# coding=utf-8
# Copyright 2018 Salesforce and 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 2.0 CTRL model."""


import logging

import numpy as np
import tensorflow as tf

from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, keras_serializable, shape_list
from .tokenization_utils import BatchEncoding


logger = logging.getLogger(__name__)

TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP = {"ctrl": "https://s3.amazonaws.com/models.huggingface.co/bert/ctrl-tf_model.h5"}


def angle_defn(pos, i, d_model_size):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model_size))
    return pos * angle_rates


def positional_encoding(position, d_model_size):
    # create the sinusoidal pattern for the positional encoding
    angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)

    sines = np.sin(angle_rads[:, 0::2])
    cosines = np.cos(angle_rads[:, 1::2])

    # pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=tf.float32)
    pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1), dtype=tf.float32)
    return pos_encoding


def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
    # calculate attention
    matmul_qk = tf.matmul(q, k, transpose_b=True)

    dk = tf.cast(shape_list(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

    if mask is not None:
        scaled_attention_logits += mask * -1e4

    if attention_mask is not None:
        # Apply the attention mask
        scaled_attention_logits = scaled_attention_logits + attention_mask

    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)

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

    output = tf.matmul(attention_weights, v)

    return output, attention_weights


class TFMultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model_size, num_heads, output_attentions=False, **kwargs):
        super().__init__(**kwargs)
        self.output_attentions = output_attentions
        self.num_heads = num_heads
        self.d_model_size = d_model_size

        self.depth = int(d_model_size / self.num_heads)

        self.Wq = tf.keras.layers.Dense(d_model_size, name="Wq")
        self.Wk = tf.keras.layers.Dense(d_model_size, name="Wk")
        self.Wv = tf.keras.layers.Dense(d_model_size, name="Wv")

        self.dense = tf.keras.layers.Dense(d_model_size, name="dense")

    def split_into_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, inputs, training=False):
        v, k, q, mask, layer_past, attention_mask, head_mask, use_cache = inputs
        batch_size = shape_list(q)[0]

        q = self.Wq(q)
        k = self.Wk(k)
        v = self.Wv(v)

        q = self.split_into_heads(q, batch_size)
        k = self.split_into_heads(k, batch_size)
        v = self.split_into_heads(v, batch_size)

        if layer_past is not None:
            past_key, past_value = tf.unstack(layer_past, axis=0)
            k = tf.concat((past_key, k), axis=-2)
            v = tf.concat((past_value, v), axis=-2)

        # to cope with keras serialization
        # we need to cast `use_cache` to correct bool
        # if it is a tensor
        if tf.is_tensor(use_cache):
            if hasattr(use_cache, "numpy"):
                use_cache = bool(use_cache.numpy())
            else:
                use_cache = True

        if use_cache is True:
            present = tf.stack((k, v), axis=0)
        else:
            present = (None,)

        output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
        scaled_attention = tf.transpose(output[0], perm=[0, 2, 1, 3])
        attn = output[1]
        original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
        output = self.dense(original_size_attention)

        outputs = (output, present)
        if self.output_attentions:
            outputs = outputs + (attn,)
        return outputs


def point_wise_feed_forward_network(d_model_size, dff, name=""):
    return tf.keras.Sequential(
        [tf.keras.layers.Dense(dff, activation="relu", name="0"), tf.keras.layers.Dense(d_model_size, name="2")],
        name="ffn",
    )


class TFEncoderLayer(tf.keras.layers.Layer):
    def __init__(
        self, d_model_size, num_heads, dff, rate=0.1, layer_norm_epsilon=1e-6, output_attentions=False, **kwargs
    ):
        super().__init__(**kwargs)

        self.multi_head_attention = TFMultiHeadAttention(
            d_model_size, num_heads, output_attentions, name="multi_head_attention"
        )
        self.ffn = point_wise_feed_forward_network(d_model_size, dff, name="ffn")

        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm1")
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm2")

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, inputs, training=False):
        x, mask, layer_past, attention_mask, head_mask, use_cache = inputs
        normed = self.layernorm1(x)
        attn_outputs = self.multi_head_attention(
            [normed, normed, normed, mask, layer_past, attention_mask, head_mask, use_cache], training=training
        )
        attn_output = attn_outputs[0]
        attn_output = self.dropout1(attn_output, training=training)
        out1 = x + attn_output

        out2 = self.layernorm2(out1)
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = out1 + ffn_output

        outputs = (out2,) + attn_outputs[1:]
        return outputs


@keras_serializable
class TFCTRLMainLayer(tf.keras.layers.Layer):
    config_class = CTRLConfig

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

        self.d_model_size = config.n_embd
        self.num_layers = config.n_layer

        self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size)

        self.w = TFSharedEmbeddings(
            config.vocab_size, config.n_embd, initializer_range=config.initializer_range, name="w"
        )

        self.dropout = tf.keras.layers.Dropout(config.embd_pdrop)
        self.h = [
            TFEncoderLayer(
                config.n_embd,
                config.n_head,
                config.dff,
                config.resid_pdrop,
                config.layer_norm_epsilon,
                config.output_attentions,
                name="h_._{}".format(i),
            )
            for i in range(config.n_layer)
        ]
        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")

    def get_input_embeddings(self):
        return self.w

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError

    def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
                heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
        """
        raise NotImplementedError

    def call(
        self,
        inputs,
        past=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        use_cache=True,
        training=False,
    ):

        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            past = inputs[1] if len(inputs) > 1 else past
            attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
            token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
            position_ids = inputs[4] if len(inputs) > 4 else position_ids
            head_mask = inputs[5] if len(inputs) > 5 else head_mask
            inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
            use_cache = inputs[7] if len(inputs) > 7 else use_cache
            assert len(inputs) <= 8, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            past = inputs.get("past", past)
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            use_cache = inputs.get("use_cache", use_cache)
            assert len(inputs) <= 8, "Too many inputs."
        else:
            input_ids = inputs

        # If using past key value states, only the last tokens
        # should be given as an input
        if past is not None:
            if input_ids is not None:
                input_ids = input_ids[:, -1:]
            if inputs_embeds is not None:
                inputs_embeds = inputs_embeds[:, -1:]
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1:]

        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)
            input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
        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 past is None:
            past_length = 0
            past = [None] * len(self.h)
        else:
            past_length = shape_list(past[0][0])[-2]
        if position_ids is None:
            position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
            position_ids = tf.tile(position_ids, [input_shape[0], 1])

        # Attention mask.
        if attention_mask is not None:
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and -10000.0 for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.

            attention_mask = tf.cast(attention_mask, tf.float32)
            attention_mask = (1.0 - attention_mask) * -10000.0
        else:
            attention_mask = None

        # 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
        # head_mask has shape n_layer x batch x n_heads x N x N
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.num_layers

        if token_type_ids is not None:
            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
            token_type_embeds = self.w(token_type_ids, mode="embedding")
            token_type_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
        else:
            token_type_embeds = 0
        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])

        if inputs_embeds is None:
            inputs_embeds = self.w(input_ids, mode="embedding")
        seq_len = input_shape[-1]
        mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)

        inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))

        pos_embeds = tf.gather(self.pos_encoding, position_ids)

        hidden_states = inputs_embeds + pos_embeds + token_type_embeds

        hidden_states = self.dropout(hidden_states, training=training)

        output_shape = input_shape + [shape_list(hidden_states)[-1]]
        presents = ()
        all_hidden_states = ()
        all_attentions = []
        for i, (h, layer_past) in enumerate(zip(self.h, past)):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
            outputs = h([hidden_states, mask, layer_past, attention_mask, head_mask[i], use_cache], training=training)
            hidden_states, present = outputs[:2]

            if use_cache is True:
                presents = presents + (present,)

            if self.output_attentions:
                all_attentions.append(outputs[2])

        hidden_states = self.layernorm(hidden_states)
        hidden_states = tf.reshape(hidden_states, output_shape)
        if self.output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if use_cache is True:
            outputs = outputs + (presents,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            # let the number of heads free (-1) so we can extract attention even after head pruning
            attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
            all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
            outputs = outputs + (all_attentions,)
        return outputs


class TFCTRLPreTrainedModel(TFPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = CTRLConfig
    pretrained_model_archive_map = TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "transformer"


CTRL_START_DOCSTRING = r"""

    .. 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 :obj:`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 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])` or :obj:`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:
          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`

    Parameters:
        config (:class:`~transformers.CTRLConfig`): 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.
"""

CTRL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.
            If `past` is used, optionally only the last `input_ids` have to be input (see `past`).

            Indices can be obtained using :class:`transformers.CTRLTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            (see `past` output below). Can be used to speed up sequential decoding.
            If `past` is used, the user can optionally input only the last `input_ids`
            (those that don't have their past given to this model) of shape :obj:`(batch_size, 1)`
            instead of all `input_ids` of shape :obj:`(batch_size, sequence_length)`.
        attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token
            If `past` is used, optionally only the last `token_type_ids` have to be input (see `past`).

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        input_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            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 `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
            If `past` is used, optionally only the last `input_embeds` have to be input (see `past`).
        use_cache (:obj:`bool`):
            If `use_cache` is True, `past` key value states are returned and
            can be used to speed up decoding (see `past`). Defaults to `True`.
        training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
            Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
            (if set to :obj:`False`) for evaluation.
"""


@add_start_docstrings(
    "The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
    CTRL_START_DOCSTRING,
)
class TFCTRLModel(TFCTRLPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFCTRLMainLayer(config, name="transformer")

    @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
        last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the last layer of the model.
        past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
            Contains pre-computed hidden-states (key and values in the attention blocks).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    Examples::

        import tensorflow as tf
        from transformers import CTRLTokenizer, TFCTRLModel

        tokenizer = CTRLTokenizer.from_pretrained('ctrl')
        model = TFCTRLModel.from_pretrained('ctrl')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

        """
        outputs = self.transformer(inputs, **kwargs)
        return outputs


class TFCTRLLMHead(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(
    """The CTRL Model transformer with a language modeling head on top
    (linear layer with weights tied to the input embeddings). """,
    CTRL_START_DOCSTRING,
)
class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFCTRLMainLayer(config, name="transformer")

        self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head")

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

    def prepare_inputs_for_generation(self, inputs, past, **kwargs):
        # only last token for inputs_ids if past is defined in kwargs
        if past:
            inputs = tf.expand_dims(inputs[:, -1], -1)

        return {"inputs": inputs, "past": past, "use_cache": kwargs["use_cache"]}

    @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
        prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
            Contains pre-computed hidden-states (key and values in the attention blocks).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    Examples::

        import tensorflow as tf
        from transformers import CTRLTokenizer, TFCTRLLMHeadModel

        tokenizer = CTRLTokenizer.from_pretrained('ctrl')
        model = TFCTRLLMHeadModel.from_pretrained('ctrl')

        input_ids = tf.constant([tokenizer.encode("Links Hello, my dog is cute", add_special_tokens=True)])
        outputs = model(input_ids)
        loss, logits = outputs[:2]

        """
        transformer_outputs = self.transformer(inputs, **kwargs)
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)

        outputs = (lm_logits,) + transformer_outputs[1:]

        return outputs  # lm_logits, presents, (all hidden_states), (attentions)
