# 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,
    cast_bool_to_primitive,
    keras_serializable,
    shape_list,
)
from .tokenization_utils import BatchEncoding


logger = logging.getLogger(__name__)

TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "ctrl"
    # See all CTRL models at https://huggingface.co/models?filter=ctrl
]


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, **kwargs):
        super().__init__(**kwargs)
        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, output_attentions = 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
        use_cache = cast_bool_to_primitive(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 cast_bool_to_primitive(output_attentions) is True:
            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, **kwargs):
        super().__init__(**kwargs)

        self.multi_head_attention = TFMultiHeadAttention(d_model_size, num_heads, 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, output_attentions = inputs
        normed = self.layernorm1(x)
        attn_outputs = self.multi_head_attention(
            [normed, normed, normed, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions],
            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.use_cache = config.use_cache

        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,
                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 set_input_embeddings(self, value):
        self.w.weight = value
        self.w.vocab_size = value.shape[0]

    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=None,
        output_attentions=None,
        output_hidden_states=None,
        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
            output_attentions = inputs[8] if len(inputs) > 8 else output_attentions
            output_hidden_states = inputs[9] if len(inputs) > 9 else output_hidden_states
            assert len(inputs) <= 10, "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)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            assert len(inputs) <= 10, "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
        use_cache = use_cache if use_cache is not None else self.use_cache

        # 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 cast_bool_to_primitive(output_hidden_states) is True:
                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, output_attentions],
                training=training,
            )
            hidden_states, present = outputs[:2]

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

            if cast_bool_to_primitive(output_attentions) is True:
                all_attentions.append(outputs[2])

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

        outputs = (hidden_states,)
        if use_cache is True:
            outputs = outputs + (presents,)
        if cast_bool_to_primitive(output_hidden_states) is True:
            outputs = outputs + (all_hidden_states,)
        if cast_bool_to_primitive(output_attentions) is True:
            # 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
    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, input_ids_length)`):
            :obj:`input_ids_length` = ``sequence_length`` if ``past`` is ``None`` else ``past[0].shape[-2]`` (``sequence_length`` of input past key value states).

            Indices of input sequence tokens in the vocabulary.

            If `past` is used, only input_ids that do not have their past calculated should be passed as input_ids (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.
            The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        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

            `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**.
        inputs_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.
        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.
        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
            If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
"""


@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 ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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 ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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)