Upload configuration_utils.py

configuration_utils.py

@@ -0,0 +1,1133 @@
+# coding=utf-8
+# 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 os
+import re
+import warnings
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from packaging import version
+
+from . import __version__
+from .dynamic_module_utils import custom_object_save
+from .utils import (
+    CONFIG_NAME,
+    PushToHubMixin,
+    add_model_info_to_auto_map,
+    cached_file,
+    copy_func,
+    download_url,
+    extract_commit_hash,
+    is_remote_url,
+    is_torch_available,
+    logging,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_re_configuration_file = re.compile(r"config\.(.*)\.json")
+
+
+class PretrainedConfig(PushToHubMixin):
+    # 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`].
+    - **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the
+      config has to be initialized from two or more configs of type [`~transformers.PretrainedConfig`] like:
+      [`~transformers.EncoderDecoderConfig`] or [`~RagConfig`].
+    - **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.
+
+    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.
+
+    Arg:
+        name_or_path (`str`, *optional*, defaults to `""`):
+            Store the string that was passed to [`PreTrainedModel.from_pretrained`] or
+            [`TFPreTrainedModel.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.
+        is_decoder (`bool`, *optional*, defaults to `False`):
+            Whether the model is used as decoder or not (in which case it's used as an encoder).
+        cross_attention_hidden_size** (`bool`, *optional*):
+            The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
+            setting and the cross-attention hidden dimension differs from `self.config.hidden_size`.
+        add_cross_attention (`bool`, *optional*, defaults to `False`):
+            Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
+            that can be used as decoder models within the [`EncoderDecoderModel`] class, which consists of all models
+            in `AUTO_MODELS_FOR_CAUSAL_LM`.
+        tie_encoder_decoder (`bool`, *optional*, defaults to `False`):
+            Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
+            and decoder model to have the exact same parameter names.
+        prune_heads (`Dict[int, List[int]]`, *optional*, defaults to `{}`):
+            Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
+            heads to prune in said layer.
+
+            For instance `{1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
+        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 sequence generation
+
+        max_length (`int`, *optional*, defaults to 20):
+            Maximum length that will be used by default in the `generate` method of the model.
+        min_length (`int`, *optional*, defaults to 0):
+            Minimum length that will be used by default in the `generate` method of the model.
+        do_sample (`bool`, *optional*, defaults to `False`):
+            Flag that will be used by default in the `generate` method of the model. Whether or not to use sampling ;
+            use greedy decoding otherwise.
+        early_stopping (`bool`, *optional*, defaults to `False`):
+            Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
+            when at least `num_beams` sentences are finished per batch or not.
+        num_beams (`int`, *optional*, defaults to 1):
+            Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
+            no beam search.
+        num_beam_groups (`int`, *optional*, defaults to 1):
+            Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams
+            that will be used by default in the `generate` method of the model. 1 means no group beam search.
+        diversity_penalty (`float`, *optional*, defaults to 0.0):
+            Value to control diversity for group beam search. that will be used by default in the `generate` method of
+            the model. 0 means no diversity penalty. The higher the penalty, the more diverse are the outputs.
+        temperature (`float`, *optional*, defaults to 1.0):
+            The value used to module the next token probabilities that will be used by default in the `generate` method
+            of the model. Must be strictly positive.
+        top_k (`int`, *optional*, defaults to 50):
+            Number of highest probability vocabulary tokens to keep for top-k-filtering that will be used by default in
+            the `generate` method of the model.
+        top_p (`float`, *optional*, defaults to 1):
+            Value that will be used by default in the `generate` method of the model for `top_p`. If set to float < 1,
+            only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
+        typical_p (`float`, *optional*, defaults to 1):
+            Local typicality measures how similar the conditional probability of predicting a target token next is to
+            the expected conditional probability of predicting a random token next, given the partial text already
+            generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that
+            add up to `typical_p` or higher are kept for generation. See [this
+            paper](https://arxiv.org/pdf/2202.00666.pdf) for more details.
+        repetition_penalty (`float`, *optional*, defaults to 1):
+            Parameter for repetition penalty that will be used by default in the `generate` method of the model. 1.0
+            means no penalty.
+        length_penalty (`float`, *optional*, defaults to 1):
+            Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
+            the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
+            likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
+            `length_penalty` < 0.0 encourages shorter sequences.
+        no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by default in the
+            `generate` method of the model for `no_repeat_ngram_size`. If set to int > 0, all ngrams of that size can
+            only occur once.
+        encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by
+            default in the `generate` method of the model for `encoder_no_repeat_ngram_size`. If set to int > 0, all
+            ngrams of that size that occur in the `encoder_input_ids` cannot occur in the `decoder_input_ids`.
+        bad_words_ids (`List[int]`, *optional*):
+            List of token ids that are not allowed to be generated that will be used by default in the `generate`
+            method of the model. In order to get the tokens of the words that should not appear in the generated text,
+            use `tokenizer.encode(bad_word, add_prefix_space=True)`.
+        num_return_sequences (`int`, *optional*, defaults to 1):
+            Number of independently computed returned sequences for each element in the batch that will be used by
+            default in the `generate` method of the model.
+        output_scores (`bool`, *optional*, defaults to `False`):
+            Whether the model should return the logits when used for generation.
+        return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+            Whether the model should return a [`~transformers.utils.ModelOutput`] instead of a `torch.LongTensor`.
+        forced_bos_token_id (`int`, *optional*):
+            The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
+            multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
+            language token.
+        forced_eos_token_id (`int`, *optional*):
+            The id of the token to force as the last generated token when `max_length` is reached.
+        remove_invalid_values (`bool`, *optional*):
+            Whether to remove possible _nan_ and _inf_ outputs of the model to prevent the generation method to crash.
+            Note that using `remove_invalid_values` can slow down generation.
+
+        > Parameters for fine-tuning tasks
+
+        architectures (`List[str]`, *optional*):
+            Model architectures that can be used with the model pretrained weights.
+        finetuning_task (`str`, *optional*):
+            Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
+            or PyTorch) checkpoint.
+        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.
+        task_specific_params (`Dict[str, Any]`, *optional*):
+            Additional keyword arguments to store for the current task.
+        problem_type (`str`, *optional*):
+            Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
+            `"single_label_classification"` or `"multi_label_classification"`.
+
+        > Parameters linked to the tokenizer
+
+        tokenizer_class (`str`, *optional*):
+            The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
+            model by default).
+        prefix (`str`, *optional*):
+            A specific prompt that should be added at the beginning of each text before calling the model.
+        bos_token_id (`int`, *optional*): The id of the _beginning-of-stream_ token.
+        pad_token_id (`int`, *optional*): The id of the _padding_ token.
+        eos_token_id (`int`, *optional*): The id of the _end-of-stream_ token.
+        decoder_start_token_id (`int`, *optional*):
+            If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token.
+        sep_token_id (`int`, *optional*): The id of the _separation_ token.
+
+        > PyTorch specific parameters
+
+        torchscript (`bool`, *optional*, defaults to `False`):
+            Whether or not the model should be used with Torchscript.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
+            model has a output word embedding layer.
+        torch_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. Since the config object is stored in plain text, this attribute contains just the
+            floating type string without the `torch.` prefix. For example, for `torch.float16` ``torch_dtype` is the
+            `"float16"` string.
+
+            This attribute is currently not being used during model loading time, but this may change in the future
+            versions. But we can already start preparing for the future by saving the dtype with save_pretrained.
+
+        > TensorFlow specific parameters
+
+        use_bfloat16 (`bool`, *optional*, defaults to `False`):
+            Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models).
+        tf_legacy_loss (`bool`, *optional*, defaults to `False`):
+            Whether the model should use legacy TensorFlow losses. Legacy losses have variable output shapes and may
+            not be XLA-compatible. This option is here for backward compatibility and will be removed in Transformers
+            v5.
+    """
+
+    model_type: str = ""
+    is_composition: bool = False
+    attribute_map: Dict[str, str] = {}
+    _auto_class: Optional[str] = None
+
+    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 __init__(self, **kwargs):
+        # Attributes with defaults
+        self.return_dict = kwargs.pop("return_dict", True)
+        self.output_hidden_states = kwargs.pop("output_hidden_states", False)
+        self.output_attentions = kwargs.pop("output_attentions", False)
+        self.torchscript = kwargs.pop("torchscript", False)  # Only used by PyTorch models
+        self.torch_dtype = kwargs.pop("torch_dtype", None)  # Only used by PyTorch models
+        self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
+        self.tf_legacy_loss = kwargs.pop("tf_legacy_loss", False)  # Only used by TensorFlow models
+        self.pruned_heads = kwargs.pop("pruned_heads", {})
+        self.tie_word_embeddings = kwargs.pop(
+            "tie_word_embeddings", True
+        )  # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models.
+        self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
+
+        # Is decoder is used in encoder-decoder models to differentiate encoder from decoder
+        self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
+        self.is_decoder = kwargs.pop("is_decoder", False)
+        self.cross_attention_hidden_size = kwargs.pop("cross_attention_hidden_size", None)
+        self.add_cross_attention = kwargs.pop("add_cross_attention", False)
+        self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False)
+
+        # Retrocompatibility: Parameters for sequence generation. While we will keep the ability to load these
+        # parameters, saving them will be deprecated. In a distant future, we won't need to load them.
+        for parameter_name, default_value in self._get_generation_defaults().items():
+            setattr(self, parameter_name, kwargs.pop(parameter_name, default_value))
+
+        # Fine-tuning task arguments
+        self.architectures = kwargs.pop("architectures", None)
+        self.finetuning_task = kwargs.pop("finetuning_task", None)
+        self.id2label = kwargs.pop("id2label", None)
+        self.label2id = kwargs.pop("label2id", None)
+        if self.label2id is not None and not isinstance(self.label2id, dict):
+            raise ValueError("Argument label2id should be a dictionary.")
+        if self.id2label is not None:
+            if not isinstance(self.id2label, dict):
+                raise ValueError("Argument id2label should be a dictionary.")
+            num_labels = kwargs.pop("num_labels", None)
+            if num_labels is not None and len(self.id2label) != num_labels:
+                logger.warning(
+                    f"You passed along `num_labels={num_labels}` with an incompatible id to label map: "
+                    f"{self.id2label}. The number of labels wil be overwritten to {self.num_labels}."
+                )
+            self.id2label = {int(key): value for key, value in self.id2label.items()}
+            # Keys are always strings in JSON so convert ids to int here.
+        else:
+            self.num_labels = kwargs.pop("num_labels", 2)
+
+        if self.torch_dtype is not None and isinstance(self.torch_dtype, str):
+            # we will start using self.torch_dtype in v5, but to be consistent with
+            # from_pretrained's torch_dtype arg convert it to an actual torch.dtype object
+            if is_torch_available():
+                import torch
+
+                self.torch_dtype = getattr(torch, self.torch_dtype)
+
+        # Tokenizer arguments TODO: eventually tokenizer and models should share the same config
+        self.tokenizer_class = kwargs.pop("tokenizer_class", None)
+        self.prefix = kwargs.pop("prefix", None)
+        self.bos_token_id = kwargs.pop("bos_token_id", None)
+        self.pad_token_id = kwargs.pop("pad_token_id", None)
+        self.eos_token_id = kwargs.pop("eos_token_id", None)
+        self.sep_token_id = kwargs.pop("sep_token_id", None)
+
+        self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
+
+        # task specific arguments
+        self.task_specific_params = kwargs.pop("task_specific_params", None)
+
+        # regression / multi-label classification
+        self.problem_type = kwargs.pop("problem_type", None)
+        allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification")
+        if self.problem_type is not None and self.problem_type not in allowed_problem_types:
+            raise ValueError(
+                f"The config parameter `problem_type` was not understood: received {self.problem_type} "
+                "but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid."
+            )
+
+        # TPU arguments
+        if kwargs.pop("xla_device", None) is not None:
+            logger.warning(
+                "The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can "
+                "safely remove it from your `config.json` file."
+            )
+
+        # Name or path to the pretrained checkpoint
+        self._name_or_path = str(kwargs.pop("name_or_path", ""))
+        # Config hash
+        self._commit_hash = kwargs.pop("_commit_hash", None)
+
+        # Attention implementation to use, if relevant.
+        self._attn_implementation_internal = kwargs.pop("attn_implementation", None)
+
+        # Drop the transformers version info
+        self.transformers_version = kwargs.pop("transformers_version", None)
+
+        # Deal with gradient checkpointing
+        if kwargs.get("gradient_checkpointing", False):
+            warnings.warn(
+                "Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
+                "Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the "
+                "`Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`."
+            )
+
+        # Additional attributes without default values
+        for key, value in kwargs.items():
+            try:
+                setattr(self, key, value)
+            except AttributeError as err:
+                logger.error(f"Can't set {key} with value {value} for {self}")
+                raise err
+
+    @property
+    def name_or_path(self) -> str:
+        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 use_return_dict(self) -> bool:
+        """
+        `bool`: Whether or not return [`~utils.ModelOutput`] instead of tuples.
+        """
+        # If torchscript is set, force `return_dict=False` to avoid jit errors
+        return self.return_dict and not self.torchscript
+
+    @property
+    def num_labels(self) -> int:
+        """
+        `int`: The number of labels for classification models.
+        """
+        return len(self.id2label)
+
+    @num_labels.setter
+    def num_labels(self, num_labels: int):
+        if not hasattr(self, "id2label") or self.id2label is None or len(self.id2label) != 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 _attn_implementation(self):
+        # This property is made private for now (as it cannot be changed and a PreTrainedModel.use_attn_implementation method needs to be implemented.)
+        if hasattr(self, "_attn_implementation_internal"):
+            if self._attn_implementation_internal is None:
+                # `config.attn_implementation` should never be None, for backward compatibility.
+                return "eager"
+            else:
+                return self._attn_implementation_internal
+        else:
+            return "eager"
+
+    @_attn_implementation.setter
+    def _attn_implementation(self, value):
+        self._attn_implementation_internal = value
+
+    def save_pretrained(self, save_directory: Union[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.
+        """
+        self._set_token_in_kwargs(kwargs)
+
+        if os.path.isfile(save_directory):
+            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        non_default_generation_parameters = {}
+        for parameter_name, default_value in self._get_generation_defaults().items():
+            if hasattr(self, parameter_name) and getattr(self, parameter_name) != default_value:
+                non_default_generation_parameters[parameter_name] = getattr(self, parameter_name)
+        if len(non_default_generation_parameters) > 0:
+            logger.warning(
+                "Some non-default generation parameters are set in the model config. These should go into a "
+                "GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) "
+                "instead. This warning will be raised to an exception in v4.41.\n"
+                f"Non-default generation parameters: {str(non_default_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 = self._create_repo(repo_id, **kwargs)
+            files_timestamps = self._get_files_timestamps(save_directory)
+
+        # 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)
+
+        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"),
+            )
+
+    @staticmethod
+    def _set_token_in_kwargs(kwargs, token=None):
+        """Temporary method to deal with `token` and `use_auth_token`.
+
+        This method is to avoid apply the same changes in all model config classes that overwrite `from_pretrained`.
+
+        Need to clean up `use_auth_token` in a follow PR.
+        """
+        # Some model config classes like CLIP define their own `from_pretrained` without the new argument `token` yet.
+        if token is None:
+            token = kwargs.pop("token", None)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+
+        if use_auth_token is not None:
+            warnings.warn(
+                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
+                FutureWarning,
+            )
+            if token is not None:
+                raise ValueError(
+                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
+                )
+            token = use_auth_token
+
+        if token is not None:
+            kwargs["token"] = token
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        **kwargs,
+    ) -> "PretrainedConfig":
+        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 or url 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.
+            resume_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
+                exists.
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
+            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 `huggingface-cli login` (stored in `~/.huggingface`).
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
+                identifier allowed by git.
+
+                <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
+
+        cls._set_token_in_kwargs(kwargs, token)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        if "model_type" in config_dict and hasattr(cls, "model_type") and 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 is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+    @classmethod
+    def get_config_dict(
+        cls, pretrained_model_name_or_path: Union[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.
+
+        """
+        cls._set_token_in_kwargs(kwargs)
+
+        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 "_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: Union[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)
+        resume_download = kwargs.pop("resume_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)
+
+        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
+        elif is_remote_url(pretrained_model_name_or_path):
+            configuration_file = pretrained_model_name_or_path
+            resolved_config_file = download_url(pretrained_model_name_or_path)
+        else:
+            configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME)
+
+            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,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                    token=token,
+                    user_agent=user_agent,
+                    revision=revision,
+                    subfolder=subfolder,
+                    _commit_hash=commit_hash,
+                )
+                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
+            except EnvironmentError:
+                # 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 EnvironmentError(
+                    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:
+            # Load config dict
+            config_dict = cls._dict_from_json_file(resolved_config_file)
+            config_dict["_commit_hash"] = commit_hash
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            raise EnvironmentError(
+                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}")
+
+        if "auto_map" in config_dict and not is_local:
+            config_dict["auto_map"] = add_model_info_to_auto_map(
+                config_dict["auto_map"], pretrained_model_name_or_path
+            )
+        return config_dict, kwargs
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig":
+        """
+        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)
+        # Those arguments may be passed along for our internal telemetry.
+        # We remove them so they don't appear in `return_unused_kwargs`.
+        kwargs.pop("_from_auto", None)
+        kwargs.pop("_from_pipeline", None)
+        # 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["_commit_hash"] = config_dict["_commit_hash"]
+
+        # We remove it from kwargs so that it does not appear in `return_unused_kwargs`.
+        config_dict["attn_implementation"] = kwargs.pop("attn_implementation", None)
+
+        config = cls(**config_dict)
+
+        if hasattr(config, "pruned_heads"):
+            config.pruned_heads = {int(key): value for key, value in config.pruned_heads.items()}
+
+        # Update config with kwargs if needed
+        if "num_labels" in kwargs and "id2label" in kwargs:
+            num_labels = kwargs["num_labels"]
+            id2label = kwargs["id2label"] if kwargs["id2label"] is not None else []
+            if len(id2label) != num_labels:
+                raise ValueError(
+                    f"You passed along `num_labels={num_labels }` with an incompatible id to label map: "
+                    f"{kwargs['id2label']}. Since those arguments are inconsistent with each other, you should remove "
+                    "one of them."
+                )
+        to_remove = []
+        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.
+                if isinstance(current_attr, PretrainedConfig) and isinstance(value, dict):
+                    value = current_attr.__class__(**value)
+                setattr(config, key, value)
+                if key != "torch_dtype":
+                    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, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
+        """
+        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: Union[str, os.PathLike]):
+        with open(json_file, "r", encoding="utf-8") as reader:
+            text = reader.read()
+        return json.loads(text)
+
+    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 to_diff_dict(self) -> Dict[str, Any]:
+        """
+        Removes all attributes from config which correspond to the default config attributes for better readability and
+        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
+        default_config_dict = PretrainedConfig().to_dict()
+
+        # get class specific config dict
+        class_config_dict = self.__class__().to_dict() if not self.is_composition else {}
+
+        serializable_config_dict = {}
+
+        # only serialize values that differ from the default config
+        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, class_config_dict[key], 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"]
+                if len(diff) > 0:
+                    serializable_config_dict[key] = diff
+            elif (
+                key not in default_config_dict
+                or key == "transformers_version"
+                or value != default_config_dict[key]
+                or (key in class_config_dict and value != class_config_dict[key])
+            ):
+                serializable_config_dict[key] = value
+
+        if hasattr(self, "quantization_config"):
+            serializable_config_dict["quantization_config"] = (
+                self.quantization_config.to_dict()
+                if not isinstance(self.quantization_config, dict)
+                else self.quantization_config
+            )
+
+            # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
+            _ = serializable_config_dict.pop("_pre_quantization_dtype", None)
+
+        self.dict_torch_dtype_to_str(serializable_config_dict)
+
+        if "_attn_implementation_internal" in serializable_config_dict:
+            del serializable_config_dict["_attn_implementation_internal"]
+
+        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
+        if "_auto_class" in output:
+            del output["_auto_class"]
+        if "_commit_hash" in output:
+            del output["_commit_hash"]
+        if "_attn_implementation_internal" in output:
+            del output["_attn_implementation_internal"]
+
+        # Transformers version when serializing the model
+        output["transformers_version"] = __version__
+
+        for key, value in output.items():
+            # Deal with nested configs like CLIP
+            if isinstance(value, PretrainedConfig):
+                value = value.to_dict()
+                del value["transformers_version"]
+
+            output[key] = value
+
+        if hasattr(self, "quantization_config"):
+            output["quantization_config"] = (
+                self.quantization_config.to_dict()
+                if not isinstance(self.quantization_config, dict)
+                else self.quantization_config
+            )
+
+            # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
+            _ = output.pop("_pre_quantization_dtype", None)
+
+        self.dict_torch_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()
+        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path: Union[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 ValueError(
+                    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_torch_dtype_to_str(self, d: Dict[str, Any]) -> None:
+        """
+        Checks whether the passed dictionary and its nested dicts have a *torch_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("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str):
+            d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1]
+        for value in d.values():
+            if isinstance(value, dict):
+                self.dict_torch_dtype_to_str(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`.
+
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
+        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
+
+    @staticmethod
+    def _get_generation_defaults() -> Dict[str, Any]:
+        return {
+            "max_length": 20,
+            "min_length": 0,
+            "do_sample": False,
+            "early_stopping": False,
+            "num_beams": 1,
+            "num_beam_groups": 1,
+            "diversity_penalty": 0.0,
+            "temperature": 1.0,
+            "top_k": 50,
+            "top_p": 1.0,
+            "typical_p": 1.0,
+            "repetition_penalty": 1.0,
+            "length_penalty": 1.0,
+            "no_repeat_ngram_size": 0,
+            "encoder_no_repeat_ngram_size": 0,
+            "bad_words_ids": None,
+            "num_return_sequences": 1,
+            "output_scores": False,
+            "return_dict_in_generate": False,
+            "forced_bos_token_id": None,
+            "forced_eos_token_id": None,
+            "remove_invalid_values": False,
+            "exponential_decay_length_penalty": None,
+            "suppress_tokens": None,
+            "begin_suppress_tokens": None,
+        }
+
+    def _has_non_default_generation_parameters(self) -> bool:
+        """
+        Whether or not this instance holds non-default generation parameters.
+        """
+        for parameter_name, default_value in self._get_generation_defaults().items():
+            if hasattr(self, parameter_name) and getattr(self, parameter_name) != default_value:
+                return True
+        return False
+
+
+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:
+        search = _re_configuration_file.search(file_name)
+        if search is not None:
+            v = search.groups()[0]
+            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`.
+    """
+    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)
+            if len(diff_value) > 0:
+                diff[key] = diff_value
+        elif key not in dict_b or value != dict_b[key] or key not in default 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"
+    )