code: # coding=utf-8
  # Copyright 2018 T5 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 T5 model. """
  
  
  import copy
  import itertools
  import logging
  import math
  
  import tensorflow as tf
  
  from .configuration_t5 import T5Config
  from .file_utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
  
  
  logger = logging.getLogger(__name__)
  
  TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "t5-small": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5",
      "t5-base": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-tf_model.h5",
      "t5-large": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-tf_model.h5",
      "t5-3b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-tf_model.h5",
      "t5-11b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-tf_model.h5",
  }
  
  ####################################################
  # TF 2.0 Models are constructed using Keras imperative API by sub-classing
  # - tf.keras.layers.Layer for the layers and
  # - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
  ####################################################
  
  
  class TFT5LayerNorm(tf.keras.layers.Layer):
      def __init__(self, epsilon=1e-6, **kwargs):
          """ Construct a layernorm module in the T5 style
              No bias and no substraction of mean.
          """
          super().__init__(**kwargs)
          self.variance_epsilon = epsilon
  
      def build(self, input_shape):
          """Build shared word embedding layer """
          self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones")
          super().build(input_shape)
  
      def call(self, x):
          variance = tf.math.reduce_mean(tf.math.square(x), axis=-1, keepdims=True)
          x = x * tf.math.rsqrt(variance + self.variance_epsilon)
          return self.weight * x
  
  
  class TFT5DenseReluDense(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi")
          self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name="wo")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
          self.act = tf.keras.activations.relu
  
      def call(self, hidden_states, training=False):
          h = self.wi(hidden_states)
          h = self.act(h)
          h = self.dropout(h, training=training)
          h = self.wo(h)
          return h
  
  
  class TFT5LayerFF(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.DenseReluDense = TFT5DenseReluDense(config, name="DenseReluDense")
          self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
      def call(self, hidden_states, training=False):
          norm_x = self.layer_norm(hidden_states)
          y = self.DenseReluDense(norm_x, training=training)
          layer_output = hidden_states + self.dropout(y, training=training)
          return layer_output
  
  
  class TFT5Attention(tf.keras.layers.Layer):
      NEW_ID = itertools.count()
  
      def __init__(self, config, has_relative_attention_bias=False, **kwargs):
          super().__init__(**kwargs)
          self.layer_id = next(TFT5Attention.NEW_ID)
          self.is_decoder = config.is_decoder
          self.has_relative_attention_bias = has_relative_attention_bias
  
          self.output_attentions = config.output_attentions
          self.relative_attention_num_buckets = config.relative_attention_num_buckets
          self.d_model = config.d_model
          self.d_kv = config.d_kv
          self.n_heads = config.num_heads
          self.inner_dim = self.n_heads * self.d_kv
  
          # Mesh TensorFlow initialization to avoid scaling before softmax
          self.q = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="q")
          self.k = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="k")
          self.v = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="v")
          self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name="o")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
          if self.has_relative_attention_bias:
              self.relative_attention_bias = tf.keras.layers.Embedding(
                  self.relative_attention_num_buckets, self.n_heads, name="relative_attention_bias"
              )
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      @staticmethod
      def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
          """
          Adapted from Mesh Tensorflow:
          https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
  
          Translate relative position to a bucket number for relative attention.
          The relative position is defined as memory_position - query_position, i.e.
          the distance in tokens from the attending position to the attended-to
          position.  If bidirectional=False, then positive relative positions are
          invalid.
          We use smaller buckets for small absolute relative_position and larger buckets
          for larger absolute relative_positions.  All relative positions >=max_distance
          map to the same bucket.  All relative positions <=-max_distance map to the
          same bucket.  This should allow for more graceful generalization to longer
          sequences than the model has been trained on.
          Args:
              relative_position: an int32 Tensor
              bidirectional: a boolean - whether the attention is bidirectional
              num_buckets: an integer
              max_distance: an integer
          Returns:
              a Tensor with the same shape as relative_position, containing int32
              values in the range [0, num_buckets)
          """
          ret = 0
          n = -relative_position
          if bidirectional:
              num_buckets //= 2
              ret += tf.dtypes.cast(tf.math.less(n, 0), tf.int32) * num_buckets
              n = tf.math.abs(n)
          else:
              n = tf.math.maximum(n, 0)
          # now n is in the range [0, inf)
          max_exact = num_buckets // 2
          is_small = tf.math.less(n, max_exact)
          val_if_large = max_exact + tf.dtypes.cast(
              tf.math.log(tf.dtypes.cast(n, tf.float32) / max_exact)
              / math.log(max_distance / max_exact)
              * (num_buckets - max_exact),
              tf.int32,
          )
          val_if_large = tf.math.minimum(val_if_large, num_buckets - 1)
          ret += tf.where(is_small, n, val_if_large)
          return ret
  
      def compute_bias(self, qlen, klen):
          """ Compute binned relative position bias """
          context_position = tf.range(qlen)[:, None]
          memory_position = tf.range(klen)[None, :]
          relative_position = memory_position - context_position  # shape (qlen, klen)
          rp_bucket = self._relative_position_bucket(
              relative_position, bidirectional=not self.is_decoder, num_buckets=self.relative_attention_num_buckets
          )
          values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
          values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0)  # shape (1, num_heads, qlen, klen)
          return values
  
      def call(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None, training=False):
          """
          Self-attention (if kv is None) or attention over source sentence (provided by kv).
          """
          # Input is (bs, qlen, dim)
          # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
          bs, qlen, dim = shape_list(input)
          if kv is None:
              klen = qlen if cache is None else cache["slen"] + qlen
          else:
              klen = shape_list(kv)[1]
  
          def shape(x):
              """  projection """
              return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, self.d_kv)), perm=(0, 2, 1, 3))
  
          def unshape(x):
              """  compute context """
              return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.inner_dim))
  
          q = shape(self.q(input))  # (bs, n_heads, qlen, dim_per_head)
          if kv is None:
              k = shape(self.k(input))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v(input))  # (bs, n_heads, qlen, dim_per_head)
          elif cache is None or self.layer_id not in cache:
              k = v = kv
              k = shape(self.k(k))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v(v))  # (bs, n_heads, qlen, dim_per_head)
  
          if cache is not None:
              if self.layer_id in cache:
                  if kv is None:
                      k_, v_ = cache[self.layer_id]
                      k = tf.concat([k_, k], axis=2)  # (bs, n_heads, klen, dim_per_head)
                      v = tf.concat([v_, v], axis=2)  # (bs, n_heads, klen, dim_per_head)
                  else:
                      k, v = cache[self.layer_id]
              cache[self.layer_id] = (k, v)
  
          # q = q / math.sqrt(dim_per_head)                                     # No scaling in T5
          # scores = tf.matmul(q, k, transpose_b=True)                            # (bs, n_heads, qlen, klen)
          scores = tf.einsum("bnqd,bnkd->bnqk", q, k)  # (bs, n_heads, qlen, klen)
  
          if position_bias is None:
              if not self.has_relative_attention_bias:
                  raise ValueError("No position_bias provided and no weights to compute position_bias")
              position_bias = self.compute_bias(qlen, klen)
              if mask is not None:
                  position_bias = position_bias + mask
                  # mask = (mask == 0).expand_as(scores)                              # (bs, n_heads, qlen, klen)
                  # scores.masked_fill_(mask, -float('inf'))                          # (bs, n_heads, qlen, klen)
  
          scores += position_bias
          weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
          weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
          context = unshape(context)  # (bs, qlen, dim)
  
          context = self.o(context)
  
          outputs = (context,)
          if self.output_attentions:
              outputs = outputs + (weights,)
          if self.has_relative_attention_bias:
              outputs = outputs + (position_bias,)
          return outputs
  
  
  class TFT5LayerSelfAttention(tf.keras.layers.Layer):
      def __init__(self, config, has_relative_attention_bias=False, **kwargs):
          super().__init__(**kwargs)
          self.SelfAttention = TFT5Attention(
              config, has_relative_attention_bias=has_relative_attention_bias, name="SelfAttention"
          )
          self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
      def call(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None, training=False):
          norm_x = self.layer_norm(hidden_states)
          attention_output = self.SelfAttention(
              norm_x, mask=attention_mask, position_bias=position_bias, head_mask=head_mask, training=training
          )
          y = attention_output[0]
          layer_output = hidden_states + self.dropout(y, training=training)
          outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
          return outputs
  
  
  class TFT5LayerCrossAttention(tf.keras.layers.Layer):
      def __init__(self, config, has_relative_attention_bias=False, **kwargs):
          super().__init__(**kwargs)
          self.EncDecAttention = TFT5Attention(
              config, has_relative_attention_bias=has_relative_attention_bias, name="EncDecAttention"
          )
          self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
      def call(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None, training=False):
          norm_x = self.layer_norm(hidden_states)
          attention_output = self.EncDecAttention(
              norm_x, mask=attention_mask, kv=kv, position_bias=position_bias, head_mask=head_mask, training=training
          )
          y = attention_output[0]
          layer_output = hidden_states + self.dropout(y, training=training)
          outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
          return outputs
  
  
  class TFT5Block(tf.keras.layers.Layer):
      def __init__(self, config, has_relative_attention_bias=False, **kwargs):
          super().__init__(**kwargs)
          self.is_decoder = config.is_decoder
          self.layer = []
          self.layer.append(
              TFT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias, name="layer_._0")
          )
          if self.is_decoder:
              self.layer.append(
                  TFT5LayerCrossAttention(
                      config, has_relative_attention_bias=has_relative_attention_bias, name="layer_._1"
                  )
              )
              self.layer.append(TFT5LayerFF(config, name="layer_._2"))
          else:
              self.layer.append(TFT5LayerFF(config, name="layer_._1"))
  
      def call(
          self,
          hidden_states,
          attention_mask=None,
          position_bias=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          encoder_decoder_position_bias=None,
          head_mask=None,
          training=False,
      ):
          self_attention_outputs = self.layer[0](
              hidden_states,
              attention_mask=attention_mask,
              position_bias=position_bias,
              head_mask=head_mask,
              training=training,
          )
          hidden_states = self_attention_outputs[0]
          outputs = self_attention_outputs[1:]
  
          if not self.is_decoder:
              hidden_states = self.layer[1](hidden_states, training=training)
          else:
              cross_attention_outputs = self.layer[1](
                  hidden_states,
                  kv=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  position_bias=encoder_decoder_position_bias,
                  head_mask=head_mask,
                  training=training,
              )
              hidden_states = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:]
              hidden_states = self.layer[2](hidden_states, training=training)
  
          outputs = (hidden_states,) + outputs  # add attentions if we output them
          return outputs  # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
  
  
  class _NoLayerEmbedTokens(object):
      """
       this class wraps a the TFSharedEmbeddingTokens layer into a python 'no-keras-layer'
       class to avoid problem with weight restoring. Also it makes sure that the layer is
       called from the correct scope to avoid problem with saving/storing the correct weights
      """
  
      def __init__(self, layer, abs_scope_name=None):
          self._layer = layer
          self._abs_scope_name = abs_scope_name
  
      def call(self, inputs, mode="embedding"):
          if self._abs_scope_name is None:
              return self._layer.call(inputs, mode)
  
          # if an abs scope name is given to the embedding variable, call variable from absolute scope
          with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name:
              with tf.name_scope(abs_scope_name.original_name_scope):
                  return self._layer.call(inputs, mode)
  
      def __call__(self, inputs, mode="embedding"):
          if self._abs_scope_name is None:
              return self._layer(inputs, mode)
  
          # if an abs scope name is given to the embedding variable, call variable from absolute scope
          with tf.compat.v1.variable_scope(self._abs_scope_name, auxiliary_name_scope=False) as abs_scope_name:
              with tf.name_scope(abs_scope_name.original_name_scope):
                  return self._layer(inputs, mode)
  
  
  ####################################################
  # The full model without a specific pretrained or finetuning head is
  # provided as a tf.keras.layers.Layer usually called "TFT5MainLayer"
  ####################################################
  class TFT5MainLayer(tf.keras.layers.Layer):
      def __init__(self, config, embed_tokens=None, **kwargs):
          super().__init__(**kwargs)
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
  
          self.embed_tokens = embed_tokens
          self.is_decoder = config.is_decoder
  
          self.config = config
          self.num_hidden_layers = config.num_layers
  
          self.block = [
              TFT5Block(config, has_relative_attention_bias=bool(i == 0), name="block_._{}".format(i))
              for i in range(config.num_layers)
          ]
          self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="final_layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
      def get_input_embeddings(self):
          return self.embed_tokens
  
      def get_output_embeddings(self):
          return self.embed_tokens
  
      def set_embed_tokens(self, embed_tokens):
          self.embed_tokens = embed_tokens
  
      def _resize_token_embeddings(self, new_num_tokens):
          raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
  
      def _prune_heads(self, heads_to_prune):
          raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
  
      def call(
          self,
          input_ids,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          inputs_embeds=None,
          head_mask=None,
          training=False,
      ):
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = shape_list(input_ids)
              input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
          elif inputs_embeds is not None:
              input_shape = shape_list(inputs_embeds)[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs_embeds is None:
              assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
              inputs_embeds = self.embed_tokens(input_ids)
  
          batch_size, seq_length = input_shape
  
          if attention_mask is None:
              attention_mask = tf.fill((batch_size, seq_length), 1)
          if self.is_decoder and encoder_attention_mask is None:
              encoder_seq_length = encoder_hidden_states.shape[1]
              encoder_attention_mask = tf.fill((batch_size, encoder_seq_length), 1)
  
          # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
          # ourselves in which case we just need to make it broadcastable to all heads.
          attention_mask = tf.cast(attention_mask, dtype=tf.float32)
          num_dims_attention_mask = len(shape_list(attention_mask))
          if num_dims_attention_mask == 3:
              extended_attention_mask = attention_mask[:, None, :, :]
          elif num_dims_attention_mask == 2:
              # Provided a padding mask of dimensions [batch_size, seq_length]
              # - if the model is a decoder, apply a causal mask in addition to the padding mask
              # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
              if self.config.is_decoder:
                  seq_ids = tf.range(seq_length)
                  causal_mask = tf.less_equal(
                      tf.tile(seq_ids[None, None, :], (batch_size, seq_length, 1)), seq_ids[None, :, None]
                  )
                  causal_mask = tf.cast(causal_mask, dtype=tf.float32)
                  extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
              else:
                  extended_attention_mask = attention_mask[:, None, None, :]
  
          # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
          # masked positions, this operation will create a tensor which is 0.0 for
          # positions we want to attend and -10000.0 for masked positions.
          # Since we are adding it to the raw scores before the softmax, this is
          # effectively the same as removing these entirely.
  
          # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
          # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
          # extended_attention_mask = tf.math.equal(extended_attention_mask,
          #                                         tf.transpose(extended_attention_mask, perm=(-1, -2)))
  
          extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
  
          if self.is_decoder:
              # If a 2D ou 3D attention mask is provided for the cross-attention
              # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
              encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=tf.float32)
              num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
              if num_dims_encoder_attention_mask == 3:
                  encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
              if num_dims_encoder_attention_mask == 2:
                  encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
  
              # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
              # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
              # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
              #                                         tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
  
              encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
          else:
              encoder_extended_attention_mask = None
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          if head_mask is not None:
              raise NotImplementedError
          else:
              head_mask = [None] * self.num_hidden_layers
              # head_mask = tf.constant([0] * self.num_hidden_layers)
  
          all_hidden_states = ()
          all_attentions = ()
          position_bias = None
          encoder_decoder_position_bias = None
  
          hidden_states = inputs_embeds
          for i, layer_module in enumerate(self.block):
              if self.output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_outputs = layer_module(
                  hidden_states,
                  attention_mask=extended_attention_mask,
                  position_bias=position_bias,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_extended_attention_mask,
                  encoder_decoder_position_bias=encoder_decoder_position_bias,
                  head_mask=head_mask[i],
                  training=training,
              )
              hidden_states = layer_outputs[0]
              if i == 0:
                  # We share the position biases between the layers - the first layer store them
                  # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
                  position_bias = layer_outputs[2 if self.output_attentions else 1]
                  if self.is_decoder:
                      encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2]
  
              if self.output_attentions:
                  all_attentions = all_attentions + (layer_outputs[1],)
  
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
  
          # Add last layer
          if self.output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          outputs = (hidden_states,)
          if self.output_hidden_states:
              outputs = outputs + (all_hidden_states,)
          if self.output_attentions:
              outputs = outputs + (all_attentions,)
          return outputs  # last-layer hidden state, (all hidden states), (all attentions)
  
  
  ####################################################
  # TFT5PreTrainedModel is a sub-class of tf.keras.Model
  # which take care of loading and saving pretrained weights
  # and various common utilities.
  # Here you just need to specify a few (self-explanatory)
  # pointers for your model.
  ####################################################
  class TFT5PreTrainedModel(TFPreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for downloading and loading pretrained models.
      """
  
      config_class = T5Config
      pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
      base_model_prefix = "transformer"
  
      @property
      def dummy_inputs(self):
          input_ids = tf.constant(DUMMY_INPUTS)
          input_mask = tf.constant(DUMMY_MASK)
          dummy_inputs = {
              "decoder_input_ids": input_ids,
              "input_ids": input_ids,
              "decoder_attention_mask": input_mask,
          }
          return dummy_inputs
  
  
  T5_START_DOCSTRING = r"""    The T5 model was proposed in
      `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
      by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
      It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
  
      This model is a tf.keras.Model `tf.keras.Model`_ sub-class. 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.
  
      .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
          https://arxiv.org/abs/1910.10683
  
      .. _`tf.keras.Model`:
          https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
  
      Note on the model inputs:
          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 usefull 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 associaed to the input names given in the docstring:
              `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
  
      Parameters:
          config (:class:`~transformers.T5Config`): 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.
  """
  
  T5_INPUTS_DOCSTRING = r"""
      Args:
          decoder_input_ids are usually used as a `dict` (see T5 description above for more information) containing all the following.
          decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
              Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
  
          input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
              To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
  
              (a) For sequence pairs:
  
                  ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
  
              (b) For single sequences:
  
                  ``tokens:         [CLS] the dog is hairy . [SEP]``
  
              T5 is a model with relative position embeddings so you should be able to pad the inputs on
              the right or the left.
  
              Indices can be obtained using :class:`transformers.T5Tokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
          attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Mask to avoid performing attention on padding token indices.
              Mask values selected in ``[0, 1]``:
              ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
          encoder_outputs (tuple(:obj:`tuple(tf.FloatTensor)`, `optional`, defaults to :obj:`None`):
              Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
              `last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`) is a sequence of hidden-states at the output of the last layer of the encoder.
              Used in the cross-attention of the decoder.
          decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
              Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
          inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          decoder_inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
              Mask to nullify selected heads of the self-attention modules.
              Mask values selected in ``[0, 1]``:
              ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
  """
  
  
  @add_start_docstrings(
      "The bare T5 Model transformer outputting raw hidden-states" "without any specific head on top.",
      T5_START_DOCSTRING,
  )
  class TFT5Model(TFT5PreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
  
          # retrieve correct absolute scope for embed token wrapper
          with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
              pass
  
          embed_tokens = _NoLayerEmbedTokens(self.shared, abs_scope_name=shared_abs_scope_name)
  
          encoder_config = copy.deepcopy(config)
          self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
  
      def get_input_embeddings(self):
          return self.shared
  
      def get_output_embeddings(self):
          return self.shared
  
      @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
      def call(self, decoder_input_ids, **kwargs):
          r"""
      Return:
          :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
          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.
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned 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 ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
                  :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import T5Tokenizer, TFT5Model
  
          tokenizer = T5Tokenizer.from_pretrained('t5-small')
          model = TFT5Model.from_pretrained('t5-small')
          input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
          outputs = model(input_ids, input_ids=input_ids)
          last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
  
          """
  
          if isinstance(decoder_input_ids, dict):
              kwargs.update(decoder_input_ids)
          else:
              kwargs["decoder_input_ids"] = decoder_input_ids
  
          # retrieve arguments
          input_ids = kwargs.get("input_ids", None)
          decoder_input_ids = kwargs.get("decoder_input_ids", None)
          attention_mask = kwargs.get("attention_mask", None)
          encoder_outputs = kwargs.get("encoder_outputs", None)
          decoder_attention_mask = kwargs.get("decoder_attention_mask", None)
          inputs_embeds = kwargs.get("inputs_embeds", None)
          decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None)
          head_mask = kwargs.get("head_mask", None)
  
          # Encode if needed (training, first prediction pass)
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask
              )
  
          hidden_states = encoder_outputs[0]
  
          # Decode
          decoder_outputs = self.decoder(
              decoder_input_ids,
              attention_mask=decoder_attention_mask,
              inputs_embeds=decoder_inputs_embeds,
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=attention_mask,
              head_mask=head_mask,
          )
  
          return decoder_outputs + encoder_outputs
  
  
  @add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING)
  class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.model_dim = config.d_model
  
          self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
  
          # retrieve correct absolute scope for embed token wrapper
          with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
              pass
  
          embed_tokens = _NoLayerEmbedTokens(self.shared, abs_scope_name=shared_abs_scope_name)
  
          encoder_config = copy.deepcopy(config)
          self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
  
      def get_input_embeddings(self):
          return self.shared
  
      def get_output_embeddings(self):
          return self.shared
  
      def get_encoder(self):
          return self.encoder
  
      @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
      def call(self, decoder_input_ids, **kwargs):
          r"""
          lm_labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for computing the sequence classification/regression loss.
              Indices should be in :obj:`[0, ..., config.vocab_size - 1]`.
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
  
      Return:
          :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_label` is provided):
              Classification loss (cross entropy).
          prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned 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 ``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.
  
      Examples::
  
          from transformers import T5Tokenizer, TFT5ForConditionalGeneration
  
          tokenizer = T5Tokenizer.from_pretrained('t5-small')
          model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
          input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
          outputs = model(input_ids, input_ids=input_ids, lm_labels=input_ids)
          prediction_scores = outputs[:1]  # TODO: TFT5 still needs to implement
  
          tokenizer = T5Tokenizer.from_pretrained('t5-small')
          model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
          input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="tf")  # Batch size 1
          model.generate(input_ids)
  
          """
  
          if isinstance(decoder_input_ids, dict):
              kwargs.update(decoder_input_ids)
          else:
              kwargs["decoder_input_ids"] = decoder_input_ids
  
          # retrieve arguments
          input_ids = kwargs.get("input_ids", None)
          decoder_input_ids = kwargs.get("decoder_input_ids", None)
          attention_mask = kwargs.get("attention_mask", None)
          encoder_outputs = kwargs.get("encoder_outputs", None)
          decoder_attention_mask = kwargs.get("decoder_attention_mask", None)
          inputs_embeds = kwargs.get("inputs_embeds", None)
          decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None)
          head_mask = kwargs.get("head_mask", None)
  
          # Encode if needed (training, first prediction pass)
          if encoder_outputs is None:
              # Convert encoder inputs in embeddings if needed
              encoder_outputs = self.encoder(
                  input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask
              )
  
          hidden_states = encoder_outputs[0]
  
          # Decode
          decoder_outputs = self.decoder(
              decoder_input_ids,
              attention_mask=decoder_attention_mask,
              inputs_embeds=decoder_inputs_embeds,
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=attention_mask,
              head_mask=head_mask,
          )
  
          # TODO (thom / patrick): add lm_labels for loss function
          sequence_output = decoder_outputs[0] * (self.model_dim ** -0.5)
          embed_tokens = self.get_output_embeddings()
          lm_logits = embed_tokens(sequence_output, mode="linear")
          decoder_outputs = (lm_logits,) + decoder_outputs[1:]
  
          return decoder_outputs + encoder_outputs
  
      def prepare_inputs_for_generation(self, input_ids, past, attention_mask, **kwargs):
          assert past is not None, "past has to be defined for encoder_outputs"
  
          # first step
          if type(past) is tuple:
              encoder_outputs = past
          else:
              encoder_outputs = (past,)
  
          return {
              "inputs": input_ids,
              "encoder_outputs": encoder_outputs,
              "attention_mask": attention_mask,
          }
  
      def _reorder_cache(self, past, beam_idx):
          # past does not have to be re-ordered for T5.
          return past
  