# 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.
"""TF general model utils."""
import functools
import os
import re
import warnings
from typing import Dict, List, Optional, Union

import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.saving import hdf5_format

from .configuration_utils import PretrainedConfig
from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
from .generation_tf_utils import TFGenerationMixin
from .utils import logging


logger = logging.get_logger(__name__)


class TFModelUtilsMixin:
    """
    A few utilities for :obj:`tf.keras.Model`, to be used as a mixin.
    """

    def num_parameters(self, only_trainable: bool = False) -> int:
        """
        Get the number of (optionally, trainable) parameters in the model.

        Args:
            only_trainable (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to return only the number of trainable parameters

        Returns:
            :obj:`int`: The number of parameters.
        """
        if only_trainable:
            return int(sum(np.prod(w.shape.as_list()) for w in self.trainable_variables))
        else:
            return self.count_params()


def keras_serializable(cls):
    """
    Decorate a Keras Layer class to support Keras serialization.

    This is done by:

    1. Adding a :obj:`transformers_config` dict to the Keras config dictionary in :obj:`get_config` (called by Keras at
       serialization time.
    2. Wrapping :obj:`__init__` to accept that :obj:`transformers_config` dict (passed by Keras at deserialization
       time) and convert it to a config object for the actual layer initializer.
    3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does
       not need to be supplied in :obj:`custom_objects` in the call to :obj:`tf.keras.models.load_model`.

    Args:
        cls (a :obj:`tf.keras.layers.Layers subclass`):
            Typically a :obj:`TF.MainLayer` class in this project, in general must accept a :obj:`config` argument to
            its initializer.

    Returns:
        The same class object, with modifications for Keras deserialization.
    """
    initializer = cls.__init__

    config_class = getattr(cls, "config_class", None)
    if config_class is None:
        raise AttributeError("Must set `config_class` to use @keras_serializable")

    @functools.wraps(initializer)
    def wrapped_init(self, *args, **kwargs):
        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.pop("config", None)

        if isinstance(config, dict):
            config = config_class.from_dict(config)
            initializer(self, config, *args, **kwargs)
        elif isinstance(config, PretrainedConfig):
            if len(args) > 0:
                initializer(self, *args, **kwargs)
            else:
                initializer(self, config, *args, **kwargs)
        else:
            raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)")

        self._config = config
        self._kwargs = kwargs

    cls.__init__ = wrapped_init

    if not hasattr(cls, "get_config"):
        raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses")
    if hasattr(cls.get_config, "_is_default"):

        def get_config(self):
            cfg = super(cls, self).get_config()
            cfg["config"] = self._config.to_dict()
            cfg.update(self._kwargs)
            return cfg

        cls.get_config = get_config

    cls._keras_serializable = True
    if hasattr(tf.keras.utils, "register_keras_serializable"):
        cls = tf.keras.utils.register_keras_serializable()(cls)
    return cls


class TFCausalLanguageModelingLoss:
    """
    Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.

    .. note::

        Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    """

    def compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        # make sure only labels that are not equal to -100 do not affect loss
        active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100)
        reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
        labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)
        return loss_fn(labels, reduced_logits)


class TFQuestionAnsweringLoss:
    """
    Loss function suitable for quetion answering.
    """

    def compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        start_loss = loss_fn(labels["start_position"], logits[0])
        end_loss = loss_fn(labels["end_position"], logits[1])

        return (start_loss + end_loss) / 2.0


class TFTokenClassificationLoss:
    """
    Loss function suitable for token classification.

    .. note::

        Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    """

    def compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        # make sure only labels that are not equal to -100
        # are taken into account as loss
        if tf.math.reduce_any(labels == -1):
            warnings.warn("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.")
            active_loss = tf.reshape(labels, (-1,)) != -1
        else:
            active_loss = tf.reshape(labels, (-1,)) != -100
        reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
        labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)

        return loss_fn(labels, reduced_logits)


class TFSequenceClassificationLoss:
    """
    Loss function suitable for sequence classification.
    """

    def compute_loss(self, labels, logits):
        if len(shape_list(logits)) == 1 or shape_list(logits)[1] == 1:
            loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)
        else:
            loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
                from_logits=True, reduction=tf.keras.losses.Reduction.NONE
            )

        return loss_fn(labels, logits)


class TFMultipleChoiceLoss(TFSequenceClassificationLoss):
    """Loss function suitable for multiple choice tasks."""


class TFMaskedLanguageModelingLoss(TFCausalLanguageModelingLoss):
    """
    Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.

    .. note::

         Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
    """


def detect_tf_missing_unexpected_layers(model, resolved_archive_file):
    """
    Detect missing and unexpected layers.

    Args:
        model (:obj:`tf.keras.models.Model`):
            The model to load the weights into.
        resolved_archive_file (:obj:`str`):
            The location of the H5 file.

    Returns:
        Two lists, one for the missing layers, and another one for the unexpected layers.
    """
    missing_layers = []
    unexpected_layers = []

    with h5py.File(resolved_archive_file, "r") as f:
        saved_layer_names = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names"))
        model_layer_names = set(layer.name for layer in model.layers)
        missing_layers = list(model_layer_names - saved_layer_names)
        unexpected_layers = list(saved_layer_names - model_layer_names)

        for layer in model.layers:
            if layer.name in saved_layer_names:
                g = f[layer.name]
                saved_weight_names = hdf5_format.load_attributes_from_hdf5_group(g, "weight_names")
                saved_weight_names_set = set(
                    "/".join(weight_name.split("/")[2:]) for weight_name in saved_weight_names
                )
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                symbolic_weights_names = set(
                    "/".join(symbolic_weight.name.split("/")[2:]) for symbolic_weight in symbolic_weights
                )
                missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set))
                unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names))

    return missing_layers, unexpected_layers


def load_tf_weights(model, resolved_archive_file):
    """
    Load the TF weights from a H5 file.

    Args:
        model (:obj:`tf.keras.models.Model`):
            The model to load the weights into.
        resolved_archive_file (:obj:`str`):
            The location of the H5 file.
    """
    with h5py.File(resolved_archive_file, "r") as f:
        saved_layer_names = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names"))
        weight_value_tuples = []

        for layer in model.layers:
            if layer.name in saved_layer_names:
                g = f[layer.name]
                saved_weight_names = hdf5_format.load_attributes_from_hdf5_group(g, "weight_names")
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                saved_weight_names_values = {}

                for weight_name in saved_weight_names:
                    name = "/".join(weight_name.split("/")[1:])
                    saved_weight_names_values[name] = np.asarray(g[weight_name])

                for symbolic_weight in symbolic_weights:
                    splited_layers = symbolic_weight.name.split("/")[1:]
                    symbolic_weight_name = "/".join(splited_layers)

                    if symbolic_weight_name in saved_weight_names_values:
                        saved_weight_value = saved_weight_names_values[symbolic_weight_name]

                        if K.int_shape(symbolic_weight) != saved_weight_value.shape:
                            try:
                                array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
                            except AssertionError as e:
                                e.args += (K.int_shape(symbolic_weight), saved_weight_value.shape)
                                raise e
                        else:
                            array = saved_weight_value

                        weight_value_tuples.append((symbolic_weight, array))

    K.batch_set_value(weight_value_tuples)


class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
    r"""
    Base class for all TF models.

    :class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods
    for loading, downloading and saving models as well as a few methods common to all models to:

        * resize the input embeddings,
        * prune heads in the self-attention heads.

    Class attributes (overridden by derived classes):
        - **config_class** (:class:`~transformers.PretrainedConfig`) -- A subclass of
          :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
        - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
          derived classes of the same architecture adding modules on top of the base model.
        - **authorized_missing_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to ignore
          from the model when loading the model weights (and avoid unnecessary warnings).
        - **authorized_unexpected_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to ignore
          from the weights when loading the model weights (and avoid unnecessary warnings).
    """
    config_class = None
    base_model_prefix = ""
    authorized_missing_keys = None
    authorized_unexpected_keys = None

    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        """
        Dummy inputs to build the network.

        Returns:
            :obj:`Dict[str, tf.Tensor]`: The dummy inputs.
        """
        return {"input_ids": tf.constant(DUMMY_INPUTS)}

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)
        if not isinstance(config, PretrainedConfig):
            raise ValueError(
                "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
                "To create a model from a pretrained model use "
                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                    self.__class__.__name__, self.__class__.__name__
                )
            )
        # Save config and origin of the pretrained weights if given in model
        self.config = config
        self.name_or_path = config.name_or_path

    def get_input_embeddings(self) -> tf.keras.layers.Layer:
        """
        Returns the model's input embeddings.

        Returns:
            :obj:`tf.keras.layers.Layer`: A torch module mapping vocabulary to hidden states.
        """
        base_model = getattr(self, self.base_model_prefix, self)
        if base_model is not self:
            return base_model.get_input_embeddings()
        else:
            raise NotImplementedError

    def set_input_embeddings(self, value):
        """
        Set model's input embeddings.

        Args:
            value (:obj:`tf.keras.layers.Layer`):
                A module mapping vocabulary to hidden states.
        """
        base_model = getattr(self, self.base_model_prefix, self)
        if base_model is not self:
            base_model.set_input_embeddings(value)
        else:
            raise NotImplementedError

    def get_output_embeddings(self) -> tf.keras.layers.Layer:
        """
        Returns the model's output embeddings.

        Returns:
            :obj:`tf.keras.layers.Layer`: A torch module mapping hidden states to vocabulary.
        """
        return None  # Overwrite for models with output embeddings

    def resize_token_embeddings(self, new_num_tokens=None) -> tf.Variable:
        """
        Resizes input token embeddings matrix of the model if :obj:`new_num_tokens != config.vocab_size`.

        Takes care of tying weights embeddings afterwards if the model class has a :obj:`tie_weights()` method.

        Arguments:
            new_num_tokens (:obj:`int`, `optional`):
                The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
                vectors at the end. Reducing the size will remove vectors from the end. If not provided or :obj:`None`,
                just returns a pointer to the input tokens :obj:`tf.Variable` module of the model wihtout doing
                anything.

        Return:
            :obj:`tf.Variable`: Pointer to the input tokens Embeddings Module of the model.
        """
        model_embeds = self._resize_token_embeddings(new_num_tokens)
        if new_num_tokens is None:
            return model_embeds

        return model_embeds

    def _resize_token_embeddings(self, new_num_tokens):
        # get_input_embeddings and set_input_embeddings need to be implemented in base layer.
        base_model = getattr(self, self.base_model_prefix, self)
        old_embeddings = base_model.get_input_embeddings()
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
        base_model.set_input_embeddings(new_embeddings)
        # Update base model and current model config
        self.config.vocab_size = new_num_tokens
        base_model.vocab_size = new_num_tokens
        return base_model.get_input_embeddings()

    def _get_word_embeddings(self, embeddings):
        if hasattr(embeddings, "word_embeddings"):
            # TFBertEmbeddings, TFAlbertEmbeddings, TFElectraEmbeddings
            return embeddings.word_embeddings
        elif hasattr(embeddings, "weight"):
            # TFSharedEmbeddings
            return embeddings.weight
        else:
            raise ValueError("word embedding is not defined.")

    def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None) -> tf.Variable:
        """
        Build a resized Embedding Module from a provided token Embedding Module. Increasing the size will add newly
        initialized vectors at the end. Reducing the size will remove vectors from the end

        Args:
            old_embeddings (:obj:`tf.Variable`):
                Old embeddings to be resized.
            new_num_tokens (:obj:`int`, `optional`):
                New number of tokens in the embedding matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or :obj:`None`, just returns a pointer to the input tokens
                :obj:`tf.Variable`` module of the model wihtout doing anything.

        Return:
            :obj:`tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if
            :obj:`new_num_tokens` is :obj:`None`
        """
        word_embeddings = self._get_word_embeddings(old_embeddings)
        if new_num_tokens is None:
            return word_embeddings
        old_num_tokens, old_embedding_dim = word_embeddings.shape
        if old_num_tokens == new_num_tokens:
            return word_embeddings

        # initialize new embeddings
        # todo: initializer range is not always passed in config.
        init_range = getattr(self.config, "initializer_range", 0.02)
        new_embeddings = self.add_weight(
            "weight",
            shape=[new_num_tokens, old_embedding_dim],
            initializer=get_initializer(init_range),
            dtype=tf.float32,
        )
        init_weights = new_embeddings.numpy()

        # Copy token embeddings from the previous weights
        num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
        init_weights[:num_tokens_to_copy] = word_embeddings[:num_tokens_to_copy, :]
        new_embeddings.assign(init_weights)

        return new_embeddings

    def prune_heads(self, heads_to_prune):
        """
        Prunes heads of the base model.

        Arguments:
            heads_to_prune (:obj:`Dict[int, List[int]]`):
                Dictionary with keys being selected layer indices (:obj:`int`) and associated values being the list
                of heads to prune in said layer (list of :obj:`int`). 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.
        """
        raise NotImplementedError

    def save_pretrained(self, save_directory):
        """
        Save a model and its configuration file to a directory, so that it can be re-loaded using the
        :func:`~transformers.TFPreTrainedModel.from_pretrained` class method.

        Arguments:
            save_directory (:obj:`str`):
                Directory to which to save. Will be created if it doesn't exist.
        """
        if os.path.isfile(save_directory):
            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
            return
        os.makedirs(save_directory, exist_ok=True)

        # Save configuration file
        self.config.save_pretrained(save_directory)

        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(save_directory, TF2_WEIGHTS_NAME)
        self.save_weights(output_model_file)
        logger.info("Model weights saved in {}".format(output_model_file))

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.

        The warning `Weights from XXX not initialized from pretrained model` means that the weights of XXX do not come
        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
        task.

        The warning `Weights from XXX not used in YYY` means that the layer XXX is not used by YYY, therefore those
        weights are discarded.

        Parameters:
            pretrained_model_name_or_path (:obj:`str`, `optional`):
                Can be either:

                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
                      ``bert-base-uncased``.
                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
                      ``dbmdz/bert-base-german-cased``.
                    - A path to a `directory` containing model weights saved using
                      :func:`~transformersTF.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                    - A path or url to a `PyTorch state_dict save file` (e.g, ``./pt_model/pytorch_model.bin``). In
                      this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
                      as ``config`` argument. This loading path is slower than converting the PyTorch model in a
                      TensorFlow model using the provided conversion scripts and loading the TensorFlow model
                      afterwards.
                    - :obj:`None` if you are both providing the configuration and state dictionary (resp. with keyword
                      arguments ``config`` and ``state_dict``).
            model_args (sequence of positional arguments, `optional`):
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
            config (:obj:`Union[PretrainedConfig, str]`, `optional`):
                Can be either:

                    - an instance of a class derived from :class:`~transformers.PretrainedConfig`,
                    - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.

                Configuration for the model to use instead of an automatically loaded configuation. Configuration can
                be automatically loaded when:

                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
                      pretrained model).
                    - The model was saved using :func:`~transformers.TFPreTrainedModel.save_pretrained` and is reloaded
                      by suppling the save directory.
                    - The model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a
                      configuration JSON file named `config.json` is found in the directory.
            from_pt: (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Load the model weights from a PyTorch state_dict save file (see docstring of
                ``pretrained_model_name_or_path`` argument).
            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies: (:obj:`Dict[str, str], `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g.,
                :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each
                request.
            output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error
                messages.
            local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to only look at local files (e.g., not try doanloading the model).
            use_cdn(:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to use Cloudfront (a Content Delivery Network, or CDN) when searching for the model on
                our S3 (faster). Should be set to :obj:`False` for checkpoints larger than 20GB.
            mirror(:obj:`str`, `optional`, defaults to :obj:`None`):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem,
                you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please
                refer to the mirror site for more information.
            kwargs (remaining dictionary of keyword arguments, `optional`):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                :obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
                automatically loaded:

                    - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
                      underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
                      initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
                      ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
                      with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
                      attribute will be passed to the underlying model's ``__init__`` function.

        Examples::

            >>> from transformers import BertConfig, TFBertModel
            >>> # Download model and configuration from S3 and cache.
            >>> model = TFBertModel.from_pretrained('bert-base-uncased')
            >>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
            >>> model = TFBertModel.from_pretrained('./test/saved_model/')
            >>> # Update configuration during loading.
            >>> model = TFBertModel.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> assert model.config.output_attentions == True
            >>> # Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).
            >>> config = BertConfig.from_json_file('./pt_model/my_pt_model_config.json')
            >>> model = TFBertModel.from_pretrained('./pt_model/my_pytorch_model.bin', from_pt=True, config=config)

        """
        config = kwargs.pop("config", None)
        cache_dir = kwargs.pop("cache_dir", None)
        from_pt = kwargs.pop("from_pt", False)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        output_loading_info = kwargs.pop("output_loading_info", False)
        local_files_only = kwargs.pop("local_files_only", False)
        use_cdn = kwargs.pop("use_cdn", True)
        mirror = kwargs.pop("mirror", None)

        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                *model_args,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                **kwargs,
            )
        else:
            model_kwargs = kwargs

        # Load model
        if pretrained_model_name_or_path is not None:
            if os.path.isdir(pretrained_model_name_or_path):
                if os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
                    # Load from a TF 2.0 checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
                elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
                    # Load from a PyTorch checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
                else:
                    raise EnvironmentError(
                        "Error no file named {} found in directory {} or `from_pt` set to False".format(
                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME], pretrained_model_name_or_path
                        )
                    )
            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
                archive_file = pretrained_model_name_or_path
            elif os.path.isfile(pretrained_model_name_or_path + ".index"):
                archive_file = pretrained_model_name_or_path + ".index"
            else:
                archive_file = hf_bucket_url(
                    pretrained_model_name_or_path,
                    filename=(WEIGHTS_NAME if from_pt else TF2_WEIGHTS_NAME),
                    use_cdn=use_cdn,
                    mirror=mirror,
                )

            try:
                # Load from URL or cache if already cached
                resolved_archive_file = cached_path(
                    archive_file,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    resume_download=resume_download,
                    local_files_only=local_files_only,
                )
                if resolved_archive_file is None:
                    raise EnvironmentError
            except EnvironmentError:
                msg = (
                    f"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                    f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
                    f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {TF2_WEIGHTS_NAME}, {WEIGHTS_NAME}.\n\n"
                )
                raise EnvironmentError(msg)
            if resolved_archive_file == archive_file:
                logger.info("loading weights file {}".format(archive_file))
            else:
                logger.info("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
        else:
            resolved_archive_file = None

        config.name_or_path = pretrained_model_name_or_path

        # Instantiate model.
        model = cls(config, *model_args, **model_kwargs)

        if from_pt:
            from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model

            # Load from a PyTorch checkpoint
            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)

        model(model.dummy_inputs, training=False)  # build the network with dummy inputs

        assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)
        # 'by_name' allow us to do transfer learning by skipping/adding layers
        # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
        try:
            load_tf_weights(model, resolved_archive_file)
        except OSError:
            raise OSError(
                "Unable to load weights from h5 file. "
                "If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. "
            )

        model(model.dummy_inputs, training=False)  # Make sure restore ops are run

        missing_keys, unexpected_keys = detect_tf_missing_unexpected_layers(model, resolved_archive_file)

        if cls.authorized_missing_keys is not None:
            for pat in cls.authorized_missing_keys:
                missing_keys = [k for k in missing_keys if re.search(pat, k) is None]

        if cls.authorized_unexpected_keys is not None:
            for pat in cls.authorized_unexpected_keys:
                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some layers from the model checkpoint at {pretrained_model_name_or_path} were not used when "
                f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
                f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
                f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n"
                f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
                f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
            )
        else:
            logger.warning(f"All model checkpoint layers were used when initializing {model.__class__.__name__}.\n")

        if len(missing_keys) > 0:
            logger.warning(
                f"Some layers of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
                f"and are newly initialized: {missing_keys}\n"
                f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        else:
            logger.warning(
                f"All the layers of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
                f"If your task is similar to the task the model of the checkpoint was trained on, "
                f"you can already use {model.__class__.__name__} for predictions without further training."
            )

        if output_loading_info:
            loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys}

            return model, loading_info

        return model


class TFConv1D(tf.keras.layers.Layer):
    """
    1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).

    Basically works like a linear layer but the weights are transposed.

    Args:
        nf (:obj:`int`):
            The number of output features.
        nx (:obj:`int`):
            The number of input features.
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation to use to initialize the weights.
        kwargs:
            Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
    """

    def __init__(self, nf, nx, initializer_range=0.02, **kwargs):
        super().__init__(**kwargs)
        self.nf = nf
        self.nx = nx
        self.initializer_range = initializer_range

    def build(self, input_shape):
        self.weight = self.add_weight(
            "weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range)
        )
        self.bias = self.add_weight("bias", shape=[1, self.nf], initializer=tf.zeros_initializer())

    def call(self, x):
        bz, sl = shape_list(x)[:2]

        x = tf.reshape(x, [-1, self.nx])
        x = tf.matmul(x, self.weight) + self.bias

        x = tf.reshape(x, [bz, sl, self.nf])

        return x


class TFSharedEmbeddings(tf.keras.layers.Layer):
    r"""
    Construct shared token embeddings.

    The weights of the embedding layer is usually shared with the weights of the linear decoder when doing
    language modeling.

    Args:
        vocab_size (:obj:`int`):
            The size of the vocabular, e.g., the number of unique tokens.
        hidden_size (:obj:`int`):
            The size of the embedding vectors.
        initializer_range (:obj:`float`, `optional`):
            The standard deviation to use when initializing the weights. If no value is provided, it will default to
            :math:`1/\sqrt{hidden\_size}`.
        kwargs:
            Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
    """

    def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.initializer_range = hidden_size ** -0.5 if initializer_range is None else initializer_range

    def build(self, input_shape):
        """Build shared token embedding layer
        Shared weights logic adapted from
            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
        """
        self.weight = self.add_weight(
            "weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range)
        )
        super().build(input_shape)

    def get_config(self):
        config = {
            "vocab_size": self.vocab_size,
            "hidden_size": self.hidden_size,
            "initializer_range": self.initializer_range,
        }
        base_config = super().get_config()

        return dict(list(base_config.items()) + list(config.items()))

    def call(self, inputs: tf.Tensor, mode: str = "embedding") -> tf.Tensor:
        """
        Get token embeddings of inputs or decode final hidden state.

        Args:
            inputs (:obj:`tf.Tensor`):
                In embedding mode, should be an int64 tensor with shape :obj:`[batch_size, length]`.

                In linear mode, should be a float tensor with shape :obj:`[batch_size, length, hidden_size]`.
            mode (:obj:`str`, defaults to :obj:`"embedding"`):
               A valid value is either :obj:`"embedding"` or :obj:`"linear"`, the first one indicates that the layer
               should be used as an embedding layer, the second one that the layer should be used as a linear decoder.

        Returns:
            :obj:`tf.Tensor`:
            In embedding mode, the output is a float32  embedding tensor, with shape
            :obj:`[batch_size, length, embedding_size]`.

            In linear mode, the ouput is a float32 with shape :obj:`[batch_size, length, vocab_size]`.

        Raises:
            ValueError: if :obj:`mode` is not valid.

        Shared weights logic is adapted from
        `here <https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24>`__.
        """
        if mode == "embedding":
            return self._embedding(inputs)
        elif mode == "linear":
            return self._linear(inputs)
        else:
            raise ValueError("mode {} is not valid.".format(mode))

    def _embedding(self, input_ids):
        """Applies embedding based on inputs tensor."""
        return tf.gather(self.weight, input_ids)

    def _linear(self, inputs):
        """
        Computes logits by running inputs through a linear layer.

        Args:
            inputs: A float32 tensor with shape [..., hidden_size]

        Returns:
            float32 tensor with shape [..., vocab_size].
        """
        first_dims = shape_list(inputs)[:-1]
        x = tf.reshape(inputs, [-1, self.hidden_size])
        logits = tf.matmul(x, self.weight, transpose_b=True)

        return tf.reshape(logits, first_dims + [self.vocab_size])


class TFSequenceSummary(tf.keras.layers.Layer):
    """
    Compute a single vector summary of a sequence hidden states.

    Args:
        config (:class:`~transformers.PretrainedConfig`):
            The config used by the model. Relevant arguments in the config class of the model are (refer to the
            actual config class of your model for the default values it uses):

            - **summary_type** (:obj:`str`) -- The method to use to make this summary. Accepted values are:

                - :obj:`"last"` -- Take the last token hidden state (like XLNet)
                - :obj:`"first"` -- Take the first token hidden state (like Bert)
                - :obj:`"mean"` -- Take the mean of all tokens hidden states
                - :obj:`"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
                - :obj:`"attn"` -- Not implemented now, use multi-head attention

            - **summary_use_proj** (:obj:`bool`) -- Add a projection after the vector extraction.
            - **summary_proj_to_labels** (:obj:`bool`) -- If :obj:`True`, the projection outputs to
              :obj:`config.num_labels` classes (otherwise to :obj:`config.hidden_size`).
            - **summary_activation**  (:obj:`Optional[str]`) -- Set to :obj:`"tanh"` to add a tanh activation to the
              output, another string or :obj:`None` will add no activation.
            - **summary_first_dropout** (:obj:`float`) -- Optional dropout probability before the projection and
              activation.
            - **summary_last_dropout** (:obj:`float`)-- Optional dropout probability after the projection and
              activation.

        initializer_range (:obj:`float`, defaults to 0.02): The standard deviation to use to initialize the weights.
        kwargs:
            Additional keyword arguments passed along to the :obj:`__init__` of :obj:`tf.keras.layers.Layer`.
    """

    def __init__(self, config: PretrainedConfig, initializer_range: float = 0.02, **kwargs):
        super().__init__(**kwargs)

        self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last"
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
        if self.has_summary:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = tf.keras.layers.Dense(
                num_classes, kernel_initializer=get_initializer(initializer_range), name="summary"
            )

        self.has_activation = hasattr(config, "summary_activation") and config.summary_activation == "tanh"
        if self.has_activation:
            self.activation = tf.keras.activations.tanh

        self.has_first_dropout = hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0
        if self.has_first_dropout:
            self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)

        self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0
        if self.has_last_dropout:
            self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)

    def call(self, inputs, cls_index=None, training=False):
        if not isinstance(inputs, (dict, tuple, list)):
            hidden_states = inputs
        elif isinstance(inputs, (tuple, list)):
            hidden_states = inputs[0]
            cls_index = inputs[1] if len(inputs) > 1 else None
            assert len(inputs) <= 2, "Too many inputs."
        else:
            hidden_states = inputs.get("hidden_states")
            cls_index = inputs.get("cls_index", None)

        if self.summary_type == "last":
            output = hidden_states[:, -1]
        elif self.summary_type == "first":
            output = hidden_states[:, 0]
        elif self.summary_type == "mean":
            output = tf.reduce_mean(hidden_states, axis=1)
        elif self.summary_type == "cls_index":
            hidden_shape = shape_list(hidden_states)  # e.g. [batch, num choices, seq length, hidden dims]
            if cls_index is None:
                cls_index = tf.fill(
                    hidden_shape[:-2], hidden_shape[-2] - 1
                )  # A tensor full of shape [batch] or [batch, num choices] full of sequence length
            cls_shape = shape_list(cls_index)
            if len(cls_shape) <= len(hidden_shape) - 2:
                cls_index = cls_index[..., tf.newaxis]
            # else:
            # cls_index = cls_index[..., tf.newaxis]
            # cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
            # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
            output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
            output = tf.squeeze(
                output, axis=len(hidden_shape) - 2
            )  # shape of output: (batch, num choices, hidden_size)
        elif self.summary_type == "attn":
            raise NotImplementedError

        if self.has_first_dropout:
            output = self.first_dropout(output, training=training)

        if self.has_summary:
            output = self.summary(output)

        if self.has_activation:
            output = self.activation(output)

        if self.has_last_dropout:
            output = self.last_dropout(output, training=training)

        return output


def shape_list(x: tf.Tensor) -> List[int]:
    """
    Deal with dynamic shape in tensorflow cleanly.

    Args:
        x (:obj:`tf.Tensor`): The tensor we want the shape of.

    Returns:
        :obj:`List[int]`: The shape of the tensor as a list.
    """
    static = x.shape.as_list()
    dynamic = tf.shape(x)
    return [dynamic[i] if s is None else s for i, s in enumerate(static)]


def get_initializer(initializer_range: float = 0.02) -> tf.initializers.TruncatedNormal:
    """
    Creates a :obj:`tf.initializers.TruncatedNormal` with the given range.

    Args:
        initializer_range (`float`, defaults to 0.02): Standard deviation of the initializer range.

    Returns:
        :obj:`tf.initializers.TruncatedNormal`: The truncated normal initializer.
    """
    return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)


def cast_bool_to_primitive(bool_variable: Union[tf.Tensor, bool], default_tensor_to_true=False) -> bool:
    """
    Function arguments can be inserted as boolean tensor and bool variables to cope with Keras serialization we need to
    cast the bool argumnets (like :obj:`output_attentions` for instance) to correct boolean if it is a tensor.

    Args:
        bool_variable (:obj:`Union[tf.Tensor, bool]`):
            The variable to convert to a boolean.
        default_tensor_to_true (:obj:`bool`, `optional`, defaults to `False`):
            The default value to use in case the tensor has no numpy attribute.

    Returns:
        :obj:`bool`: The converted value.
    """
    # if bool variable is tensor and has numpy value
    if tf.is_tensor(bool_variable):
        if hasattr(bool_variable, "numpy"):
            return bool(bool_variable.numpy())
        elif default_tensor_to_true:
            return True

    # else variable is bool
    return bool_variable
