# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 Transformer XL model.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import tensorflow as tf

from ...modeling_tf_utils import (
    TFModelInputType,
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_transfo_xl import TransfoXLConfig
from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "transfo-xl-wt103"
_CONFIG_FOR_DOC = "TransfoXLConfig"

TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "transfo-xl-wt103",
    # See all Transformer XL models at https://huggingface.co/models?filter=transfo-xl
]


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

        self.inv_freq = 1 / (10000 ** (tf.range(0, demb, 2.0) / demb))

    def call(self, pos_seq, bsz=None):
        self.inv_freq = tf.cast(self.inv_freq, dtype=pos_seq.dtype)
        sinusoid_inp = tf.einsum("i,j->ij", pos_seq, self.inv_freq)
        pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)

        if bsz is not None:
            return tf.tile(pos_emb[:, None, :], [1, bsz, 1])
        else:
            return pos_emb[:, None, :]


class TFPositionwiseFF(tf.keras.layers.Layer):
    def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs):
        super().__init__(**kwargs)

        self.d_model = d_model
        self.d_inner = d_inner
        self.dropout = dropout

        self.layer_1 = tf.keras.layers.Dense(
            d_inner, kernel_initializer=get_initializer(init_std), activation=tf.nn.relu, name="CoreNet_._0"
        )
        self.drop_1 = tf.keras.layers.Dropout(dropout)
        self.layer_2 = tf.keras.layers.Dense(d_model, kernel_initializer=get_initializer(init_std), name="CoreNet_._3")
        self.drop_2 = tf.keras.layers.Dropout(dropout)

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

        self.pre_lnorm = pre_lnorm

    def call(self, inp, training=False):
        if self.pre_lnorm:
            # layer normalization + positionwise feed-forward
            core_out = self.layer_norm(inp)
            core_out = self.layer_1(core_out)
            core_out = self.drop_1(core_out, training=training)
            core_out = self.layer_2(core_out)
            core_out = self.drop_2(core_out, training=training)

            # residual connection
            output = core_out + inp
        else:
            # positionwise feed-forward
            core_out = self.layer_1(inp)
            core_out = self.drop_1(core_out, training=training)
            core_out = self.layer_2(core_out)
            core_out = self.drop_2(core_out, training=training)

            # residual connection + layer normalization
            output = self.layer_norm(inp + core_out)

        return output


class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer):
    def __init__(
        self,
        n_head,
        d_model,
        d_head,
        dropout,
        dropatt=0.0,
        pre_lnorm=False,
        r_r_bias=None,
        r_w_bias=None,
        layer_norm_epsilon=1e-5,
        init_std=0.02,
        output_attentions=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.dropout = dropout
        self.output_attentions = output_attentions

        self.qkv_net = tf.keras.layers.Dense(
            3 * n_head * d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="qkv_net"
        )

        self.drop = tf.keras.layers.Dropout(dropout)
        self.dropatt = tf.keras.layers.Dropout(dropatt)
        self.o_net = tf.keras.layers.Dense(
            d_model, kernel_initializer=get_initializer(init_std), use_bias=False, name="o_net"
        )

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

        self.scale = 1 / (d_head**0.5)

        self.pre_lnorm = pre_lnorm

        if r_r_bias is not None and r_w_bias is not None:  # Biases are shared
            self.r_r_bias = r_r_bias
            self.r_w_bias = r_w_bias
        else:
            self.r_r_bias = None
            self.r_w_bias = None

        self.r_net = tf.keras.layers.Dense(
            self.n_head * self.d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="r_net"
        )

    def build(self, input_shape):
        if self.r_r_bias is None or self.r_w_bias is None:  # Biases are not shared
            self.r_r_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias"
            )
            self.r_w_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias"
            )
        super().build(input_shape)

    def _rel_shift(self, x):
        x_size = shape_list(x)

        x = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])
        x = tf.reshape(x, [x_size[1] + 1, x_size[0], x_size[2], x_size[3]])
        x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
        x = tf.reshape(x, x_size)

        return x

    def call(self, w, r, attn_mask, mems, head_mask, output_attentions, training=False):
        qlen, rlen, bsz = shape_list(w)[0], shape_list(r)[0], shape_list(w)[1]

        if mems is not None:
            mems = tf.cast(mems, dtype=w.dtype)
            cat = tf.concat([mems, w], 0)
            if self.pre_lnorm:
                w_heads = self.qkv_net(self.layer_norm(cat))
            else:
                w_heads = self.qkv_net(cat)
            r_head_k = self.r_net(r)

            w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1)
            w_head_q = w_head_q[-qlen:]
        else:
            if self.pre_lnorm:
                w_heads = self.qkv_net(self.layer_norm(w))
            else:
                w_heads = self.qkv_net(w)
            r_head_k = self.r_net(r)

            w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1)

        klen = shape_list(w_head_k)[0]

        w_head_q = tf.reshape(w_head_q, (qlen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head
        w_head_k = tf.reshape(w_head_k, (klen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head
        w_head_v = tf.reshape(w_head_v, (klen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head

        r_head_k = tf.reshape(r_head_k, (rlen, self.n_head, self.d_head))  # qlen x n_head x d_head

        # compute attention score
        rw_head_q = w_head_q + self.r_w_bias  # qlen x bsz x n_head x d_head
        AC = tf.einsum("ibnd,jbnd->ijbn", rw_head_q, w_head_k)  # qlen x klen x bsz x n_head

        rr_head_q = w_head_q + self.r_r_bias
        BD = tf.einsum("ibnd,jnd->ijbn", rr_head_q, r_head_k)  # qlen x klen x bsz x n_head
        BD = self._rel_shift(BD)

        # [qlen x klen x bsz x n_head]
        attn_score = AC + BD
        attn_score = attn_score * self.scale

        # compute attention probability
        if attn_mask is not None:
            attn_mask_t = attn_mask[:, :, None, None]
            attn_mask_t = tf.cast(attn_mask_t, dtype=attn_score.dtype)
            attn_score = attn_score * (1.0 - attn_mask_t) - 1e30 * attn_mask_t

        # [qlen x klen x bsz x n_head]
        attn_prob = stable_softmax(attn_score, axis=1)
        attn_prob = self.dropatt(attn_prob, training=training)

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

        # compute attention vector
        attn_vec = tf.einsum("ijbn,jbnd->ibnd", attn_prob, w_head_v)

        # [qlen x bsz x n_head x d_head]
        attn_vec_sizes = shape_list(attn_vec)
        attn_vec = tf.reshape(attn_vec, (attn_vec_sizes[0], attn_vec_sizes[1], self.n_head * self.d_head))

        # linear projection
        attn_out = self.o_net(attn_vec)
        attn_out = self.drop(attn_out, training=training)

        if self.pre_lnorm:
            # residual connection
            outputs = [w + attn_out]
        else:
            # residual connection + layer normalization
            outputs = [self.layer_norm(w + attn_out)]

        if output_attentions:
            outputs.append(attn_prob)

        return outputs


class TFRelPartialLearnableDecoderLayer(tf.keras.layers.Layer):
    def __init__(
        self,
        n_head,
        d_model,
        d_head,
        d_inner,
        dropout,
        dropatt=0.0,
        pre_lnorm=False,
        r_w_bias=None,
        r_r_bias=None,
        layer_norm_epsilon=1e-5,
        init_std=0.02,
        output_attentions=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.dec_attn = TFRelPartialLearnableMultiHeadAttn(
            n_head,
            d_model,
            d_head,
            dropout,
            dropatt=dropatt,
            pre_lnorm=pre_lnorm,
            r_w_bias=r_w_bias,
            r_r_bias=r_r_bias,
            init_std=init_std,
            layer_norm_epsilon=layer_norm_epsilon,
            output_attentions=output_attentions,
            name="dec_attn",
        )
        self.pos_ff = TFPositionwiseFF(
            d_model,
            d_inner,
            dropout,
            pre_lnorm=pre_lnorm,
            init_std=init_std,
            layer_norm_epsilon=layer_norm_epsilon,
            name="pos_ff",
        )

    def call(self, dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=False):
        attn_outputs = self.dec_attn(dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=training)
        ff_output = self.pos_ff(attn_outputs[0], training=training)

        outputs = [ff_output] + attn_outputs[1:]

        return outputs


class TFTransfoEmbeddings(tf.keras.layers.Layer):
    def __init__(self, vocab_size, emb_size, init_std, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = vocab_size
        self.emb_size = emb_size
        self.init_std = init_std

    def build(self, input_shape):
        self.weight = self.add_weight(
            shape=(self.vocab_size, self.emb_size),
            initializer=get_initializer(self.init_std),
            name="embeddings",
        )

        super().build(input_shape)

    def call(self, inputs):
        return tf.gather(self.weight, inputs)


class TFAdaptiveEmbedding(tf.keras.layers.Layer):
    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, init_std=0.02, sample_softmax=False, **kwargs):
        super().__init__(**kwargs)

        self.n_token = n_token
        self.d_embed = d_embed
        self.init_std = init_std

        self.cutoffs = cutoffs + [n_token]
        self.div_val = div_val
        self.d_proj = d_proj

        self.emb_scale = d_proj**0.5

        self.cutoff_ends = [0] + self.cutoffs

        self.emb_layers = []
        self.emb_projs = []

        if div_val == 1:
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
        else:
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                d_emb_i = d_embed // (div_val**i)
                self.emb_layers.append(
                    TFTransfoEmbeddings(
                        r_idx - l_idx,
                        d_emb_i,
                        init_std,
                        name=f"emb_layers_._{i}",
                    )
                )

    def build(self, input_shape):
        for i in range(len(self.cutoffs)):
            d_emb_i = self.d_embed // (self.div_val**i)
            self.emb_projs.append(
                self.add_weight(
                    shape=(d_emb_i, self.d_proj),
                    initializer=get_initializer(self.init_std),
                    trainable=True,
                    name=f"emb_projs_._{i}",
                )
            )

        super().build(input_shape)

    def call(self, inp):
        if self.div_val == 1:
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
        else:
            inp_flat = tf.reshape(inp, (-1,))
            emb_flat = tf.zeros([shape_list(inp_flat)[0], self.d_proj])
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]

                mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)

                inp_i = tf.boolean_mask(inp_flat, mask_i) - l_idx
                emb_i = self.emb_layers[i](inp_i)
                emb_i = tf.einsum("id,de->ie", emb_i, self.emb_projs[i])

                mask_idx = tf.where(mask_i)
                scatter = tf.scatter_nd(mask_idx, emb_i, shape_list(emb_flat))
                emb_flat = tf.cast(emb_flat, dtype=scatter.dtype)
                emb_flat += scatter

            embed_shape = shape_list(inp) + [self.d_proj]
            embed = tf.reshape(emb_flat, embed_shape)

        embed *= self.emb_scale

        return embed


@keras_serializable
class TFTransfoXLMainLayer(tf.keras.layers.Layer):
    config_class = TransfoXLConfig

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

        self.config = config
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.return_dict = config.use_return_dict

        self.n_token = config.vocab_size

        self.d_embed = config.d_embed
        self.d_model = config.d_model
        self.n_head = config.n_head
        self.d_head = config.d_head
        self.untie_r = config.untie_r

        self.word_emb = TFAdaptiveEmbedding(
            config.vocab_size,
            config.d_embed,
            config.d_model,
            config.cutoffs,
            div_val=config.div_val,
            init_std=config.init_std,
            name="word_emb",
        )

        self.drop = tf.keras.layers.Dropout(config.dropout)

        self.n_layer = config.n_layer
        self.mem_len = config.mem_len
        self.attn_type = config.attn_type

        self.layers = []
        if config.attn_type == 0:  # the default attention
            for i in range(config.n_layer):
                self.layers.append(
                    TFRelPartialLearnableDecoderLayer(
                        config.n_head,
                        config.d_model,
                        config.d_head,
                        config.d_inner,
                        config.dropout,
                        dropatt=config.dropatt,
                        pre_lnorm=config.pre_lnorm,
                        r_w_bias=None if self.untie_r else self.r_w_bias,
                        r_r_bias=None if self.untie_r else self.r_r_bias,
                        layer_norm_epsilon=config.layer_norm_epsilon,
                        init_std=config.init_std,
                        output_attentions=self.output_attentions,
                        name=f"layers_._{i}",
                    )
                )
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

        self.same_length = config.same_length
        self.clamp_len = config.clamp_len

        if self.attn_type == 0:  # default attention
            self.pos_emb = TFPositionalEmbedding(self.d_model, name="pos_emb")
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

    def build(self, input_shape):
        if not self.untie_r:
            self.r_w_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias"
            )
            self.r_r_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias"
            )
        super().build(input_shape)

    def get_input_embeddings(self):
        return self.word_emb

    def set_input_embeddings(self, value):
        raise NotImplementedError

    def backward_compatible(self):
        self.sample_softmax = -1

    def reset_memory_length(self, mem_len):
        self.mem_len = mem_len

    def _prune_heads(self, heads):
        raise NotImplementedError

    def init_mems(self, bsz):
        if self.mem_len > 0:
            mems = []
            for i in range(self.n_layer):
                empty = tf.zeros([self.mem_len, bsz, self.d_model])
                mems.append(empty)

            return mems
        else:
            return None

    def _update_mems(self, hids, mems, mlen, qlen):
        # does not deal with None
        if mems is None:
            return None

        # mems is not None
        assert len(hids) == len(mems), "len(hids) != len(mems)"

        # There are `mlen + qlen` steps that can be cached into mems
        new_mems = []
        end_idx = mlen + tf.math.maximum(0, qlen)
        beg_idx = tf.math.maximum(0, end_idx - tf.convert_to_tensor(self.mem_len))
        for i in range(len(hids)):
            mems[i] = tf.cast(mems[i], dtype=hids[i].dtype)
            cat = tf.concat([mems[i], hids[i]], axis=0)
            tf.stop_gradient(cat)
            new_mems.append(cat[beg_idx:end_idx])

        return new_mems

    @unpack_inputs
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: bool = False,
    ):
        # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
        # so we transpose here from shape [bsz, len] to shape [len, bsz]
        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_ids = tf.transpose(input_ids, perm=(1, 0))
            qlen, bsz = shape_list(input_ids)
        elif inputs_embeds is not None:
            inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
            qlen, bsz = shape_list(inputs_embeds)[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if mems is None:
            mems = self.init_mems(bsz)

        # 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] (a head_mask for each layer)
        # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.n_layer

        if inputs_embeds is not None:
            word_emb = inputs_embeds
        else:
            word_emb = self.word_emb(input_ids)

        mlen = shape_list(mems[0])[0] if mems is not None else 0
        klen = mlen + qlen

        # Compute decoder attention mask
        all_ones = tf.ones([qlen, klen], dtype=tf.int32)
        upper_mask = 1 - tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), -1, mlen)
        if self.same_length:
            mask_len = klen - self.mem_len
            mask_shift_len = qlen - tf.nn.relu(mask_len)  # Lazy clamping of negatives to zero

            # Use an indicator variable instead of a conditional to keep the compiler happy
            lower_mask = tf.linalg.band_part(all_ones, -1, 0) - (
                tf.linalg.band_part(all_ones, mask_shift_len - 1, 0) * tf.cast(mask_shift_len != 0, tf.int32)
            )
            dec_attn_mask = upper_mask + lower_mask
        else:
            dec_attn_mask = upper_mask

        hids = []
        attentions = [] if output_attentions else None
        if self.attn_type == 0:  # default
            pos_seq = tf.range(klen - 1, -1, -1.0)
            if self.clamp_len > 0:
                pos_seq = tf.minimum(pos_seq, self.clamp_len)
            pos_emb = self.pos_emb(pos_seq)

            core_out = self.drop(word_emb, training=training)
            pos_emb = self.drop(pos_emb, training=training)

            for i, layer in enumerate(self.layers):
                hids.append(core_out)
                mems_i = None if mems is None else mems[i]
                layer_outputs = layer(
                    core_out,
                    pos_emb,
                    dec_attn_mask,
                    mems_i,
                    head_mask[i],
                    output_attentions,
                    training=training,
                )
                core_out = layer_outputs[0]
                if output_attentions:
                    attentions.append(layer_outputs[1])
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

        core_out = self.drop(core_out, training=training)

        new_mems = self._update_mems(hids, mems, mlen, qlen)

        # We transpose back here to shape [bsz, len, hidden_dim]
        core_out = tf.transpose(core_out, perm=(1, 0, 2))

        if output_hidden_states:
            # Transpose to library standard shape [bsz, len, hidden_dim] and add last layer
            hids = tuple(tf.transpose(t, perm=(1, 0, 2)) for t in hids)
            hids = hids + (core_out,)
        else:
            hids = None
        if output_attentions:
            # Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len]
            attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)

        if not return_dict:
            return tuple(v for v in [core_out, new_mems, hids, attentions] if v is not None)

        return TFTransfoXLModelOutput(
            last_hidden_state=core_out,
            mems=new_mems,
            hidden_states=hids,
            attentions=attentions,
        )


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

    config_class = TransfoXLConfig
    base_model_prefix = "transformer"


@dataclass
class TFTransfoXLModelOutput(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

    Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    last_hidden_state: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


@dataclass
class TFTransfoXLLMHeadModelOutput(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

    Args:
        losses (`tf.Tensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
            Language modeling losses (not reduced).
        prediction_scores (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    prediction_scores: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


@dataclass
class TFTransfoXLSequenceClassifierOutputWithPast(ModelOutput):
    """
    Base class for outputs of sentence classification models.

    Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


TRANSFO_XL_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>

    TensorFlow models and layers in `transformers` accept two formats as input:

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

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, 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(input_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})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

    Parameters:
        config ([`TransfoXLConfig`]): 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.
"""

TRANSFO_XL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

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

            [What are input IDs?](../glossary#input-ids)
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
            `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems
            given to this model should not be passed as `input_ids` as they have already been computed.
        head_mask (`tf.Tensor` or `Numpy array` 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 (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, 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 [`~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 Bert Model transformer outputting raw hidden-states without any specific head on top.",
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ):
        outputs = self.transformer(
            input_ids=input_ids,
            mems=mems,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs


@add_start_docstrings(
    """
    The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive
    input embeddings)
    """,
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")
        self.sample_softmax = config.sample_softmax
        assert self.sample_softmax <= 0, (
            "Sampling from the softmax is not implemented yet. Please look at issue: #3310:"
            " https://github.com/huggingface/transformers/issues/3310"
        )

        self.crit = TFAdaptiveSoftmaxMask(
            config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val, name="crit"
        )

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError()

    def get_output_embeddings(self):
        """Double-check if you are using adaptive softmax."""
        if len(self.crit.out_layers) > 0:
            return self.crit.out_layers[-1]
        return None

    def reset_memory_length(self, mem_len):
        self.transformer.reset_memory_length(mem_len)

    def init_mems(self, bsz):
        return self.transformer.init_mems(bsz)

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLLMHeadModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: bool = False,
    ):
        if input_ids is not None:
            bsz, tgt_len = shape_list(input_ids)[:2]
        else:
            bsz, tgt_len = shape_list(inputs_embeds)[:2]

        transformer_outputs = self.transformer(
            input_ids,
            mems,
            head_mask,
            inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict,
            training=training,
        )

        last_hidden = transformer_outputs[0]
        pred_hid = last_hidden[:, -tgt_len:]

        softmax_output = self.crit(pred_hid, labels, training=training)
        prediction_scores = softmax_output if labels is None else ()

        if not return_dict:
            return (prediction_scores,) + transformer_outputs[1:]

        return TFTransfoXLLMHeadModelOutput(
            prediction_scores=prediction_scores,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **model_kwargs):
        inputs = {}

        # if past is defined in model kwargs then use it for faster decoding
        if past_key_values:
            input_ids = tf.expand_dims(input_ids[:, -1], axis=-1)
        else:
            input_ids = input_ids

        return inputs


@add_start_docstrings(
    """
    The Transfo XL Model transformer with a sequence classification head on top (linear layer).

    [`TFTransfoXLForSequenceClassification`] uses the last token in order to do the classification, as other causal
    models (e.g. GPT-1,GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.score = tf.keras.layers.Dense(
            config.num_labels,
            kernel_initializer=get_initializer(config.init_range),
            name="score",
            use_bias=False,
        )
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")

    def get_output_embeddings(self):
        # Remove after transformers v4.32. Fix this model's `test_model_common_attributes` test too.
        logger.warning(
            "Sequence classification models do not have output embeddings. `.get_output_embeddings` will be removed "
            "in transformers v4.32."
        )
        return self.transformer.word_emb

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLSequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFTransfoXLSequenceClassifierOutputWithPast]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        transformer_outputs = self.transformer(
            input_ids=input_ids,
            mems=mems,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)
        in_logits = None
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (
                    tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1)
                    - 1
                )
                sequence_lengths = tf.where(sequence_lengths >= 0, sequence_lengths, input_ids.shape[-1] - 1)
                in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
            else:
                sequence_lengths = -1
                logger.warning(
                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )
        loss = None

        if labels is not None:
            if input_ids is not None:
                batch_size, sequence_length = shape_list(input_ids)[:2]
            else:
                batch_size, sequence_length = shape_list(inputs_embeds)[:2]
            assert (
                self.config.pad_token_id is not None or batch_size == 1
            ), "Cannot handle batch sizes > 1 if no padding token is defined."

            if not tf.is_tensor(sequence_lengths):
                in_logits = logits[0:batch_size, sequence_lengths]

            loss = self.hf_compute_loss(tf.reshape(labels, [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels]))

        pooled_logits = in_logits if in_logits is not None else logits

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

        return TFTransfoXLSequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 Transformer XL model.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import tensorflow as tf

from ...modeling_tf_utils import (
    TFModelInputType,
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_transfo_xl import TransfoXLConfig
from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "transfo-xl-wt103"
_CONFIG_FOR_DOC = "TransfoXLConfig"

TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "transfo-xl-wt103",
    # See all Transformer XL models at https://huggingface.co/models?filter=transfo-xl
]


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

        self.inv_freq = 1 / (10000 ** (tf.range(0, demb, 2.0) / demb))

    def call(self, pos_seq, bsz=None):
        self.inv_freq = tf.cast(self.inv_freq, dtype=pos_seq.dtype)
        sinusoid_inp = tf.einsum("i,j->ij", pos_seq, self.inv_freq)
        pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)

        if bsz is not None:
            return tf.tile(pos_emb[:, None, :], [1, bsz, 1])
        else:
            return pos_emb[:, None, :]


class TFPositionwiseFF(tf.keras.layers.Layer):
    def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs):
        super().__init__(**kwargs)

        self.d_model = d_model
        self.d_inner = d_inner
        self.dropout = dropout

        self.layer_1 = tf.keras.layers.Dense(
            d_inner, kernel_initializer=get_initializer(init_std), activation=tf.nn.relu, name="CoreNet_._0"
        )
        self.drop_1 = tf.keras.layers.Dropout(dropout)
        self.layer_2 = tf.keras.layers.Dense(d_model, kernel_initializer=get_initializer(init_std), name="CoreNet_._3")
        self.drop_2 = tf.keras.layers.Dropout(dropout)

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

        self.pre_lnorm = pre_lnorm

    def call(self, inp, training=False):
        if self.pre_lnorm:
            # layer normalization + positionwise feed-forward
            core_out = self.layer_norm(inp)
            core_out = self.layer_1(core_out)
            core_out = self.drop_1(core_out, training=training)
            core_out = self.layer_2(core_out)
            core_out = self.drop_2(core_out, training=training)

            # residual connection
            output = core_out + inp
        else:
            # positionwise feed-forward
            core_out = self.layer_1(inp)
            core_out = self.drop_1(core_out, training=training)
            core_out = self.layer_2(core_out)
            core_out = self.drop_2(core_out, training=training)

            # residual connection + layer normalization
            output = self.layer_norm(inp + core_out)

        return output


class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer):
    def __init__(
        self,
        n_head,
        d_model,
        d_head,
        dropout,
        dropatt=0.0,
        pre_lnorm=False,
        r_r_bias=None,
        r_w_bias=None,
        layer_norm_epsilon=1e-5,
        init_std=0.02,
        output_attentions=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.dropout = dropout
        self.output_attentions = output_attentions

        self.qkv_net = tf.keras.layers.Dense(
            3 * n_head * d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="qkv_net"
        )

        self.drop = tf.keras.layers.Dropout(dropout)
        self.dropatt = tf.keras.layers.Dropout(dropatt)
        self.o_net = tf.keras.layers.Dense(
            d_model, kernel_initializer=get_initializer(init_std), use_bias=False, name="o_net"
        )

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

        self.scale = 1 / (d_head**0.5)

        self.pre_lnorm = pre_lnorm

        if r_r_bias is not None and r_w_bias is not None:  # Biases are shared
            self.r_r_bias = r_r_bias
            self.r_w_bias = r_w_bias
        else:
            self.r_r_bias = None
            self.r_w_bias = None

        self.r_net = tf.keras.layers.Dense(
            self.n_head * self.d_head, kernel_initializer=get_initializer(init_std), use_bias=False, name="r_net"
        )

    def build(self, input_shape):
        if self.r_r_bias is None or self.r_w_bias is None:  # Biases are not shared
            self.r_r_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias"
            )
            self.r_w_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias"
            )
        super().build(input_shape)

    def _rel_shift(self, x):
        x_size = shape_list(x)

        x = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])
        x = tf.reshape(x, [x_size[1] + 1, x_size[0], x_size[2], x_size[3]])
        x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
        x = tf.reshape(x, x_size)

        return x

    def call(self, w, r, attn_mask, mems, head_mask, output_attentions, training=False):
        qlen, rlen, bsz = shape_list(w)[0], shape_list(r)[0], shape_list(w)[1]

        if mems is not None:
            mems = tf.cast(mems, dtype=w.dtype)
            cat = tf.concat([mems, w], 0)
            if self.pre_lnorm:
                w_heads = self.qkv_net(self.layer_norm(cat))
            else:
                w_heads = self.qkv_net(cat)
            r_head_k = self.r_net(r)

            w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1)
            w_head_q = w_head_q[-qlen:]
        else:
            if self.pre_lnorm:
                w_heads = self.qkv_net(self.layer_norm(w))
            else:
                w_heads = self.qkv_net(w)
            r_head_k = self.r_net(r)

            w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, axis=-1)

        klen = shape_list(w_head_k)[0]

        w_head_q = tf.reshape(w_head_q, (qlen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head
        w_head_k = tf.reshape(w_head_k, (klen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head
        w_head_v = tf.reshape(w_head_v, (klen, bsz, self.n_head, self.d_head))  # qlen x bsz x n_head x d_head

        r_head_k = tf.reshape(r_head_k, (rlen, self.n_head, self.d_head))  # qlen x n_head x d_head

        # compute attention score
        rw_head_q = w_head_q + self.r_w_bias  # qlen x bsz x n_head x d_head
        AC = tf.einsum("ibnd,jbnd->ijbn", rw_head_q, w_head_k)  # qlen x klen x bsz x n_head

        rr_head_q = w_head_q + self.r_r_bias
        BD = tf.einsum("ibnd,jnd->ijbn", rr_head_q, r_head_k)  # qlen x klen x bsz x n_head
        BD = self._rel_shift(BD)

        # [qlen x klen x bsz x n_head]
        attn_score = AC + BD
        attn_score = attn_score * self.scale

        # compute attention probability
        if attn_mask is not None:
            attn_mask_t = attn_mask[:, :, None, None]
            attn_mask_t = tf.cast(attn_mask_t, dtype=attn_score.dtype)
            attn_score = attn_score * (1.0 - attn_mask_t) - 1e30 * attn_mask_t

        # [qlen x klen x bsz x n_head]
        attn_prob = stable_softmax(attn_score, axis=1)
        attn_prob = self.dropatt(attn_prob, training=training)

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

        # compute attention vector
        attn_vec = tf.einsum("ijbn,jbnd->ibnd", attn_prob, w_head_v)

        # [qlen x bsz x n_head x d_head]
        attn_vec_sizes = shape_list(attn_vec)
        attn_vec = tf.reshape(attn_vec, (attn_vec_sizes[0], attn_vec_sizes[1], self.n_head * self.d_head))

        # linear projection
        attn_out = self.o_net(attn_vec)
        attn_out = self.drop(attn_out, training=training)

        if self.pre_lnorm:
            # residual connection
            outputs = [w + attn_out]
        else:
            # residual connection + layer normalization
            outputs = [self.layer_norm(w + attn_out)]

        if output_attentions:
            outputs.append(attn_prob)

        return outputs


class TFRelPartialLearnableDecoderLayer(tf.keras.layers.Layer):
    def __init__(
        self,
        n_head,
        d_model,
        d_head,
        d_inner,
        dropout,
        dropatt=0.0,
        pre_lnorm=False,
        r_w_bias=None,
        r_r_bias=None,
        layer_norm_epsilon=1e-5,
        init_std=0.02,
        output_attentions=False,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.dec_attn = TFRelPartialLearnableMultiHeadAttn(
            n_head,
            d_model,
            d_head,
            dropout,
            dropatt=dropatt,
            pre_lnorm=pre_lnorm,
            r_w_bias=r_w_bias,
            r_r_bias=r_r_bias,
            init_std=init_std,
            layer_norm_epsilon=layer_norm_epsilon,
            output_attentions=output_attentions,
            name="dec_attn",
        )
        self.pos_ff = TFPositionwiseFF(
            d_model,
            d_inner,
            dropout,
            pre_lnorm=pre_lnorm,
            init_std=init_std,
            layer_norm_epsilon=layer_norm_epsilon,
            name="pos_ff",
        )

    def call(self, dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=False):
        attn_outputs = self.dec_attn(dec_inp, r, dec_attn_mask, mems, head_mask, output_attentions, training=training)
        ff_output = self.pos_ff(attn_outputs[0], training=training)

        outputs = [ff_output] + attn_outputs[1:]

        return outputs


class TFTransfoEmbeddings(tf.keras.layers.Layer):
    def __init__(self, vocab_size, emb_size, init_std, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = vocab_size
        self.emb_size = emb_size
        self.init_std = init_std

    def build(self, input_shape):
        self.weight = self.add_weight(
            shape=(self.vocab_size, self.emb_size),
            initializer=get_initializer(self.init_std),
            name="embeddings",
        )

        super().build(input_shape)

    def call(self, inputs):
        return tf.gather(self.weight, inputs)


class TFAdaptiveEmbedding(tf.keras.layers.Layer):
    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, init_std=0.02, sample_softmax=False, **kwargs):
        super().__init__(**kwargs)

        self.n_token = n_token
        self.d_embed = d_embed
        self.init_std = init_std

        self.cutoffs = cutoffs + [n_token]
        self.div_val = div_val
        self.d_proj = d_proj

        self.emb_scale = d_proj**0.5

        self.cutoff_ends = [0] + self.cutoffs

        self.emb_layers = []
        self.emb_projs = []

        if div_val == 1:
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
        else:
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                d_emb_i = d_embed // (div_val**i)
                self.emb_layers.append(
                    TFTransfoEmbeddings(
                        r_idx - l_idx,
                        d_emb_i,
                        init_std,
                        name=f"emb_layers_._{i}",
                    )
                )

    def build(self, input_shape):
        for i in range(len(self.cutoffs)):
            d_emb_i = self.d_embed // (self.div_val**i)
            self.emb_projs.append(
                self.add_weight(
                    shape=(d_emb_i, self.d_proj),
                    initializer=get_initializer(self.init_std),
                    trainable=True,
                    name=f"emb_projs_._{i}",
                )
            )

        super().build(input_shape)

    def call(self, inp):
        if self.div_val == 1:
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint
        else:
            inp_flat = tf.reshape(inp, (-1,))
            emb_flat = tf.zeros([shape_list(inp_flat)[0], self.d_proj])
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]

                mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)

                inp_i = tf.boolean_mask(inp_flat, mask_i) - l_idx
                emb_i = self.emb_layers[i](inp_i)
                emb_i = tf.einsum("id,de->ie", emb_i, self.emb_projs[i])

                mask_idx = tf.where(mask_i)
                scatter = tf.scatter_nd(mask_idx, emb_i, shape_list(emb_flat))
                emb_flat = tf.cast(emb_flat, dtype=scatter.dtype)
                emb_flat += scatter

            embed_shape = shape_list(inp) + [self.d_proj]
            embed = tf.reshape(emb_flat, embed_shape)

        embed *= self.emb_scale

        return embed


@keras_serializable
class TFTransfoXLMainLayer(tf.keras.layers.Layer):
    config_class = TransfoXLConfig

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

        self.config = config
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.return_dict = config.use_return_dict

        self.n_token = config.vocab_size

        self.d_embed = config.d_embed
        self.d_model = config.d_model
        self.n_head = config.n_head
        self.d_head = config.d_head
        self.untie_r = config.untie_r

        self.word_emb = TFAdaptiveEmbedding(
            config.vocab_size,
            config.d_embed,
            config.d_model,
            config.cutoffs,
            div_val=config.div_val,
            init_std=config.init_std,
            name="word_emb",
        )

        self.drop = tf.keras.layers.Dropout(config.dropout)

        self.n_layer = config.n_layer
        self.mem_len = config.mem_len
        self.attn_type = config.attn_type

        self.layers = []
        if config.attn_type == 0:  # the default attention
            for i in range(config.n_layer):
                self.layers.append(
                    TFRelPartialLearnableDecoderLayer(
                        config.n_head,
                        config.d_model,
                        config.d_head,
                        config.d_inner,
                        config.dropout,
                        dropatt=config.dropatt,
                        pre_lnorm=config.pre_lnorm,
                        r_w_bias=None if self.untie_r else self.r_w_bias,
                        r_r_bias=None if self.untie_r else self.r_r_bias,
                        layer_norm_epsilon=config.layer_norm_epsilon,
                        init_std=config.init_std,
                        output_attentions=self.output_attentions,
                        name=f"layers_._{i}",
                    )
                )
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

        self.same_length = config.same_length
        self.clamp_len = config.clamp_len

        if self.attn_type == 0:  # default attention
            self.pos_emb = TFPositionalEmbedding(self.d_model, name="pos_emb")
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

    def build(self, input_shape):
        if not self.untie_r:
            self.r_w_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_w_bias"
            )
            self.r_r_bias = self.add_weight(
                shape=(self.n_head, self.d_head), initializer="zeros", trainable=True, name="r_r_bias"
            )
        super().build(input_shape)

    def get_input_embeddings(self):
        return self.word_emb

    def set_input_embeddings(self, value):
        raise NotImplementedError

    def backward_compatible(self):
        self.sample_softmax = -1

    def reset_memory_length(self, mem_len):
        self.mem_len = mem_len

    def _prune_heads(self, heads):
        raise NotImplementedError

    def init_mems(self, bsz):
        if self.mem_len > 0:
            mems = []
            for i in range(self.n_layer):
                empty = tf.zeros([self.mem_len, bsz, self.d_model])
                mems.append(empty)

            return mems
        else:
            return None

    def _update_mems(self, hids, mems, mlen, qlen):
        # does not deal with None
        if mems is None:
            return None

        # mems is not None
        assert len(hids) == len(mems), "len(hids) != len(mems)"

        # There are `mlen + qlen` steps that can be cached into mems
        new_mems = []
        end_idx = mlen + tf.math.maximum(0, qlen)
        beg_idx = tf.math.maximum(0, end_idx - tf.convert_to_tensor(self.mem_len))
        for i in range(len(hids)):
            mems[i] = tf.cast(mems[i], dtype=hids[i].dtype)
            cat = tf.concat([mems[i], hids[i]], axis=0)
            tf.stop_gradient(cat)
            new_mems.append(cat[beg_idx:end_idx])

        return new_mems

    @unpack_inputs
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: bool = False,
    ):
        # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
        # so we transpose here from shape [bsz, len] to shape [len, bsz]
        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_ids = tf.transpose(input_ids, perm=(1, 0))
            qlen, bsz = shape_list(input_ids)
        elif inputs_embeds is not None:
            inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
            qlen, bsz = shape_list(inputs_embeds)[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if mems is None:
            mems = self.init_mems(bsz)

        # 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] (a head_mask for each layer)
        # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.n_layer

        if inputs_embeds is not None:
            word_emb = inputs_embeds
        else:
            word_emb = self.word_emb(input_ids)

        mlen = shape_list(mems[0])[0] if mems is not None else 0
        klen = mlen + qlen

        # Compute decoder attention mask
        all_ones = tf.ones([qlen, klen], dtype=tf.int32)
        upper_mask = 1 - tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), -1, mlen)
        if self.same_length:
            mask_len = klen - self.mem_len
            mask_shift_len = qlen - tf.nn.relu(mask_len)  # Lazy clamping of negatives to zero

            # Use an indicator variable instead of a conditional to keep the compiler happy
            lower_mask = tf.linalg.band_part(all_ones, -1, 0) - (
                tf.linalg.band_part(all_ones, mask_shift_len - 1, 0) * tf.cast(mask_shift_len != 0, tf.int32)
            )
            dec_attn_mask = upper_mask + lower_mask
        else:
            dec_attn_mask = upper_mask

        hids = []
        attentions = [] if output_attentions else None
        if self.attn_type == 0:  # default
            pos_seq = tf.range(klen - 1, -1, -1.0)
            if self.clamp_len > 0:
                pos_seq = tf.minimum(pos_seq, self.clamp_len)
            pos_emb = self.pos_emb(pos_seq)

            core_out = self.drop(word_emb, training=training)
            pos_emb = self.drop(pos_emb, training=training)

            for i, layer in enumerate(self.layers):
                hids.append(core_out)
                mems_i = None if mems is None else mems[i]
                layer_outputs = layer(
                    core_out,
                    pos_emb,
                    dec_attn_mask,
                    mems_i,
                    head_mask[i],
                    output_attentions,
                    training=training,
                )
                core_out = layer_outputs[0]
                if output_attentions:
                    attentions.append(layer_outputs[1])
        else:  # learnable embeddings and absolute embeddings
            raise NotImplementedError  # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint

        core_out = self.drop(core_out, training=training)

        new_mems = self._update_mems(hids, mems, mlen, qlen)

        # We transpose back here to shape [bsz, len, hidden_dim]
        core_out = tf.transpose(core_out, perm=(1, 0, 2))

        if output_hidden_states:
            # Transpose to library standard shape [bsz, len, hidden_dim] and add last layer
            hids = tuple(tf.transpose(t, perm=(1, 0, 2)) for t in hids)
            hids = hids + (core_out,)
        else:
            hids = None
        if output_attentions:
            # Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len]
            attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)

        if not return_dict:
            return tuple(v for v in [core_out, new_mems, hids, attentions] if v is not None)

        return TFTransfoXLModelOutput(
            last_hidden_state=core_out,
            mems=new_mems,
            hidden_states=hids,
            attentions=attentions,
        )


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

    config_class = TransfoXLConfig
    base_model_prefix = "transformer"


@dataclass
class TFTransfoXLModelOutput(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

    Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    last_hidden_state: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


@dataclass
class TFTransfoXLLMHeadModelOutput(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

    Args:
        losses (`tf.Tensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
            Language modeling losses (not reduced).
        prediction_scores (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    prediction_scores: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


@dataclass
class TFTransfoXLSequenceClassifierOutputWithPast(ModelOutput):
    """
    Base class for outputs of sentence classification models.

    Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems`
            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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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.
    """

    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mems: List[tf.Tensor] = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


TRANSFO_XL_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>

    TensorFlow models and layers in `transformers` accept two formats as input:

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

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, 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(input_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})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

    Parameters:
        config ([`TransfoXLConfig`]): 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.
"""

TRANSFO_XL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

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

            [What are input IDs?](../glossary#input-ids)
        mems (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
            `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems
            given to this model should not be passed as `input_ids` as they have already been computed.
        head_mask (`tf.Tensor` or `Numpy array` 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 (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, 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 [`~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 Bert Model transformer outputting raw hidden-states without any specific head on top.",
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ):
        outputs = self.transformer(
            input_ids=input_ids,
            mems=mems,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs


@add_start_docstrings(
    """
    The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive
    input embeddings)
    """,
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")
        self.sample_softmax = config.sample_softmax
        assert self.sample_softmax <= 0, (
            "Sampling from the softmax is not implemented yet. Please look at issue: #3310:"
            " https://github.com/huggingface/transformers/issues/3310"
        )

        self.crit = TFAdaptiveSoftmaxMask(
            config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val, name="crit"
        )

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError()

    def get_output_embeddings(self):
        """Double-check if you are using adaptive softmax."""
        if len(self.crit.out_layers) > 0:
            return self.crit.out_layers[-1]
        return None

    def reset_memory_length(self, mem_len):
        self.transformer.reset_memory_length(mem_len)

    def init_mems(self, bsz):
        return self.transformer.init_mems(bsz)

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLLMHeadModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: bool = False,
    ):
        if input_ids is not None:
            bsz, tgt_len = shape_list(input_ids)[:2]
        else:
            bsz, tgt_len = shape_list(inputs_embeds)[:2]

        transformer_outputs = self.transformer(
            input_ids,
            mems,
            head_mask,
            inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict,
            training=training,
        )

        last_hidden = transformer_outputs[0]
        pred_hid = last_hidden[:, -tgt_len:]

        softmax_output = self.crit(pred_hid, labels, training=training)
        prediction_scores = softmax_output if labels is None else ()

        if not return_dict:
            return (prediction_scores,) + transformer_outputs[1:]

        return TFTransfoXLLMHeadModelOutput(
            prediction_scores=prediction_scores,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **model_kwargs):
        inputs = {}

        # if past is defined in model kwargs then use it for faster decoding
        if past_key_values:
            input_ids = tf.expand_dims(input_ids[:, -1], axis=-1)
        else:
            input_ids = input_ids

        return inputs


@add_start_docstrings(
    """
    The Transfo XL Model transformer with a sequence classification head on top (linear layer).

    [`TFTransfoXLForSequenceClassification`] uses the last token in order to do the classification, as other causal
    models (e.g. GPT-1,GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    TRANSFO_XL_START_DOCSTRING,
)
class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.score = tf.keras.layers.Dense(
            config.num_labels,
            kernel_initializer=get_initializer(config.init_range),
            name="score",
            use_bias=False,
        )
        self.transformer = TFTransfoXLMainLayer(config, name="transformer")

    def get_output_embeddings(self):
        # Remove after transformers v4.32. Fix this model's `test_model_common_attributes` test too.
        logger.warning(
            "Sequence classification models do not have output embeddings. `.get_output_embeddings` will be removed "
            "in transformers v4.32."
        )
        return self.transformer.word_emb

    @unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLSequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        mems: List[tf.Tensor] | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFTransfoXLSequenceClassifierOutputWithPast]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        transformer_outputs = self.transformer(
            input_ids=input_ids,
            mems=mems,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)
        in_logits = None
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (
                    tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1)
                    - 1
                )
                sequence_lengths = tf.where(sequence_lengths >= 0, sequence_lengths, input_ids.shape[-1] - 1)
                in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
            else:
                sequence_lengths = -1
                logger.warning(
                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )
        loss = None

        if labels is not None:
            if input_ids is not None:
                batch_size, sequence_length = shape_list(input_ids)[:2]
            else:
                batch_size, sequence_length = shape_list(inputs_embeds)[:2]
            assert (
                self.config.pad_token_id is not None or batch_size == 1
            ), "Cannot handle batch sizes > 1 if no padding token is defined."

            if not tf.is_tensor(sequence_lengths):
                in_logits = logits[0:batch_size, sequence_lengths]

            loss = self.hf_compute_loss(tf.reshape(labels, [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels]))

        pooled_logits = in_logits if in_logits is not None else logits

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

        return TFTransfoXLSequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            mems=transformer_outputs.mems,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )
