text: # 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
  
  import tensorflow as tf
  
  from ...file_utils import (
      ModelOutput,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
  )
  from ...modeling_tf_utils import (
      TFPreTrainedModel,
      TFSequenceClassificationLoss,
      get_initializer,
      input_processing,
      keras_serializable,
      shape_list,
  )
  from ...utils import logging
  from .configuration_transfo_xl import TransfoXLConfig
  from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask
  
  
  logger = logging.get_logger(__name__)
  
  _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="emb_layers_._{}".format(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="emb_projs_._{}".format(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="layers_._{}".format(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
  
      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,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              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,
              kwargs_call=kwargs,
          )
  
          # 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 inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif inputs["input_ids"] is not None:
              inputs["input_ids"] = tf.transpose(inputs["input_ids"], perm=(1, 0))
              qlen, bsz = shape_list(inputs["input_ids"])
          elif inputs["inputs_embeds"] is not None:
              inputs["inputs_embeds"] = tf.transpose(inputs["inputs_embeds"], perm=(1, 0, 2))
              qlen, bsz = shape_list(inputs["inputs_embeds"])[:2]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs["mems"] is None:
              inputs["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 inputs["head_mask"] is not None:
              raise NotImplementedError
          else:
              inputs["head_mask"] = [None] * self.n_layer
  
          if inputs["inputs_embeds"] is not None:
              word_emb = inputs["inputs_embeds"]
          else:
              word_emb = self.word_emb(inputs["input_ids"])
  
          mlen = shape_list(inputs["mems"][0])[0] if inputs["mems"] is not None else 0
          klen = mlen + qlen
  
          attn_mask = tf.ones([qlen, qlen])
          mask_u = tf.linalg.band_part(attn_mask, 0, -1)
          mask_dia = tf.linalg.band_part(attn_mask, 0, 0)
          attn_mask_pad = tf.zeros([qlen, mlen])
          dec_attn_mask = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
          if self.same_length:
              mask_l = tf.linalg.band_part(attn_mask, -1, 0)
              dec_attn_mask = tf.concat([dec_attn_mask[:, :qlen] + mask_l - mask_dia, dec_attn_mask[:, qlen:]], 1)
          # ::: 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]
  
          hids = []
          attentions = [] if inputs["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=inputs["training"])
              pos_emb = self.drop(pos_emb, training=inputs["training"])
  
              for i, layer in enumerate(self.layers):
                  hids.append(core_out)
                  mems_i = None if inputs["mems"] is None else inputs["mems"][i]
                  layer_outputs = layer(
                      core_out,
                      pos_emb,
                      dec_attn_mask,
                      mems_i,
                      inputs["head_mask"][i],
                      inputs["output_attentions"],
                      training=inputs["training"],
                  )
                  core_out = layer_outputs[0]
                  if inputs["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=inputs["training"])
  
          new_mems = self._update_mems(hids, inputs["mems"], mlen, qlen)
  
          # We transpose back here to shape [bsz, len, hidden_dim]
          core_out = tf.transpose(core_out, perm=(1, 0, 2))
  
          if inputs["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 inputs["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 inputs["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 (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
              Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see :obj:`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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
              shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
              sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      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 (:obj:`tf.Tensor` of shape `(batch_size, sequence_length-1)`, `optional`, returned when ``labels`` is provided)
              Language modeling losses (not reduced).
          prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).
          mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
              Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see :obj:`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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
              shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
              sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      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 (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
              Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see :obj:`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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
              shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
              sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      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 :class:`~transformers.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.
  
      .. note::
  
          TF 2.0 models accepts two formats as inputs:
  
          - having all inputs as keyword arguments (like PyTorch models), or
          - having all inputs as a list, tuple or dict in the first positional arguments.
  
          This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
          the tensors in the first argument of the model call function: :obj:`model(inputs)`.
  
          If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
          the first positional argument :
  
          - a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
          - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
            :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
          - a dictionary with one or several input Tensors associated to the input names given in the docstring:
            :obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
  
      Parameters:
          config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  TRANSFO_XL_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.BertTokenizer`. See
              :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
              Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
              :obj:`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 :obj:`input_ids` as they have already been computed.
          head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(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 (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          output_attentions (:obj:`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 (:obj:`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 (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
              argument can be used in eager mode, in graph mode the value will always be set to True.
          training (:obj:`bool`, `optional`, defaults to :obj:`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")
  
      @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="transfo-xl-wt103",
          output_type=TFTransfoXLModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      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,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              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,
              kwargs_call=kwargs,
          )
          outputs = self.transformer(
              input_ids=inputs["input_ids"],
              mems=inputs["mems"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          return outputs
  
      def serving_output(self, output):
          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)
  
      @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="transfo-xl-wt103",
          output_type=TFTransfoXLLMHeadModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          mems=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          inputs = input_processing(
              func=self.call,
              config=self.config,
              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,
              kwargs_call=kwargs,
          )
  
          if inputs["input_ids"] is not None:
              bsz, tgt_len = shape_list(inputs["input_ids"])[:2]
          else:
              bsz, tgt_len = shape_list(inputs["inputs_embeds"])[:2]
  
          transformer_outputs = self.transformer(
              inputs["input_ids"],
              inputs["mems"],
              inputs["head_mask"],
              inputs["inputs_embeds"],
              inputs["output_attentions"],
              inputs["output_hidden_states"],
              inputs["return_dict"],
              training=inputs["training"],
          )
  
          last_hidden = transformer_outputs[0]
          pred_hid = last_hidden[:, -tgt_len:]
  
          softmax_output = self.crit(pred_hid, labels, training=inputs["training"])
  
          if not inputs["return_dict"]:
              return (softmax_output,) + transformer_outputs[1:]
  
          return TFTransfoXLLMHeadModelOutput(
              prediction_scores=softmax_output,
              mems=transformer_outputs.mems,
              hidden_states=transformer_outputs.hidden_states,
              attentions=transformer_outputs.attentions,
          )
  
      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 TFTransfoXLLMHeadModelOutput(
              prediction_scores=output.prediction_scores,
              mems=tf.convert_to_tensor(output.mems),
              hidden_states=hs,
              attentions=attns,
          )
  
      def prepare_inputs_for_generation(self, inputs, past, **model_kwargs):
          inputs = {"input_ids": inputs}
  
          # if past is defined in model kwargs then use it for faster decoding
          if past:
              inputs["mems"] = past
  
          return inputs
  
  
  @add_start_docstrings(
      """
      The Transfo XL Model transformer with a sequence classification head on top (linear layer).
  
      :class:`~transformers.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
      :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
      row. If no :obj:`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 :obj:`inputs_embeds` are passed instead of :obj:`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
  
      @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="transfo-xl-wt103",
          output_type=TFTransfoXLSequenceClassifierOutputWithPast,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          mems=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
              config.vocab_size - 1]``.
          """
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              mems=mems,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              labels=labels,
              training=training,
              kwargs_call=kwargs,
          )
  
          transformer_outputs = self.transformer(
              input_ids=inputs["input_ids"],
              mems=inputs["mems"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          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 inputs["input_ids"] is not None:
                  sequence_lengths = (
                      tf.reduce_sum(
                          tf.cast(
                              tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id),
                              dtype=inputs["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 inputs["labels"] is not None:
              if input_ids is not None:
                  batch_size, sequence_length = shape_list(inputs["input_ids"])[:2]
              else:
                  batch_size, sequence_length = shape_list(inputs["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.compute_loss(
                  tf.reshape(inputs["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 inputs["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,
          )
  
      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 TFTransfoXLSequenceClassifierOutputWithPast(
              logits=output.logits, mems=tf.convert_to_tensor(output.mems), hidden_states=hs, attentions=attns
          )
  