FM / transfo

/usr/local/lib/python3.6/site-packages/transformers/models/transfo_xl/

/usr/local/lib/python3.6/site-packages/transformers/models/transfo_xl

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_transfo_xl.py	7860	0644	edit dl rm
convert_transfo_xl_original_tf_checkpoint_to_pytorch.py	4888	0644	edit dl rm
modeling_tf_transfo_xl.py	46168	0644	edit dl rm
modeling_tf_transfo_xl_utilities.py	7577	0644	edit dl rm
modeling_transfo_xl.py	55711	0644	edit dl rm
modeling_transfo_xl_utilities.py	10678	0644	edit dl rm
tokenization_transfo_xl.py	30534	0644	edit dl rm
__init__.py	2825	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/transfo_xl/modeling_tf_transfo_xl.py (46168B)

# 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 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
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"
_TOKENIZER_FOR_DOC = "TransfoXLTokenizer"

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 = tf.nn.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=None,
        mems=None,
        head_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        training=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

# ::: PyTorch masking code for reference :::
        # if self.same_length:
        #     all_ones = word_emb.new_ones((qlen, klen), dtype=torch.uint8)
        #     mask_len = klen - self.mem_len
        #     if mask_len > 0:
        #         mask_shift_len = qlen - mask_len
        #     else:
        #         mask_shift_len = qlen
        #     dec_attn_mask = (torch.triu(all_ones, 1+mlen)
        #             + torch.tril(all_ones, -mask_shift_len))[:, :, None] # -1
        # else:
        #     dec_attn_mask = torch.triu(
        #         word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1+mlen)[:,:,None]

# TensorFlow version
        dec_attn_mask = 1 - tf.linalg.band_part(
            tf.ones([qlen, klen], dtype=tf.int32), -1, mlen
        )  # (q, q): diagonal with 1's
        if self.same_length:
            mask_len = klen - self.mem_len
            if mask_len > 0:
                mask_shift_len = qlen - mask_len
            else:
                mask_shift_len = qlen
            if mask_shift_len >= 1:
                dec_attn_mask += 1 - tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), mask_shift_len - 1, -1)
            else:
                dec_attn_mask += tf.linalg.band_part(tf.ones([qlen, klen], dtype=tf.int32), -1, -mask_shift_len)

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"

@tf.function(
        input_signature=[
            {
                "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
            }
        ]
    )
    def serving(self, inputs):
        output = self.call(inputs)

return self.serving_output(output)

@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: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = 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)`.

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: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = 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)`.

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

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

TF 2.0 models accepts two formats as inputs:

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

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

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

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

</Tip>

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 [`BertTokenizer`]. 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")

return outputs

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

return TFTransfoXLModelOutput(
            last_hidden_state=output.last_hidden_state,
            mems=tf.convert_to_tensor(output.mems),
            hidden_states=hs,
            attentions=attns,
        )

@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)

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,
        )

return TFTransfoXLLMHeadModelOutput(
            prediction_scores=output.prediction_scores,
            mems=tf.convert_to_tensor(output.mems),
            hidden_states=hs,
            attentions=attns,
        )

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

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

return inputs

@staticmethod
    def _reorder_cache(mems: List[tf.Tensor], beam_idx: tf.Tensor) -> List[tf.Tensor]:
        return [tf.gather(layer_past, beam_idx, axis=1) for layer_past in mems]

@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):
        return self.transformer.word_emb

@unpack_inputs
    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLSequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        mems: Optional[List[tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> 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.reduce_sum(
                        tf.cast(
                            tf.math.not_equal(input_ids, self.config.pad_token_id),
                            dtype=input_ids.dtype,
                        ),
                        -1,
                        keepdims=False,
                    )
                    - 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 "
                    f"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,
        )

return TFTransfoXLSequenceClassifierOutputWithPast(
            logits=output.logits, mems=tf.convert_to_tensor(output.mems), hidden_states=hs, attentions=attns
        )

cmd: