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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" TF 2.0 CamemBERT model."""

from ...utils import add_start_docstrings, logging
from ..roberta.modeling_tf_roberta import (
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForMultipleChoice,
    TFRobertaForQuestionAnswering,
    TFRobertaForSequenceClassification,
    TFRobertaForTokenClassification,
    TFRobertaModel,
)
from .configuration_camembert import CamembertConfig


logger = logging.get_logger(__name__)

TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    # See all CamemBERT models at https://huggingface.co/models?filter=camembert
]


CAMEMBERT_START_DOCSTRING = r"""

    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

    <Tip>

    TF 2.0 models accepts two formats as inputs:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
    first positional argument :

    - a single Tensor with `input_ids` only and nothing else: `model(inputs_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    </Tip>

    Parameters:
        config ([`CamembertConfig`]): Model configuration class with all the parameters of the
            model. Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertModel(TFRobertaModel):
    """
    This class overrides [`TFRobertaModel`]. Please check the superclass for the appropriate documentation alongside
    usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """CamemBERT Model with a `language modeling` head on top.""",
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForMaskedLM(TFRobertaForMaskedLM):
    """
    This class overrides [`TFRobertaForMaskedLM`]. Please check the superclass for the appropriate documentation
    alongside usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """
    CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForSequenceClassification(TFRobertaForSequenceClassification):
    """
    This class overrides [`TFRobertaForSequenceClassification`]. Please check the superclass for the appropriate
    documentation alongside usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """
    CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
    for Named-Entity-Recognition (NER) tasks.
    """,
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForTokenClassification(TFRobertaForTokenClassification):
    """
    This class overrides [`TFRobertaForTokenClassification`]. Please check the superclass for the appropriate
    documentation alongside usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """
    CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForMultipleChoice(TFRobertaForMultipleChoice):
    """
    This class overrides [`TFRobertaForMultipleChoice`]. Please check the superclass for the appropriate documentation
    alongside usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """
    CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    CAMEMBERT_START_DOCSTRING,
)
class TFCamembertForQuestionAnswering(TFRobertaForQuestionAnswering):
    """
    This class overrides [`TFRobertaForQuestionAnswering`]. Please check the superclass for the appropriate
    documentation alongside usage examples.
    """

    config_class = CamembertConfig


@add_start_docstrings(
    """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING
)
class TFCamembertForCausalLM(TFRobertaForCausalLM):
    """
    This class overrides [`TFRobertaForCausalLM`]. Please check the superclass for the appropriate documentation
    alongside usage examples.
    """

    config_class = CamembertConfig
