args:
            json_file (`str` or `os.PathLike`):
                Path to the JSON file containing the parameters.

        Returns:
            [`PreTrainedConfig`]: The configuration object instantiated from that JSON file.

        """
        config_dict = cls._dict_from_json_file(json_file)
        return cls(**config_dict)

    @classmethod
    def _dict_from_json_file(cls, json_file: str | os.PathLike):
        with open(json_file, encoding="utf-8") as reader:
            text = reader.read()
        config_dict = json.loads(text)

        return cls._decode_special_floats(config_dict)

    @classmethod
    def _encode_special_floats(cls, obj: Any) -> Any:
        """
        Iterates over the passed object and encode specific floats that cannot be JSON-serialized. Python's JSON
        engine saves floats like `Infinity` (+/-) or `NaN` which are not compatible with other JSON engines.

        It serializes floats like `Infinity` as an object: `{'__float__': Infinity}`.
        """
        if isinstance(obj, float):
            if math.isnan(obj):
                return {_FLOAT_TAG_KEY: "NaN"}
            if obj == float("inf"):
                return {_FLOAT_TAG_KEY: "Infinity"}
            if obj == float("-inf"):
                return {_FLOAT_TAG_KEY: "-Infinity"}
            return obj

        if isinstance(obj, dict):
            return {k: cls._encode_special_floats(v) for k, v in obj.items()}

        if isinstance(obj, (list, tuple)):
            return [cls._encode_special_floats(v) for v in obj]

        return obj

    @classmethod
    def _decode_special_floats(cls, obj: Any) -> Any:
        """
        Iterates over the passed object and decode specific floats that cannot be JSON-serialized. Python's JSON
        engine saves floats like `Infinity` (+/-) or `NaN` which are not compatible with other JSON engines.

        This method deserializes objects like `{'__float__': Infinity}` to their float values like `Infinity`.
        """
        if isinstance(obj, dict):
            if set(obj.keys()) == {_FLOAT_TAG_KEY} and isinstance(obj[_FLOAT_TAG_KEY], str):
                tag = obj[_FLOAT_TAG_KEY]
                if tag in _FLOAT_TAG_VALUES:
                    return _FLOAT_TAG_VALUES[tag]
                return obj

            return {k: cls._decode_special_floats(v) for k, v in obj.items()}

        if isinstance(obj, list):
            return [cls._decode_special_floats(v) for v in obj]

        return obj

    def __eq__(self, other):
        return isinstance(other, PreTrainedConfig) and (self.__dict__ == other.__dict__)

    def __repr__(self):
        return f"{self.__class__.__name__} {self.to_json_string()}"

    def __iter__(self):
        yield from self.__dict__

    def to_diff_dict(self) -> dict[str, Any]:
        """
        Removes all attributes from the configuration that correspond to the default config attributes for
        better readability, while always retaining the `config` attribute from the class. Serializes to a
        Python dictionary.

        Returns:
            dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.
        """
        config_dict = self.to_dict()

        # Get the default config dict (from a fresh PreTrainedConfig instance)
        default_config_dict = PreTrainedConfig().to_dict()

        # get class specific config dict
        class_config_dict = self.__class__().to_dict() if not self.has_no_defaults_at_init else {}

        serializable_config_dict = {}

        # Only serialize values that differ from the default config,
        # except always keep the 'config' attribute.
        for key, value in config_dict.items():
            if (
                isinstance(getattr(self, key, None), PreTrainedConfig)
                and key in class_config_dict
                and isinstance(class_config_dict[key], dict)
            ):
                # For nested configs we need to clean the diff recursively
                diff = recursive_diff_dict(value, default_config_dict, config_obj=getattr(self, key, None))
                if "model_type" in value:
                    # Needs to be set even if it's not in the diff
                    diff["model_type"] = value["model_type"]

                serializable_config_dict[key] = diff
            elif (
                key not in default_config_dict
                or key == "transformers_version"
                or key == "vocab_file"
                or value != default_config_dict[key]
                or (key in default_config_dict and value != class_config_dict.get(key, value))
            ):
                serializable_config_dict[key] = value

        self._remove_keys_not_serialized(serializable_config_dict)

        # Key removed only in diff dict
        if "_name_or_path" in serializable_config_dict:
            del serializable_config_dict["_name_or_path"]

        if hasattr(self, "quantization_config"):
            serializable_config_dict["quantization_config"] = (
                self.quantization_config.to_dict()
                if not isinstance(self.quantization_config, dict) and self.quantization_config is not None
                else self.quantization_config
            )
        self.dict_dtype_to_str(serializable_config_dict)

        return serializable_config_dict

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes this instance to a Python dictionary.

        Returns:
            `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
        """
        output = copy.deepcopy(self.__dict__)
        if hasattr(self.__class__, "model_type"):
            output["model_type"] = self.__class__.model_type

        # Transformers version when serializing the model
        output["transformers_version"] = __version__

        # Pop "kwargs" since they are unpacked and set in the post init
        output.pop("kwargs", None)

        if "distributed_config" in output and hasattr(output["distributed_config"], "to_dict"):
            output["distributed_config"] = output["distributed_config"].to_dict()

        def to_list(value):
            if isinstance(value, tuple):
                value = [to_list(item) for item in value]
            return value

        for key, value in output.items():
            # Deal with nested configs like CLIP
            if isinstance(value, PreTrainedConfig):
                value = value.to_dict()
                del value["transformers_version"]

            # Some models have defaults as tuples because dataclass
            # doesn't allow mutables. Let's convert back to `list``
            elif isinstance(value, tuple):
                value = to_list(value)

            output[key] = value

        self._remove_keys_not_serialized(output)

        if hasattr(self, "quantization_config"):
            output["quantization_config"] = (
                self.quantization_config.to_dict()
                if not isinstance(self.quantization_config, dict) and self.quantization_config is not None
                else self.quantization_config
            )
        self.dict_dtype_to_str(output)

        return output

    def to_json_string(self, use_diff: bool = True) -> str:
        """
        Serializes this instance to a JSON string.

        Args:
            use_diff (`bool`, *optional*, defaults to `True`):
                If set to `True`, only the difference between the config instance and the default `PreTrainedConfig()`
                is serialized to JSON string.

        Returns:
            `str`: String containing all the attributes that make up this configuration instance in JSON format.
        """
        if use_diff is True:
            config_dict = self.to_diff_dict()
        else:
            config_dict = self.to_dict()

        # Handle +/-Infinity and NaNs
        config_dict = self._encode_special_floats(config_dict)

        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path: str | os.PathLike, use_diff: bool = True):
        """
        Save this instance to a JSON file.

        Args:
            json_file_path (`str` or `os.PathLike`):
                Path to the JSON file in which this configuration instance's parameters will be saved.
            use_diff (`bool`, *optional*, defaults to `True`):
                If set to `True`, only the difference between the config instance and the default `PreTrainedConfig()`
                is serialized to JSON file.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string(use_diff=use_diff))

    def update(self, config_dict: dict[str, Any]):
        """
        Updates attributes of this class with attributes from `config_dict`.

        Args:
            config_dict (`dict[str, Any]`): Dictionary of attributes that should be updated for this class.
        """
        for key, value in config_dict.items():
            setattr(self, key, value)

    def update_from_string(self, update_str: str):
        """
        Updates attributes of this class with attributes from `update_str`.

        The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example:
        "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"

        The keys to change have to already exist in the config object.

        Args:
            update_str (`str`): String with attributes that should be updated for this class.

        """

        d = dict(x.split("=") for x in update_str.split(","))
        for k, v in d.items():
            if not hasattr(self, k):
                raise ValueError(f"key {k} isn't in the original config dict")

            old_v = getattr(self, k)
            if isinstance(old_v, bool):
                if v.lower() in ["true", "1", "y", "yes"]:
                    v = True
                elif v.lower() in ["false", "0", "n", "no"]:
                    v = False
                else:
                    raise ValueError(f"can't derive true or false from {v} (key {k})")
            elif isinstance(old_v, int):
                v = int(v)
            elif isinstance(old_v, float):
                v = float(v)
            elif not isinstance(old_v, str):
                raise TypeError(
                    f"You can only update int, float, bool or string values in the config, got {v} for key {k}"
                )

            setattr(self, k, v)

    def dict_dtype_to_str(self, d: dict[str, Any]) -> None:
        """
        Checks whether the passed dictionary and its nested dicts have a *dtype* key and if it's not None,
        converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
        string, which can then be stored in the json format.
        """
        if d.get("dtype") is not None:
            if isinstance(d["dtype"], dict):
                d["dtype"] = {k: str(v).split(".")[-1] for k, v in d["dtype"].items()}
            # models like Emu3 can have "dtype" as token in config's vocabulary map,
            # so we also exclude int type here to avoid error in this special case.
            elif not isinstance(d["dtype"], (str, int)):
                d["dtype"] = str(d["dtype"]).split(".")[1]
        for value in d.values():
            if isinstance(value, dict):
                self.dict_dtype_to_str(value)

    def _remove_keys_not_serialized(self, d: dict[str, Any]) -> None:
        """
        Checks and removes if there are any keys in the dict that should not be serialized when saving the config.
        Runs recursive check on the dict, to remove from all sub configs.
        """

        for key_to_remove in [
            "_is_quantized",
            "_auto_class",
            "_commit_hash",
            "_attn_implementation_internal",
            "_experts_implementation_internal",
            "ignore_keys_at_rope_validation",
            "base_model_tp_plan",
            "base_model_pp_plan",
        ]:
            d.pop(key_to_remove, None)

        if "_output_attentions" in d:
            d["output_attentions"] = d.pop("_output_attentions")

        for value in d.values():
            if isinstance(value, dict):
                self._remove_keys_not_serialized(value)

    @classmethod
    def register_for_auto_class(cls, auto_class="AutoConfig"):
        """
        Register this class with a given auto class. This should only be used for custom configurations as the ones in
        the library are already mapped with `AutoConfig`.



        Args:
            auto_class (`str` or `type`, *optional*, defaults to `"AutoConfig"`):
                The auto class to register this new configuration with.
        """
        if not isinstance(auto_class, str):
            auto_class = auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(auto_module, auto_class):
            raise ValueError(f"{auto_class} is not a valid auto class.")

        cls._auto_class = auto_class

    def _get_generation_parameters(self) -> dict[str, Any]:
        """
        Checks if there are generation parameters in `PreTrainedConfig` instance. Note that
        we should not save generation params in PreTrainedConfig, and we will raise error
        if there are any.
        """
        generation_params = {}
        default_config = self.__class__().to_dict() if not self.has_no_defaults_at_init else {}
        for key in GenerationConfig._get_default_generation_params().keys():
            if key == "use_cache":
                continue  # common key for most models
            if hasattr(self, key) and getattr(self, key) is not None and key not in default_config:
                generation_params[key] = getattr(self, key)

        return generation_params

    def get_text_config(self, decoder=None, encoder=None) -> "PreTrainedConfig":
        """
        Returns the text config related to the text input (encoder) or text output (decoder) of the model. The
        `decoder` and `encoder` input arguments can be used to specify which end of the model we are interested in,
        which is useful on models that have both text input and output modalities.

        There are three possible outcomes of using this method:
        1. On most models, it returns the original config instance itself.
        2. On newer (2024+) composite models, it returns the text section of the config, which is nested under a set
            of valid names.
        3. On older (2023-) composite models, it discards decoder-only parameters when `encoder=True` and vice-versa.

        Args:
            decoder (`Optional[bool]`, *optional*):
                If set to `True`, then only search for decoder config names.
            encoder (`Optional[bool]`, *optional*):
                If set to `True`, then only search for encoder config names.
        """
        return_both = decoder == encoder  # both unset or both set -> search all possible names

        decoder_possible_text_config_names = ("decoder", "generator", "text_config")
        encoder_possible_text_config_names = ("text_encoder",)
        if return_both:
            possible_text_config_names = encoder_possible_text_config_names + decoder_possible_text_config_names
        elif decoder:
            possible_text_config_names = decoder_possible_text_config_names
        else:
            possible_text_config_names = encoder_possible_text_config_names

        valid_text_config_names = []
        for text_config_name in possible_text_config_names:
            if hasattr(self, text_config_name):
                text_config = getattr(self, text_config_name, None)
                if text_config is not None:
                    valid_text_config_names += [text_config_name]

        if len(valid_text_config_names) > 1:
            raise ValueError(
                f"Multiple valid text configs were found in the model config: {valid_text_config_names}. In this "
                "case, using `get_text_config()` would be ambiguous. Please specify the desired text config directly, "
                "e.g. `text_config = config.sub_config_name`"
            )
        elif len(valid_text_config_names) == 1:
            config_to_return = getattr(self, valid_text_config_names[0])
        else:
            config_to_return = self

        # handle legacy models with flat config structure, when we only want one of the configs
        if not return_both and len(valid_text_config_names) == 0 and config_to_return.is_encoder_decoder:
            config_to_return = copy.deepcopy(config_to_return)
            prefix_to_keep = "decoder" if decoder else "encoder"
            for key in config_to_return.to_dict():
                # NOTE: We can't discard keys because:
                # 1) we can't truly delete a cls attribute on a dataclass; 2) we can't set the value to `None` due to
                # strict validation. So we just keep it as is, since there are only a couple old models falling in this condition
                if key.startswith(prefix_to_keep):
                    # [encoder/decoder]_layers -> num_hidden_layers
                    if key == prefix_to_keep + "_layers":
                        new_key = "num_hidden_layers"
                    # [encoder/decoder]_attention_heads -> num_attention_heads
                    elif key == prefix_to_keep + "_attention_heads":
                        new_key = "num_attention_heads"
                    # e.g. encoder_hidden_act -> hidden_act
                    else:
                        new_key = key[len(prefix_to_keep) + 1 :]

                    # Does the class map the new key into a different attribute name at read time? if so, let's write
                    # into that attribute instead
                    if new_key in config_to_return.attribute_map:
                        new_key = config_to_return.attribute_map[new_key]

                    value = getattr(config_to_return, key)
                    delattr(config_to_return, key)
                    setattr(config_to_return, new_key, value)

        return config_to_return


def get_configuration_file(configuration_files: list[str]) -> str:
    """
    Get the configuration file to use for this version of transformers.

    Args:
        configuration_files (`list[str]`): The list of available configuration files.

    Returns:
        `str`: The configuration file to use.
    """
    configuration_files_map = {}
    for file_name in configuration_files:
        if file_name.startswith("config.") and file_name.endswith(".json") and file_name != "config.json":
            v = file_name.removeprefix("config.").removesuffix(".json")
            configuration_files_map[v] = file_name
    available_versions = sorted(configuration_files_map.keys())

    # Defaults to FULL_CONFIGURATION_FILE and then try to look at some newer versions.
    configuration_file = CONFIG_NAME
    transformers_version = version.parse(__version__)
    for v in available_versions:
        if version.parse(v) <= transformers_version:
            configuration_file = configuration_files_map[v]
        else:
            # No point going further since the versions are sorted.
            break

    return configuration_file


def recursive_diff_dict(dict_a, dict_b, config_obj=None):
    """
    Helper function to recursively take the diff between two nested dictionaries. The resulting diff only contains the
    values from `dict_a` that are different from values in `dict_b`.

    dict_b : the default config dictionary. We want to remove values that are in this one
    """
    diff = {}
    default = config_obj.__class__().to_dict() if config_obj is not None else {}
    for key, value in dict_a.items():
        obj_value = getattr(config_obj, str(key), None)
        if isinstance(obj_value, PreTrainedConfig) and key in dict_b and isinstance(dict_b[key], dict):
            diff_value = recursive_diff_dict(value, dict_b[key], config_obj=obj_value)
            diff[key] = diff_value
        elif key not in dict_b or (value != default[key]):
            diff[key] = value
    return diff


PreTrainedConfig.push_to_hub = copy_func(PreTrainedConfig.push_to_hub)
if PreTrainedConfig.push_to_hub.__doc__ is not None:
    PreTrainedConfig.push_to_hub.__doc__ = PreTrainedConfig.push_to_hub.__doc__.format(
        object="config", object_class="AutoConfig", object_files="configuration file"
    )


# The alias is only here for BC - we did not have the correct CamelCasing before
PretrainedConfig = PreTrainedConfig


def layer_type_validation(layer_types: list[str], num_hidden_layers: int | None = None, attention: bool = True):
    logger.warning(
        "`layer_type_validation` is deprecated and will be removed in v5.20. "
        "Use `PreTrainedConfig.validate_layer_type` instead"
    )

    if not all(layer_type in ALLOWED_LAYER_TYPES for layer_type in layer_types):
        raise ValueError(f"The `layer_types` entries must be in {ALLOWED_LAYER_TYPES}")
    if num_hidden_layers is not None and num_hidden_layers != len(layer_types):
        raise ValueError(
            f"`num_hidden_layers` ({num_hidden_layers}) must be equal to the number of layer types "
            f"({len(layer_types)})"
        )
