# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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 2.0 DistilBERT model
"""


import tensorflow as tf

from .activations_tf import get_tf_activation
from .configuration_distilbert import DistilBertConfig
from .file_utils import (
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from .modeling_tf_outputs import (
    TFBaseModelOutput,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from .modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFSharedEmbeddings,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    shape_list,
)
from .tokenization_utils import BatchEncoding
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "DistilBertConfig"
_TOKENIZER_FOR_DOC = "DistilBertTokenizer"

TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "distilbert-base-uncased",
    "distilbert-base-uncased-distilled-squad",
    "distilbert-base-cased",
    "distilbert-base-cased-distilled-squad",
    "distilbert-base-multilingual-cased",
    "distilbert-base-uncased-finetuned-sst-2-english",
    # See all DistilBERT models at https://huggingface.co/models?filter=distilbert
]


class TFEmbeddings(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size
        self.dim = config.dim
        self.initializer_range = config.initializer_range
        self.word_embeddings = TFSharedEmbeddings(
            config.vocab_size, config.dim, initializer_range=config.initializer_range, name="word_embeddings"
        )  # padding_idx=0)
        self.position_embeddings = tf.keras.layers.Embedding(
            config.max_position_embeddings,
            config.dim,
            embeddings_initializer=get_initializer(config.initializer_range),
            name="position_embeddings",
        )

        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.dropout)

    def build(self, input_shape):
        """Build shared word embedding layer """
        with tf.name_scope("word_embeddings"):
            # Create and initialize weights. The random normal initializer was chosen
            # arbitrarily, and works well.
            self.word_embeddings = self.add_weight(
                "weight", shape=[self.vocab_size, self.dim], initializer=get_initializer(self.initializer_range)
            )
        super().build(input_shape)

    def call(self, input_ids=None, position_ids=None, inputs_embeds=None, mode="embedding", training=False):
        """
        Get token embeddings of inputs.

        Args:
            inputs: list of two int64 tensors with shape [batch_size, length]: (input_ids, position_ids)
            mode: string, a valid value is one of "embedding" and "linear".

        Returns:
            outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
            embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
            vocab_size].

        Raises:
            ValueError: if mode is not valid.

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

    def _embedding(self, input_ids, position_ids, inputs_embeds, training=False):
        """
        Parameters:
            input_ids: tf.Tensor(bs, max_seq_length) The token ids to embed.

        Returns:
            tf.Tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type embeddings)
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            seq_length = shape_list(input_ids)[1]
        else:
            seq_length = shape_list(inputs_embeds)[1]

        if position_ids is None:
            position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]

        if inputs_embeds is None:
            inputs_embeds = tf.gather(self.word_embeddings, input_ids)
        position_embeddings = tf.cast(
            self.position_embeddings(position_ids), inputs_embeds.dtype
        )  # (bs, max_seq_length, dim)

        embeddings = inputs_embeds + position_embeddings  # (bs, max_seq_length, dim)
        embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
        embeddings = self.dropout(embeddings, training=training)  # (bs, max_seq_length, dim)
        return embeddings

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

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

        Returns:
            float32 tensor with shape [batch_size, length, vocab_size].
        """
        batch_size = shape_list(inputs)[0]
        length = shape_list(inputs)[1]

        x = tf.reshape(inputs, [-1, self.dim])
        logits = tf.matmul(x, self.word_embeddings, transpose_b=True)

        return tf.reshape(logits, [batch_size, length, self.vocab_size])


class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
        self.output_attentions = config.output_attentions

        assert self.dim % self.n_heads == 0, f"Hidden size {self.dim} not dividable by number of heads {self.n_heads}"

        self.q_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="q_lin"
        )
        self.k_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="k_lin"
        )
        self.v_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="v_lin"
        )
        self.out_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="out_lin"
        )

        self.pruned_heads = set()

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(self, query, key, value, mask, head_mask, output_attentions, training=False):
        """
        Parameters:
            query: tf.Tensor(bs, seq_length, dim)
            key: tf.Tensor(bs, seq_length, dim)
            value: tf.Tensor(bs, seq_length, dim)
            mask: tf.Tensor(bs, seq_length)

        Returns:
            weights: tf.Tensor(bs, n_heads, seq_length, seq_length) Attention weights context: tf.Tensor(bs,
            seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
        """
        bs, q_length, dim = shape_list(query)
        k_length = shape_list(key)[1]
        # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
        # assert key.size() == value.size()
        dim_per_head = tf.math.divide(self.dim, self.n_heads)
        dim_per_head = tf.cast(dim_per_head, dtype=tf.int32)
        mask_reshape = [bs, 1, 1, k_length]

        def shape(x):
            """ separate heads """
            return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))

        def unshape(x):
            """ group heads """
            return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))

        q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
        k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
        v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)
        q = tf.cast(q, dtype=tf.float32)
        q = tf.multiply(q, tf.math.rsqrt(tf.cast(dim_per_head, dtype=tf.float32)))
        k = tf.cast(k, dtype=q.dtype)
        scores = tf.matmul(q, k, transpose_b=True)  # (bs, n_heads, q_length, k_length)
        mask = tf.reshape(mask, mask_reshape)  # (bs, n_heads, qlen, klen)
        # scores.masked_fill_(mask, -float('inf'))            # (bs, n_heads, q_length, k_length)

        mask = tf.cast(mask, dtype=scores.dtype)
        scores = scores - 1e30 * (1.0 - mask)
        weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
        weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)

        # Mask heads if we want to
        if head_mask is not None:
            weights = weights * head_mask

        context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
        context = unshape(context)  # (bs, q_length, dim)
        context = self.out_lin(context)  # (bs, q_length, dim)

        if output_attentions:
            return (context, weights)
        else:
            return (context,)


class TFFFN(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.lin1 = tf.keras.layers.Dense(
            config.hidden_dim, kernel_initializer=get_initializer(config.initializer_range), name="lin1"
        )
        self.lin2 = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="lin2"
        )
        assert config.activation in ["relu", "gelu"], "activation ({}) must be in ['relu', 'gelu']".format(
            config.activation
        )
        self.activation = get_tf_activation(config.activation)

    def call(self, input, training=False):
        x = self.lin1(input)
        x = self.activation(x)
        x = self.lin2(x)
        x = self.dropout(x, training=training)
        return x


class TFTransformerBlock(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.hidden_dim = config.hidden_dim
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.activation = config.activation
        self.output_attentions = config.output_attentions

        assert (
            config.dim % config.n_heads == 0
        ), f"Hidden size {config.dim} not dividable by number of heads {config.n_heads}"

        self.attention = TFMultiHeadSelfAttention(config, name="attention")
        self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")

        self.ffn = TFFFN(config, name="ffn")
        self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")

    def call(self, x, attn_mask, head_mask, output_attentions, training=False):  # removed: src_enc=None, src_len=None
        """
        Parameters:
            x: tf.Tensor(bs, seq_length, dim)
            attn_mask: tf.Tensor(bs, seq_length)

        Outputs: sa_weights: tf.Tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output:
        tf.Tensor(bs, seq_length, dim) The output of the transformer block contextualization.
        """
        # Self-Attention
        sa_output = self.attention(x, x, x, attn_mask, head_mask, output_attentions, training=training)
        if output_attentions:
            sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
        else:  # To handle these `output_attentions` or `output_hidden_states` cases returning tuples
            # assert type(sa_output) == tuple
            sa_output = sa_output[0]
        sa_output = self.sa_layer_norm(sa_output + x)  # (bs, seq_length, dim)

        # Feed Forward Network
        ffn_output = self.ffn(sa_output, training=training)  # (bs, seq_length, dim)
        ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)

        output = (ffn_output,)
        if output_attentions:
            output = (sa_weights,) + output
        return output


class TFTransformer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.n_layers = config.n_layers
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions

        self.layer = [TFTransformerBlock(config, name="layer_._{}".format(i)) for i in range(config.n_layers)]

    def call(self, x, attn_mask, head_mask, output_attentions, output_hidden_states, return_dict, training=False):
        # docstyle-ignore
        """
        Parameters:
            x: tf.Tensor(bs, seq_length, dim) Input sequence embedded.
            attn_mask: tf.Tensor(bs, seq_length) Attention mask on the sequence.

        Returns:
            hidden_state: tf.Tensor(bs, seq_length, dim)
                Sequence of hidden states in the last (top) layer
            all_hidden_states: Tuple[tf.Tensor(bs, seq_length, dim)]
                Tuple of length n_layers with the hidden states from each layer.
                Optional: only if output_hidden_states=True
            all_attentions: Tuple[tf.Tensor(bs, n_heads, seq_length, seq_length)]
                Tuple of length n_layers with the attention weights from each layer
                Optional: only if output_attentions=True
        """
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        hidden_state = x
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_state,)

            layer_outputs = layer_module(hidden_state, attn_mask, head_mask[i], output_attentions, training=training)
            hidden_state = layer_outputs[-1]

            if output_attentions:
                assert len(layer_outputs) == 2
                attentions = layer_outputs[0]
                all_attentions = all_attentions + (attentions,)
            else:
                assert len(layer_outputs) == 1, f"Incorrect number of outputs {len(layer_outputs)} instead of 1"

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_state,)

        if not return_dict:
            return tuple(v for v in [hidden_state, all_hidden_states, all_attentions] if v is not None)
        return TFBaseModelOutput(
            last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=all_attentions
        )


@keras_serializable
class TFDistilBertMainLayer(tf.keras.layers.Layer):
    config_class = DistilBertConfig

    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.num_hidden_layers = config.num_hidden_layers
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.return_dict = config.use_return_dict

        self.embeddings = TFEmbeddings(config, name="embeddings")  # Embeddings
        self.transformer = TFTransformer(config, name="transformer")  # Encoder

    def get_input_embeddings(self):
        return self.embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value
        self.embeddings.vocab_size = value.shape[0]

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

    def call(
        self,
        inputs,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        training=False,
    ):
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            head_mask = inputs[2] if len(inputs) > 2 else head_mask
            inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
            output_attentions = inputs[4] if len(inputs) > 4 else output_attentions
            output_hidden_states = inputs[5] if len(inputs) > 5 else output_hidden_states
            return_dict = inputs[6] if len(inputs) > 6 else return_dict
            assert len(inputs) <= 7, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            return_dict = inputs.get("return_dict", return_dict)
            assert len(inputs) <= 7, "Too many inputs."
        else:
            input_ids = inputs

        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
        return_dict = return_dict if return_dict is not None else self.return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.ones(input_shape)  # (bs, seq_length)

        attention_mask = tf.cast(attention_mask, dtype=tf.float32)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        if head_mask is not None:
            raise NotImplementedError
        else:

            head_mask = [None] * self.num_hidden_layers

        embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds)  # (bs, seq_length, dim)
        tfmr_output = self.transformer(
            embedding_output,
            attention_mask,
            head_mask,
            output_attentions,
            output_hidden_states,
            return_dict,
            training=training,
        )

        return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)


# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DistilBertConfig
    base_model_prefix = "distilbert"


DISTILBERT_START_DOCSTRING = r"""

    This model inherits from :class:`~transformers.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.

    .. note::

        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 :meth:`tf.keras.Model.fit` method which currently requires having all
        the tensors in the first argument of the model call function: :obj:`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 :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
          :obj:`model([input_ids, attention_mask])`
        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
          :obj:`model({"input_ids": input_ids})`

    Parameters:
        config (:class:`~transformers.DistilBertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
            weights.
"""

DISTILBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`~transformers.DistilBertTokenizer`. See
            :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            `What are attention masks? <../glossary.html#attention-mask>`__
        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
        training (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""


@add_start_docstrings(
    "The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertModel(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")  # Embeddings

    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(self, inputs, **kwargs):
        outputs = self.distilbert(inputs, **kwargs)
        return outputs


class TFDistilBertLMHead(tf.keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.input_embeddings = input_embeddings

    def build(self, input_shape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
        super().build(input_shape)

    def call(self, hidden_states):
        hidden_states = self.input_embeddings(hidden_states, mode="linear")
        hidden_states = hidden_states + self.bias
        return hidden_states


@add_start_docstrings(
    """DistilBert Model with a `masked language modeling` head on top. """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModelingLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.vocab_size = config.vocab_size

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.vocab_transform = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="vocab_transform"
        )
        self.act = get_tf_activation("gelu")
        self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
        self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")

    def get_output_embeddings(self):
        return self.vocab_projector.input_embeddings

    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
        """
        return_dict = return_dict if return_dict is not None else self.distilbert.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[7] if len(inputs) > 7 else labels
            if len(inputs) > 7:
                inputs = inputs[:7]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        distilbert_output = self.distilbert(
            inputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        hidden_states = distilbert_output[0]  # (bs, seq_length, dim)
        prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
        prediction_logits = self.act(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_projector(prediction_logits)

        loss = None if labels is None else self.compute_loss(labels, prediction_logits)

        if not return_dict:
            output = (prediction_logits,) + distilbert_output[1:]
            return ((loss,) + output) if loss is not None else output

        return TFMaskedLMOutput(
            loss=loss,
            logits=prediction_logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )


@add_start_docstrings(
    """
    DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.pre_classifier = tf.keras.layers.Dense(
            config.dim,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="relu",
            name="pre_classifier",
        )
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )
        self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)

    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ...,
            config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.distilbert.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[7] if len(inputs) > 7 else labels
            if len(inputs) > 7:
                inputs = inputs[:7]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        distilbert_output = self.distilbert(
            inputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = self.dropout(pooled_output, training=training)  # (bs, dim)
        logits = self.classifier(pooled_output)  # (bs, dim)

        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + distilbert_output[1:]
            return ((loss,) + output) if loss is not None else output

        return TFSequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )


@add_start_docstrings(
    """
    DistilBert 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.
    """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.
        """
        return_dict = return_dict if return_dict is not None else self.distilbert.return_dict
        if isinstance(inputs, (tuple, list)):
            labels = inputs[7] if len(inputs) > 7 else labels
            if len(inputs) > 7:
                inputs = inputs[:7]
        elif isinstance(inputs, (dict, BatchEncoding)):
            labels = inputs.pop("labels", labels)

        outputs = self.distilbert(
            inputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output, training=training)
        logits = self.classifier(sequence_output)

        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TFTokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DistilBert 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.
    """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoiceLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
        self.pre_classifier = tf.keras.layers.Dense(
            config.dim,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="relu",
            name="pre_classifier",
        )
        self.classifier = tf.keras.layers.Dense(
            1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @property
    def dummy_inputs(self):
        """
        Dummy inputs to build the network.

        Returns:
            tf.Tensor with dummy inputs
        """
        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}

    @add_start_docstrings_to_model_forward(
        DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
            num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
            :obj:`input_ids` above)
        """
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            head_mask = inputs[2] if len(inputs) > 2 else head_mask
            inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
            output_attentions = inputs[4] if len(inputs) > 4 else output_attentions
            output_hidden_states = inputs[5] if len(inputs) > 5 else output_hidden_states
            return_dict = inputs[6] if len(inputs) > 6 else return_dict
            labels = inputs[7] if len(inputs) > 7 else labels
            assert len(inputs) <= 8, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            return_dict = inputs.get("return_dict", return_dict)
            labels = inputs.get("labels", labels)
            assert len(inputs) <= 8, "Too many inputs."
        else:
            input_ids = inputs
        return_dict = return_dict if return_dict is not None else self.distilbert.return_dict

        if input_ids is not None:
            num_choices = shape_list(input_ids)[1]
            seq_length = shape_list(input_ids)[2]
        else:
            num_choices = shape_list(inputs_embeds)[1]
            seq_length = shape_list(inputs_embeds)[2]

        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_inputs_embeds = (
            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
            if inputs_embeds is not None
            else None
        )
        distilbert_output = self.distilbert(
            flat_input_ids,
            flat_attention_mask,
            head_mask,
            flat_inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = self.dropout(pooled_output, training=training)  # (bs, dim)
        logits = self.classifier(pooled_output)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))

        loss = None if labels is None else self.compute_loss(labels, reshaped_logits)

        if not return_dict:
            output = (reshaped_logits,) + distilbert_output[1:]
            return ((loss,) + output) if loss is not None else output

        return TFMultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )


@add_start_docstrings(
    """
    DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.qa_outputs = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
        )
        assert config.num_labels == 2, f"Incorrect number of labels {config.num_labels} instead of 2"
        self.dropout = tf.keras.layers.Dropout(config.qa_dropout)

    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="distilbert-base-uncased",
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        inputs=None,
        attention_mask=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        start_positions=None,
        end_positions=None,
        training=False,
    ):
        r"""
        start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.distilbert.return_dict
        if isinstance(inputs, (tuple, list)):
            start_positions = inputs[7] if len(inputs) > 7 else start_positions
            end_positions = inputs[8] if len(inputs) > 8 else end_positions
            if len(inputs) > 7:
                inputs = inputs[:7]
        elif isinstance(inputs, (dict, BatchEncoding)):
            start_positions = inputs.pop("start_positions", start_positions)
            end_positions = inputs.pop("end_positions", start_positions)

        distilbert_output = self.distilbert(
            inputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)
        hidden_states = self.dropout(hidden_states, training=training)  # (bs, max_query_len, dim)
        logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)

        loss = None
        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.compute_loss(labels, (start_logits, end_logits))

        if not return_dict:
            output = (start_logits, end_logits) + distilbert_output[1:]
            return ((loss,) + output) if loss is not None else output

        return TFQuestionAnsweringModelOutput(
            loss=loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=distilbert_output.hidden_states,
            attentions=distilbert_output.attentions,
        )
