# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. 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 RoFormer model."""


import math
from typing import Dict, Optional, Tuple, Union

import numpy as np
import tensorflow as tf

from ...activations_tf import get_tf_activation
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,
    TFBaseModelOutputWithPooling,
    TFCausalLMOutput,
    TFMaskedLMOutput,
    TFMultipleChoiceModelOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFCausalLanguageModelingLoss,
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFMultipleChoiceLoss,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFSequenceSummary,
    TFTokenClassificationLoss,
    get_initializer,
    input_processing,
    keras_serializable,
    shape_list,
)
from ...utils import logging
from .configuration_roformer import RoFormerConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "junnyu/roformer_chinese_base"
_CONFIG_FOR_DOC = "RoFormerConfig"
_TOKENIZER_FOR_DOC = "RoFormerTokenizer"

TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "junnyu/roformer_chinese_small",
    "junnyu/roformer_chinese_base",
    "junnyu/roformer_chinese_char_small",
    "junnyu/roformer_chinese_char_base",
    "junnyu/roformer_small_discriminator",
    "junnyu/roformer_small_generator"
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
]


class TFRoFormerSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
        super().__init__(**kwargs)

        if embedding_dim % 2 != 0:
            raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")

        self.embedding_dim = embedding_dim
        self.num_positions = num_positions

    def build(self, input_shape: tf.TensorShape):
        """
        Build shared token embedding layer Shared weights logic adapted from
        https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
        """

        weight = self._init_weight(self.num_positions, self.embedding_dim)

        self.weight = self.add_weight(
            name="embeddings",
            shape=[self.num_positions, self.embedding_dim],
        )
        weight = tf.cast(weight, dtype=self.weight.dtype)

        self.weight.assign(weight)

        super().build(input_shape)

    @staticmethod
    def _init_weight(n_pos: int, dim: int):
        """
        Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
        the 2nd half of the vector. [dim // 2:]
        """
        position_enc = np.array(
            [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
        )
        table = np.zeros_like(position_enc)
        # index 0 is all zero
        table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
        table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
        # convert to tensor
        table = tf.convert_to_tensor(table)
        tf.stop_gradient(table)
        return table

    def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
        """Input is expected to be of size [bsz x seqlen]."""
        bsz, seq_len = input_shape[:2]

        positions = tf.range(past_key_values_length, seq_len + past_key_values_length, delta=1, name="range")
        return tf.gather(self.weight, positions)


class TFRoFormerEmbeddings(tf.keras.layers.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config: RoFormerConfig, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = config.vocab_size
        self.type_vocab_size = config.type_vocab_size
        self.embedding_size = config.embedding_size
        self.initializer_range = config.initializer_range
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def build(self, input_shape: tf.TensorShape):
        with tf.name_scope("word_embeddings"):
            self.weight = self.add_weight(
                name="weight",
                shape=[self.vocab_size, self.embedding_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("token_type_embeddings"):
            self.token_type_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.type_vocab_size, self.embedding_size],
                initializer=get_initializer(self.initializer_range),
            )

        super().build(input_shape)

    def call(
        self,
        input_ids: tf.Tensor = None,
        token_type_ids: tf.Tensor = None,
        inputs_embeds: tf.Tensor = None,
        training: bool = False,
    ) -> tf.Tensor:
        """
        Applies embedding based on inputs tensor.


        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

        input_shape = shape_list(inputs_embeds)[:-1]

        if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)

        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

        return final_embeddings


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

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
                f"of attention heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)

        self.query = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
        self.rotary_value = config.rotary_value

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
        return tf.transpose(tensor, perm=[0, 2, 1, 3])

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        sinusoidal_pos: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)
        mixed_key_layer = self.key(inputs=hidden_states)
        mixed_value_layer = self.value(inputs=hidden_states)
        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        if sinusoidal_pos is not None:
            if self.rotary_value:
                query_layer, key_layer, value_layer = self.apply_rotary_position_embeddings(
                    sinusoidal_pos, query_layer, key_layer, value_layer
                )
            else:
                query_layer, key_layer = self.apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFRoFormerModel call() function)
            attention_scores = tf.add(attention_scores, attention_mask)

        # Normalize the attention scores to probabilities.
        attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(inputs=attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = tf.multiply(attention_probs, head_mask)

        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])

        # (batch_size, seq_len_q, all_head_size)
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)

        return outputs

    @staticmethod
    def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None):
        # https://kexue.fm/archives/8265
        # sin [batch_size, num_heads, sequence_length, embed_size_per_head//2]
        # cos [batch_size, num_heads, sequence_length, embed_size_per_head//2]
        sin, cos = tf.split(sinusoidal_pos, num_or_size_splits=2, axis=-1)
        # sin [θ0,θ1,θ2......θd/2-1]-> sin_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1]
        # cos [θ0,θ1,θ2......θd/2-1]-> cos_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1]
        sin_pos = tf.repeat(sin, 2, axis=-1)
        cos_pos = tf.repeat(cos, 2, axis=-1)
        # rotate_half_query_layer [-q1,q0,-q3,q2......,-qd-1,qd-2]
        rotate_half_query_layer = tf.stack([-query_layer[..., 1::2], query_layer[..., ::2]], axis=-1)
        rotate_half_query_layer = tf.reshape(rotate_half_query_layer, shape_list(query_layer))
        query_layer = query_layer * cos_pos + rotate_half_query_layer * sin_pos
        # rotate_half_key_layer [-k1,k0,-k3,k2......,-kd-1,kd-2]
        rotate_half_key_layer = tf.stack([-key_layer[..., 1::2], key_layer[..., ::2]], axis=-1)
        rotate_half_key_layer = tf.reshape(rotate_half_key_layer, shape_list(key_layer))
        key_layer = key_layer * cos_pos + rotate_half_key_layer * sin_pos
        if value_layer is not None:
            # rotate_half_value_layer [-v1,v0,-v3,v2......,-vd-1,vd-2]
            rotate_half_value_layer = tf.stack([-value_layer[..., 1::2], value_layer[..., ::2]], axis=-1)
            rotate_half_value_layer = tf.reshape(rotate_half_value_layer, shape_list(value_layer))
            value_layer = value_layer * cos_pos + rotate_half_value_layer * sin_pos
            return query_layer, key_layer, value_layer
        return query_layer, key_layer


# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->RoFormer
class TFRoFormerSelfOutput(tf.keras.layers.Layer):
    def __init__(self, config: RoFormerConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.self_attention = TFRoFormerSelfAttention(config, name="self")
        self.dense_output = TFRoFormerSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        sinusoidal_pos: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            sinusoidal_pos=sinusoidal_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->RoFormer
class TFRoFormerIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: RoFormerConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )

        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.intermediate_act_fn = config.hidden_act

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->RoFormer
class TFRoFormerOutput(tf.keras.layers.Layer):
    def __init__(self, config: RoFormerConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)

        return hidden_states


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

        self.attention = TFRoFormerAttention(config, name="attention")
        self.intermediate = TFRoFormerIntermediate(config, name="intermediate")
        self.roformer_output = TFRoFormerOutput(config, name="output")

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        sinusoidal_pos: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(
            input_tensor=hidden_states,
            attention_mask=attention_mask,
            sinusoidal_pos=sinusoidal_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = attention_outputs[0]
        intermediate_output = self.intermediate(hidden_states=attention_output)
        layer_output = self.roformer_output(
            hidden_states=intermediate_output, input_tensor=attention_output, training=training
        )
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them

        return outputs


class TFRoFormerEncoder(tf.keras.layers.Layer):
    def __init__(self, config: RoFormerConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_positions = TFRoFormerSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            config.hidden_size // config.num_attention_heads,
            name="embed_positions",
        )
        self.layer = [TFRoFormerLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        # [sequence_length, embed_size_per_head] -> [batch_size, num_heads, sequence_length, embed_size_per_head]
        sinusoidal_pos = self.embed_positions(shape_list(hidden_states)[:-1])[None, None, :, :]

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

            layer_outputs = layer_module(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                sinusoidal_pos=sinusoidal_pos,
                head_mask=head_mask[i],
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

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

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


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

        self.dense = tf.keras.layers.Dense(
            units=config.embedding_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="dense",
        )

        if isinstance(config.hidden_act, str):
            self.transform_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.transform_act_fn = config.hidden_act

        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(inputs=hidden_states)

        return hidden_states


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

        self.vocab_size = config.vocab_size
        self.embedding_size = config.embedding_size

        self.transform = TFRoFormerPredictionHeadTransform(config, name="transform")

        # 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: tf.TensorShape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")

        super().build(input_shape)

    def get_output_embeddings(self) -> tf.keras.layers.Layer:
        return self.input_embeddings

    def set_output_embeddings(self, value: tf.Variable):
        self.input_embeddings.weight = value
        self.input_embeddings.vocab_size = shape_list(value)[0]

    def get_bias(self) -> Dict[str, tf.Variable]:
        return {"bias": self.bias}

    def set_bias(self, value: tf.Variable):
        self.bias = value["bias"]
        self.vocab_size = shape_list(value["bias"])[0]

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.transform(hidden_states=hidden_states)
        seq_length = shape_list(hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)

        return hidden_states


# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->RoFormer
class TFRoFormerMLMHead(tf.keras.layers.Layer):
    def __init__(self, config: RoFormerConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)

        self.predictions = TFRoFormerLMPredictionHead(config, input_embeddings, name="predictions")

    def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
        prediction_scores = self.predictions(hidden_states=sequence_output)

        return prediction_scores


@keras_serializable
class TFRoFormerMainLayer(tf.keras.layers.Layer):
    config_class = RoFormerConfig

    def __init__(self, config: RoFormerConfig, add_pooling_layer: bool = True, **kwargs):
        super().__init__(**kwargs)

        self.config = config

        self.embeddings = TFRoFormerEmbeddings(config, name="embeddings")
        if config.embedding_size != config.hidden_size:
            self.embeddings_project = tf.keras.layers.Dense(config.hidden_size, name="embeddings_project")

        self.encoder = TFRoFormerEncoder(config, name="encoder")

    def get_input_embeddings(self) -> tf.keras.layers.Layer:
        return self.embeddings

    def set_input_embeddings(self, value: tf.Variable):
        self.embeddings.weight = value
        self.embeddings.vocab_size = shape_list(value)[0]

    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} See base
        class PreTrainedModel
        """
        raise NotImplementedError

    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
        **kwargs,
    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
            kwargs_call=kwargs,
        )

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

        if inputs["attention_mask"] is None:
            inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)

        if inputs["token_type_ids"] is None:
            inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)

        embedding_output = self.embeddings(
            input_ids=inputs["input_ids"],
            token_type_ids=inputs["token_type_ids"],
            inputs_embeds=inputs["inputs_embeds"],
            training=inputs["training"],
        )
        if hasattr(self, "embeddings_project"):
            embedding_output = self.embeddings_project(embedding_output, training=inputs["training"])

        # 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.
        extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))

        # 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.
        extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)

        # 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 inputs["head_mask"] is not None:
            raise NotImplementedError
        else:
            inputs["head_mask"] = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=inputs["head_mask"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )

        sequence_output = encoder_outputs[0]

        if not inputs["return_dict"]:
            return (sequence_output,) + encoder_outputs[1:]

        return TFBaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


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

    config_class = RoFormerConfig
    base_model_prefix = "roformer"


ROFORMER_START_DOCSTRING = r"""

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

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

    <Tip>

    TF 2.0 models accepts two formats as inputs:

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

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

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

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

    </Tip>

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

ROFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`RoFormerTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`np.ndarray` or `tf.Tensor` of shape `({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#attention-mask)
        token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
            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#token-type-ids)
        head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(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 (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `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.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
            in eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `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 RoFormer Model transformer outputing raw hidden-states without any specific head on top.",
    ROFORMER_START_DOCSTRING,
)
class TFRoFormerModel(TFRoFormerPreTrainedModel):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.roformer = TFRoFormerMainLayer(config, name="roformer")

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )

        return outputs

    def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)


@add_start_docstrings("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING)
class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        if config.is_decoder:
            logger.warning(
                "If you want to use `TFRoFormerForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.mlm = TFRoFormerMLMHead(config, input_embeddings=self.roformer.embeddings, name="mlm___cls")

    def get_lm_head(self) -> tf.keras.layers.Layer:
        return self.mlm.predictions

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(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]`
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        sequence_output = outputs[0]
        prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
        loss = (
            None
            if inputs["labels"] is None
            else self.hf_compute_loss(labels=inputs["labels"], logits=prediction_scores)
        )

        if not inputs["return_dict"]:
            output = (prediction_scores,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFMaskedLMOutput(
            loss=loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING
)
class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        if not config.is_decoder:
            logger.warning("If you want to use `TFRoFormerForCausalLM` as a standalone, add `is_decoder=True.`")

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.mlm = TFRoFormerMLMHead(config, input_embeddings=self.roformer.embeddings, name="mlm___cls")

    def get_lm_head(self) -> tf.keras.layers.Layer:
        return self.mlm.predictions

    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFCausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        sequence_output = outputs[0]
        logits = self.mlm(sequence_output=sequence_output, training=inputs["training"])
        loss = None

        if inputs["labels"] is not None:
            # shift labels to the left and cut last logit token
            shifted_logits = logits[:, :-1]
            labels = inputs["labels"][:, 1:]
            loss = self.hf_compute_loss(labels=labels, logits=shifted_logits)

        if not inputs["return_dict"]:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

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

    def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)


class TFRoFormerClassificationHead(tf.keras.layers.Layer):
    """Head for sentence-level classification tasks."""

    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.out_proj = tf.keras.layers.Dense(
            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
        )

        if isinstance(config.hidden_act, str):
            self.classifier_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.classifier_act_fn = config.hidden_act

    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.classifier_act_fn(hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.out_proj(hidden_states)

        return hidden_states


@add_start_docstrings(
    """
    RoFormer Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks.
    """,
    ROFORMER_START_DOCSTRING,
)
class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.classifier = TFRoFormerClassificationHead(config, name="classifier")

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(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).
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        logits = self.classifier(hidden_states=outputs[0], training=inputs["training"])
        loss = None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=logits)

        if not inputs["return_dict"]:
            output = (logits,) + outputs[1:]

            return ((loss,) + output) if loss is not None else output

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

    def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """
    RoFormer 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.
    """,
    ROFORMER_START_DOCSTRING,
)
class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.sequence_summary = TFSequenceSummary(config, config.initializer_range, name="sequence_summary")
        self.classifier = tf.keras.layers.Dense(
            units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        """
        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(
        ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
            where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )

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

        flat_input_ids = (
            tf.reshape(tensor=inputs["input_ids"], shape=(-1, seq_length)) if inputs["input_ids"] is not None else None
        )
        flat_attention_mask = (
            tf.reshape(tensor=inputs["attention_mask"], shape=(-1, seq_length))
            if inputs["attention_mask"] is not None
            else None
        )
        flat_token_type_ids = (
            tf.reshape(tensor=inputs["token_type_ids"], shape=(-1, seq_length))
            if inputs["token_type_ids"] is not None
            else None
        )
        flat_inputs_embeds = (
            tf.reshape(tensor=inputs["inputs_embeds"], shape=(-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
            if inputs["inputs_embeds"] is not None
            else None
        )
        outputs = self.roformer(
            input_ids=flat_input_ids,
            attention_mask=flat_attention_mask,
            token_type_ids=flat_token_type_ids,
            head_mask=inputs["head_mask"],
            inputs_embeds=flat_inputs_embeds,
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        logits = self.sequence_summary(inputs=outputs[0], training=inputs["training"])
        logits = self.classifier(inputs=logits)
        reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
        loss = (
            None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=reshaped_logits)
        )

        if not inputs["return_dict"]:
            output = (reshaped_logits,) + outputs[1:]

            return ((loss,) + output) if loss is not None else output

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

    @tf.function(
        input_signature=[
            {
                "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
                "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
                "token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
            }
        ]
    )
    def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
        output = self.call(input_ids=inputs)

        return self.serving_output(output)

    def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """
    RoFormer 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.
    """,
    ROFORMER_START_DOCSTRING,
)
class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.classifier = tf.keras.layers.Dense(
            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            labels=labels,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        sequence_output = outputs[0]
        sequence_output = self.dropout(inputs=sequence_output, training=inputs["training"])
        logits = self.classifier(inputs=sequence_output)
        loss = None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=logits)

        if not inputs["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,
        )

    def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """
    RoFormer 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`).
    """,
    ROFORMER_START_DOCSTRING,
)
class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.roformer = TFRoFormerMainLayer(config, name="roformer")
        self.qa_outputs = tf.keras.layers.Dense(
            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
        )

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
        end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
        **kwargs,
    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        r"""
        start_positions (`tf.Tensor` or `np.ndarray` of shape `(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 (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`tf.Tensor` or `np.ndarray` of shape `(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 (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        inputs = input_processing(
            func=self.call,
            config=self.config,
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            start_positions=start_positions,
            end_positions=end_positions,
            training=training,
            kwargs_call=kwargs,
        )
        outputs = self.roformer(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            token_type_ids=inputs["token_type_ids"],
            head_mask=inputs["head_mask"],
            inputs_embeds=inputs["inputs_embeds"],
            output_attentions=inputs["output_attentions"],
            output_hidden_states=inputs["output_hidden_states"],
            return_dict=inputs["return_dict"],
            training=inputs["training"],
        )
        sequence_output = outputs[0]
        logits = self.qa_outputs(inputs=sequence_output)
        start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
        start_logits = tf.squeeze(input=start_logits, axis=-1)
        end_logits = tf.squeeze(input=end_logits, axis=-1)
        loss = None

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

        if not inputs["return_dict"]:
            output = (start_logits, end_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

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

    def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFQuestionAnsweringModelOutput(
            start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
        )
