text: # coding=utf-8
  # Copyright 2020 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 math
  import warnings
  from typing import Tuple
  
  import numpy as np
  import tensorflow as tf
  from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
  
  from ...activations_tf import get_tf_activation
  from ...file_utils import (
      DUMMY_INPUTS,
      DUMMY_MASK,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFBaseModelOutputWithPastAndCrossAttentions,
      TFSeq2SeqLMOutput,
      TFSeq2SeqModelOutput,
  )
  from ...modeling_tf_utils import (
      TFCausalLanguageModelingLoss,
      TFPreTrainedModel,
      TFSharedEmbeddings,
      TFWrappedEmbeddings,
      input_processing,
      keras_serializable,
  )
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_t5 import T5Config
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "T5Config"
  _TOKENIZER_FOR_DOC = "T5Tokenizer"
  
  TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "t5-small",
      "t5-base",
      "t5-large",
      "t5-3b",
      "t5-11b",
      # See all T5 models at https://huggingface.co/models?filter=t5
  ]
  
  ####################################################
  # 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 subtraction 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, hidden_states):
          variance = tf.math.reduce_mean(tf.math.square(hidden_states), axis=-1, keepdims=True)
          hidden_states = hidden_states * tf.math.rsqrt(variance + self.variance_epsilon)
          return self.weight * hidden_states
  
  
  class TFT5DenseReluDense(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          wi_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (config.d_model**-0.5)
          )
          wo_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (config.d_ff**-0.5)
          )
          self.wi = tf.keras.layers.Dense(
              config.d_ff, use_bias=False, name="wi", kernel_initializer=wi_initializer
          )  # Update init weights as in flax
          self.wo = tf.keras.layers.Dense(
              config.d_model, use_bias=False, name="wo", kernel_initializer=wo_initializer
          )  # Update init weights as in flax
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
          self.act = tf.keras.activations.relu
  
      def call(self, hidden_states, training=False):
          hidden_states = self.wi(hidden_states)
          hidden_states = self.act(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = self.wo(hidden_states)
          return hidden_states
  
  
  class TFT5GatedGeluDense(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          wi_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (config.d_model**-0.5)
          )
          wo_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (config.d_ff**-0.5)
          )
          self.wi_0 = tf.keras.layers.Dense(
              config.d_ff, use_bias=False, name="wi_0", kernel_initializer=wi_initializer
          )  # Update init weights as in flax
          self.wi_1 = tf.keras.layers.Dense(
              config.d_ff, use_bias=False, name="wi_1", kernel_initializer=wi_initializer
          )  # Update init weights as in flax
          self.wo = tf.keras.layers.Dense(
              config.d_model, use_bias=False, name="wo", kernel_initializer=wo_initializer
          )  # Update init weights as in flax
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
          self.act = get_tf_activation("gelu_new")
  
      def call(self, hidden_states, training=False):
          hidden_gelu = self.act(self.wi_0(hidden_states))
          hidden_linear = self.wi_1(hidden_states)
          hidden_states = hidden_gelu * hidden_linear
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = self.wo(hidden_states)
          return hidden_states
  
  
  class TFT5LayerFF(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          if config.feed_forward_proj == "relu":
              self.DenseReluDense = TFT5DenseReluDense(config, name="DenseReluDense")
          elif config.feed_forward_proj == "gated-gelu":
              self.DenseReluDense = TFT5GatedGeluDense(config, name="DenseReluDense")
          else:
              raise ValueError(
                  f"{self.config.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
              )
          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):
          normed_hidden_states = self.layer_norm(hidden_states)
          dense_output = self.DenseReluDense(normed_hidden_states, training=training)
          hidden_states = hidden_states + self.dropout(dense_output, training=training)
          return hidden_states
  
  
  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.use_cache = config.use_cache
          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.relative_attention_max_distance = config.relative_attention_max_distance
          self.d_model = config.d_model
          self.key_value_proj_dim = config.d_kv
          self.n_heads = config.num_heads
          self.inner_dim = self.n_heads * self.key_value_proj_dim
  
          # Mesh TensorFlow initialization to avoid scaling before softmax
          q_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
          )
          k_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (self.inner_dim**-0.5)
          )
          v_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (self.inner_dim**-0.5)
          )
          o_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (self.inner_dim**-0.5)
          )
          self.relative_attention_bias_initializer = tf.keras.initializers.RandomNormal(
              mean=0, stddev=config.initializer_factor * (self.inner_dim**-0.5)
          )
  
          self.q = tf.keras.layers.Dense(
              self.inner_dim, use_bias=False, name="q", kernel_initializer=q_initializer
          )  # Update init weights as in flax
          self.k = tf.keras.layers.Dense(
              self.inner_dim, use_bias=False, name="k", kernel_initializer=k_initializer
          )  # Update init weights as in flax
          self.v = tf.keras.layers.Dense(
              self.inner_dim, use_bias=False, name="v", kernel_initializer=v_initializer
          )  # Update init weights as in flax
          self.o = tf.keras.layers.Dense(
              self.d_model, use_bias=False, name="o", kernel_initializer=o_initializer
          )  # Update init weights as in flax
          self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
  
          self.pruned_heads = set()
  
      def build(self, input_shape):
          if self.has_relative_attention_bias:
              with tf.name_scope("relative_attention_bias"):
                  self.relative_attention_bias = self.add_weight(
                      name="embeddings",
                      shape=[self.relative_attention_num_buckets, self.n_heads],
                      initializer=self.relative_attention_bias_initializer,  # Add initializer
                  )
  
          return super().build(input_shape)
  
      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)
          """
          relative_buckets = 0
          #        n = -relative_position
          if bidirectional:
              num_buckets //= 2
              relative_buckets += (
                  tf.cast(tf.math.greater(relative_position, 0), dtype=relative_position.dtype) * num_buckets
              )
              relative_position = tf.math.abs(relative_position)
          else:
              relative_position = -tf.math.minimum(relative_position, 0)
          # now n is in the range [0, inf)
          max_exact = num_buckets // 2
          is_small = tf.math.less(relative_position, max_exact)
          relative_position_if_large = max_exact + tf.cast(
              tf.math.log(relative_position / max_exact)
              / math.log(max_distance / max_exact)
              * (num_buckets - max_exact),
              dtype=relative_position.dtype,
          )
          relative_position_if_large = tf.math.minimum(relative_position_if_large, num_buckets - 1)
          relative_buckets += tf.where(is_small, relative_position, relative_position_if_large)
          return relative_buckets
  
      def compute_bias(self, query_length, key_length):
          """Compute binned relative position bias"""
          context_position = tf.range(query_length)[:, None]
          memory_position = tf.range(key_length)[None, :]
          relative_position = memory_position - context_position  # shape (query_length, key_length)
          relative_position_bucket = self._relative_position_bucket(
              relative_position,
              bidirectional=(not self.is_decoder),
              num_buckets=self.relative_attention_num_buckets,
              max_distance=self.relative_attention_max_distance,
          )
          values = tf.gather(
              self.relative_attention_bias, relative_position_bucket
          )  # shape (query_length, key_length, num_heads)
          values = tf.expand_dims(
              tf.transpose(values, [2, 0, 1]), axis=0
          )  # shape (1, num_heads, query_length, key_length)
          return values
  
      def call(
          self,
          hidden_states,
          mask=None,
          key_value_states=None,
          position_bias=None,
          past_key_value=None,
          layer_head_mask=None,
          query_length=None,
          use_cache=False,
          training=False,
          output_attentions=False,
      ):
          """
          Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
          """
          # Input is (batch_size, query_length, dim)
          # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
          # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
          batch_size, seq_length = shape_list(hidden_states)[:2]
  
          real_seq_length = seq_length
  
          if past_key_value is not None:
              assert (
                  len(past_key_value) == 2
              ), f"past_key_value should have 2 past states: keys and values. Got {len(past_key_value)} past states"
              real_seq_length += shape_list(past_key_value[0])[2] if query_length is None else query_length
  
          key_length = real_seq_length if key_value_states is None else shape_list(key_value_states)[1]
  
          def shape(hidden_states):
              """projection"""
              return tf.transpose(
                  tf.reshape(hidden_states, (batch_size, -1, self.n_heads, self.key_value_proj_dim)), perm=(0, 2, 1, 3)
              )
  
          def unshape(hidden_states):
              """compute context"""
              return tf.reshape(tf.transpose(hidden_states, perm=(0, 2, 1, 3)), (batch_size, -1, self.inner_dim))
  
          def project(hidden_states, proj_layer, key_value_states, past_key_value):
              """projects hidden states correctly to key/query states"""
              if key_value_states is None:
                  # self-attn
                  # (batch_size, n_heads, seq_length, dim_per_head)
                  hidden_states = shape(proj_layer(hidden_states))
              elif past_key_value is None:
                  # cross-attn
                  # (batch_size, n_heads, seq_length, dim_per_head)
                  hidden_states = shape(proj_layer(key_value_states))
  
              if past_key_value is not None:
                  if key_value_states is None:
                      # self-attn
                      # (batch_size, n_heads, key_length, dim_per_head)
                      hidden_states = tf.concat([past_key_value, hidden_states], axis=2)
                  else:
                      # cross-attn
                      hidden_states = past_key_value
              return hidden_states
  
          # get query
          query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, query_length, dim_per_head)
  
          # get key/value
          key_states = project(
              hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
          )
          value_states = project(
              hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
          )
  
          # to cope with keras serialization
          if self.is_decoder and use_cache:
              present_key_value_state = (key_states, value_states)
          else:
              present_key_value_state = None
  
          scores = tf.einsum(
              "bnqd,bnkd->bnqk", query_states, key_states
          )  # (batch_size, n_heads, query_length, key_length)
  
          if position_bias is None:
              if not self.has_relative_attention_bias:
                  position_bias = tf.zeros((1, self.n_heads, real_seq_length, key_length))
              else:
                  position_bias = self.compute_bias(real_seq_length, key_length)
  
              # if key and values are already calculated
              # we want only the last query position bias
              if past_key_value is not None:
                  position_bias = position_bias[:, :, -seq_length:, :]
  
              if mask is not None:
                  position_bias = tf.cast(position_bias, dtype=mask.dtype)
                  position_bias = position_bias + mask  # (batch_size, n_heads, query_length, key_length)
  
          scores += position_bias
          weights = tf.nn.softmax(scores, axis=-1)  # (batch_size, n_heads, query_length, key_length)
          weights = self.dropout(weights, training=training)  # (batch_size, n_heads, query_length, key_length)
  
          # Mask heads if we want to
          if layer_head_mask is not None:
              tf.debugging.assert_equal(
                  shape_list(layer_head_mask),
                  [self.n_heads],
                  message=f"Head mask for a single layer should be of size {(self.n_heads)}, but is {shape_list(layer_head_mask)}",
              )
              weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * weights
  
          attn_output = tf.matmul(weights, value_states)  # (batch_size, n_heads, query_length, dim_per_head)
  
          attn_output = self.o(unshape(attn_output))
  
          outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
  
          if output_attentions:
              outputs = outputs + (weights,)
  
          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,
          layer_head_mask=None,
          past_key_value=None,
          use_cache=False,
          output_attentions=False,
          training=False,
      ):
          normed_hidden_states = self.layer_norm(hidden_states)
          attention_output = self.SelfAttention(
              normed_hidden_states,
              mask=attention_mask,
              position_bias=position_bias,
              layer_head_mask=layer_head_mask,
              past_key_value=past_key_value,
              use_cache=use_cache,
              output_attentions=output_attentions,
              training=training,
          )
          hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
          outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
          return outputs
  
  
  class TFT5LayerCrossAttention(tf.keras.layers.Layer):
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
          self.EncDecAttention = TFT5Attention(
              config,
              has_relative_attention_bias=False,
              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,
          key_value_states,
          attention_mask=None,
          position_bias=None,
          layer_head_mask=None,
          past_key_value=None,
          query_length=None,
          use_cache=False,
          output_attentions=False,
          training=False,
      ):
          normed_hidden_states = self.layer_norm(hidden_states)
          attention_output = self.EncDecAttention(
              normed_hidden_states,
              mask=attention_mask,
              key_value_states=key_value_states,
              position_bias=position_bias,
              layer_head_mask=layer_head_mask,
              past_key_value=past_key_value,
              query_length=query_length,
              use_cache=use_cache,
              output_attentions=output_attentions,
              training=training,
          )
          hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
          outputs = (hidden_states,) + 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,
                      name="layer_._1",
                  )
              )
  
          self.layer.append(TFT5LayerFF(config, name=f"layer_._{len(self.layer)}"))
  
      def call(
          self,
          hidden_states,
          attention_mask=None,
          position_bias=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          encoder_decoder_position_bias=None,
          layer_head_mask=None,
          encoder_layer_head_mask=None,
          past_key_value=None,
          use_cache=False,
          output_attentions=False,
          training=False,
      ):
  
          if past_key_value is not None:
              assert self.is_decoder, "Only decoder can use `past_key_values`"
              expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
  
              if len(past_key_value) != expected_num_past_key_values:
                  raise ValueError(
                      f"There should be {expected_num_past_key_values} past states. "
                      f"{'2 (past / key) for cross attention' if expected_num_past_key_values == 4 else ''}."
                      f"Got {len(past_key_value)} past key / value states"
                  )
  
              self_attn_past_key_value = past_key_value[:2]
              cross_attn_past_key_value = past_key_value[2:]
          else:
              self_attn_past_key_value, cross_attn_past_key_value = None, None
  
          self_attention_outputs = self.layer[0](
              hidden_states,
              attention_mask=attention_mask,
              position_bias=position_bias,
              layer_head_mask=layer_head_mask,
              past_key_value=self_attn_past_key_value,
              use_cache=use_cache,
              output_attentions=output_attentions,
              training=training,
          )
          hidden_states, present_key_value_state = self_attention_outputs[:2]
          attention_outputs = self_attention_outputs[2:]  # Keep self-attention outputs and relative position weights
  
          if self.is_decoder and encoder_hidden_states is not None:
              # the actual query length is unknown for cross attention
              # if using past key value states. Need to inject it here
              if present_key_value_state is not None:
                  query_length = shape_list(present_key_value_state[0])[2]
              else:
                  query_length = None
  
              cross_attention_outputs = self.layer[1](
                  hidden_states,
                  key_value_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  position_bias=encoder_decoder_position_bias,
                  layer_head_mask=encoder_layer_head_mask,
                  past_key_value=cross_attn_past_key_value,
                  query_length=query_length,
                  use_cache=use_cache,
                  output_attentions=output_attentions,
                  training=training,
              )
              hidden_states = cross_attention_outputs[0]
              # Combine self attn and cross attn key value states
              if present_key_value_state is not None:
                  present_key_value_state = present_key_value_state + cross_attention_outputs[1]
  
              # Keep cross-attention outputs and relative position weights
              attention_outputs = attention_outputs + cross_attention_outputs[2:]
  
          # Apply Feed Forward layer
          hidden_states = self.layer[-1](hidden_states, training=training)
          outputs = (hidden_states,)
  
          # Add attentions if we output them
          outputs = outputs + (present_key_value_state,) + attention_outputs
          return outputs  # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
  
  
  ####################################################
  # The full model without a specific pretrained or finetuning head is
  # provided as a tf.keras.layers.Layer usually called "TFT5MainLayer"
  ####################################################
  @keras_serializable
  class TFT5MainLayer(tf.keras.layers.Layer):
      config_class = T5Config
  
      def __init__(self, config, embed_tokens=None, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
          self.output_hidden_states = config.output_hidden_states
          self.output_attentions = config.output_attentions
          self.use_cache = config.use_cache
  
          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=f"block_._{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 _prune_heads(self, heads_to_prune):
          raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
  
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          inputs_embeds=None,
          head_mask=None,
          encoder_head_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ) -> Tuple:
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              inputs_embeds=inputs_embeds,
              head_mask=head_mask,
              encoder_head_mask=encoder_head_mask,
              past_key_values=past_key_values,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
              err_msg_prefix = "decoder_" if self.is_decoder else ""
              raise ValueError(
                  f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
              )
          elif inputs["input_ids"] is not None:
              input_shape = shape_list(inputs["input_ids"])
              inputs["input_ids"] = tf.reshape(inputs["input_ids"], (-1, input_shape[-1]))
          elif inputs["inputs_embeds"] is not None:
              input_shape = shape_list(inputs["inputs_embeds"])[:-1]
          else:
              err_msg_prefix = "decoder_" if self.is_decoder else ""
              raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
  
          if inputs["inputs_embeds"] is None:
              assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
              inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
  
          batch_size, seq_length = input_shape
  
          # required mask seq length can be calculated via length of past
          mask_seq_length = (
              shape_list(inputs["past_key_values"][0][0])[2] + seq_length
              if inputs["past_key_values"] is not None
              else seq_length
          )
  
          if inputs["attention_mask"] is None:
              inputs["attention_mask"] = tf.fill((batch_size, mask_seq_length), 1)
          if (
              self.is_decoder
              and inputs["encoder_attention_mask"] is None
              and inputs["encoder_hidden_states"] is not None
          ):
              encoder_seq_length = shape_list(inputs["encoder_hidden_states"])[1]
              inputs["encoder_attention_mask"] = tf.fill((batch_size, encoder_seq_length), 1)
  
          # initialize past_key_values with `None` if past does not exist
          if inputs["past_key_values"] is None:
              inputs["past_key_values"] = [None] * len(self.block)
  
          # 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.
          inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=inputs["inputs_embeds"].dtype)
          num_dims_attention_mask = len(shape_list(inputs["attention_mask"]))
          if num_dims_attention_mask == 3:
              extended_attention_mask = inputs["attention_mask"][:, None, :, :]
          elif num_dims_attention_mask == 2:
              # Provided a padding mask of dimensions [batch_size, mask_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, mask_seq_length, mask_seq_length]
              if self.is_decoder:
                  seq_ids = tf.range(mask_seq_length)
                  causal_mask = tf.less_equal(
                      tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
                      seq_ids[None, :, None],
                  )
                  causal_mask = tf.cast(causal_mask, dtype=inputs["attention_mask"].dtype)
                  extended_attention_mask = causal_mask[:, None, :, :] * inputs["attention_mask"][:, None, None, :]
                  if inputs["past_key_values"][0] is not None:
                      extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
              else:
                  extended_attention_mask = inputs["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  -1e9 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 transposition
          # 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 and inputs["encoder_attention_mask"] is not None:
              # If a 2D ou 3D attention mask is provided for the cross-attention
              # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
              # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
              inputs["encoder_attention_mask"] = tf.cast(
                  inputs["encoder_attention_mask"], dtype=extended_attention_mask.dtype
              )
              num_dims_encoder_attention_mask = len(shape_list(inputs["encoder_attention_mask"]))
              if num_dims_encoder_attention_mask == 3:
                  encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, :, :]
              if num_dims_encoder_attention_mask == 2:
                  encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, None, :]
  
              # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
              # 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
  
          present_key_value_states = () if inputs["use_cache"] and self.is_decoder else None
          all_hidden_states = () if inputs["output_hidden_states"] else None
          all_attentions = () if inputs["output_attentions"] else None
          all_cross_attentions = () if (inputs["output_attentions"] and self.is_decoder) else None
          position_bias = None
          encoder_decoder_position_bias = None
  
          hidden_states = self.dropout(inputs["inputs_embeds"], training=inputs["training"])
  
          for idx, (layer_module, past_key_value) in enumerate(zip(self.block, inputs["past_key_values"])):
              if inputs["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=inputs["encoder_hidden_states"],
                  encoder_attention_mask=encoder_extended_attention_mask,
                  encoder_decoder_position_bias=encoder_decoder_position_bias,
                  layer_head_mask=inputs["head_mask"][idx] if inputs["head_mask"] is not None else None,
                  encoder_layer_head_mask=inputs["encoder_head_mask"][idx]
                  if inputs["encoder_head_mask"] is not None
                  else None,
                  past_key_value=past_key_value,
                  use_cache=inputs["use_cache"],
                  output_attentions=inputs["output_attentions"],
                  training=inputs["training"],
              )
  
              # layer_outputs is a tuple with:
              # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
              hidden_states, present_key_value_state = layer_outputs[:2]
  
              # We share the position biases between the layers - the first layer store them
              # layer_outputs = hidden-states, past_key_values, (self-attention weights),
              # (self-attention position bias), (cross-attention position bias), (cross-attention weights),
              position_bias = layer_outputs[2]
  
              if self.is_decoder and inputs["encoder_hidden_states"] is not None:
                  encoder_decoder_position_bias = layer_outputs[4 if inputs["output_attentions"] else 3]
  
              # append next layer key value states
              if present_key_value_state is not None and inputs["use_cache"] and self.is_decoder:
                  present_key_value_states = present_key_value_states + (present_key_value_state,)
  
              if inputs["output_attentions"]:
                  all_attentions = all_attentions + (layer_outputs[3],)
                  if self.is_decoder:
                      all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
  
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.dropout(hidden_states, training=inputs["training"])
  
          # Add last layer
          if inputs["output_hidden_states"]:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not inputs["return_dict"]:
              outputs = (hidden_states,)
              # need to check if is decoder here as well for special cases when using keras compile
              if inputs["use_cache"] and self.is_decoder:
                  outputs = outputs + (present_key_value_states,)
              if inputs["output_hidden_states"]:
                  outputs = outputs + (all_hidden_states,)
              if inputs["output_attentions"]:
                  outputs = outputs + (all_attentions,)
                  if self.is_decoder:
                      outputs + (all_cross_attentions,)
              return outputs  # last-layer hidden state, (past_key_values), (all hidden states), (all attentions), (all_cross_attentions)
  
          if self.is_decoder:
              return TFBaseModelOutputWithPastAndCrossAttentions(
                  last_hidden_state=hidden_states,
                  past_key_values=present_key_value_states,
                  hidden_states=all_hidden_states,
                  attentions=all_attentions,
                  cross_attentions=all_cross_attentions,
              )
          else:
              return TFBaseModelOutput(
                  last_hidden_state=hidden_states,
                  hidden_states=all_hidden_states,
                  attentions=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
      base_model_prefix = "transformer"
      # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
      _keys_to_ignore_on_load_unexpected = [r"decoder\Wblock[\W_0]+layer[\W_1]+EncDecAttention\Wrelative_attention_bias"]
  
      @property
      def dummy_inputs(self):
          inputs = tf.constant(DUMMY_INPUTS)
          input_mask = tf.constant(DUMMY_MASK)
          dummy_inputs = {
              "input_ids": inputs,
              "decoder_input_ids": inputs,
              "decoder_attention_mask": input_mask,
          }
          return dummy_inputs
  
      @tf.function(
          input_signature=[
              {
                  "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
                  "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
                  "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
                  "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
              }
          ]
      )
      def serving(self, inputs):
          output = self.call(inputs)
  
          return self.serving_output(output)
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, value):
          try:
              self.shared.weight = value
          except AttributeError:
              self(self.dummy_inputs)
              self.shared.weight = value
  
          self.shared.vocab_size = shape_list(value)[0]
          # retrieve correct absolute scope for embed token wrapper
          with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
              pass
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
          self.encoder.embed_tokens = embed_tokens
          if hasattr(self, "decoder"):
              self.decoder.embed_tokens = embed_tokens
  
      def _shift_right(self, input_ids):
          decoder_start_token_id = self.config.decoder_start_token_id
          pad_token_id = self.config.pad_token_id
  
          assert (
              decoder_start_token_id is not None
          ), "self.model.config.decoder_start_token_id has to be defined. In TF T5 it is usually set to the pad_token_id. See T5 docs for more information"
  
          start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id)
          start_tokens = tf.cast(start_tokens, input_ids.dtype)  # Ensure compatible dtypes for concatenation
          shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
  
          assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
          # replace possible -100 values in labels by `pad_token_id`
          shifted_input_ids = tf.where(
              shifted_input_ids == -100,
              tf.cast(tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids.dtype),
              shifted_input_ids,
          )
  
          # "Verify that `labels` has only positive values and -100"
          assert_gte0 = tf.debugging.assert_greater_equal(
              shifted_input_ids, tf.constant(0, dtype=shifted_input_ids.dtype)
          )
  
          # Make sure the assertion op is called by wrapping the result in an identity no-op
          with tf.control_dependencies([assert_gte0]):
              shifted_input_ids = tf.identity(shifted_input_ids)
  
          return shifted_input_ids
  
  
  T5_START_DOCSTRING = r"""
  
      The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
      Transformer](https://arxiv.org/abs/1910.10683) 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 inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
      as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
      behavior.
  
      <Tip>
  
      TF 2.0 models accepts two formats as inputs:
  
      - having all inputs as keyword arguments (like PyTorch models), or
      - having all inputs as a list, tuple or dict in the first positional arguments.
  
      This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
      tensors in the first argument of the model call function: `model(inputs)`.
  
      If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
      first positional argument :
  
      - a single Tensor with `input_ids` only and nothing else: `model(inputs_ids)`
      - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
      `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
      - a dictionary with one or several input Tensors associated to the input names given in the docstring:
      `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
  
      </Tip>
  
      Parameters:
          config ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
  """
  
  T5_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. 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 [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
              [`PreTrainedTokenizer.encode`] for details.
  
              [What are input IDs?](../glossary#input-ids)
  
              To know more on how to prepare `inputs` for pretraining take a look at [T5 Training](./t5#training).
          decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
              Provide for sequence to sequence training. T5 uses the `pad_token_id` as the starting token for
              `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids`
              have to be input (see `past_key_values`).
  
              To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
              Training](./t5#training).
          attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
              Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
              be used by default.
          head_mask: (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
              1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          decoder_head_mask: (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
              1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          encoder_outputs (`tuple(tuple(tf.FloatTensor)`, *optional*):
              Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
              `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
              the output of the last layer of the encoder. Used in the cross-attention of the decoder.
          past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
              contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
  
              If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
              don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
              `decoder_input_ids` of shape `(batch_size, sequence_length)`.
          inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          decoder_inputs_embeds (`tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
              Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
              representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
              input (see `past_key_values`). 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.
  
              If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
              of `inputs_embeds`.
          use_cache (`bool`, *optional*, defaults to `True`):
              If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
              `past_key_values`).
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
              config will be used instead.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
              used instead.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
              in eager mode, in graph mode the value will always be set to True.
          training (`bool`, *optional*, defaults to `False`):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  T5_ENCODER_INPUTS_DOCSTRING = r"""
      Args:
          inputs (`tf.Tensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. 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 [`T5Tokenizer`]. See [`PreTrainedTokenizer.__call__`] and
              [`PreTrainedTokenizer.encode`] for details.
  
              To know more on how to prepare `inputs` for pre-training take a look at [T5 Training](./t5#training).
          attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          head_mask: (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
          training (`bool`, *optional*, defaults to `False`):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  _HEAD_MASK_WARNING_MSG = """
  The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
  `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
  If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = tf.ones((num_layers,
  num_heads))`.
  """
  
  
  @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
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
  
          encoder_config = copy.deepcopy(config)
          encoder_config.use_cache = False
          self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          decoder_config.num_layers = config.num_decoder_layers
          self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          r"""
          Returns:
  
          Examples:
  
          ```python
          >>> from transformers import T5Tokenizer, TFT5Model
  
          >>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
          >>> model = TFT5Model.from_pretrained("t5-small")
  
          >>> input_ids = tokenizer(
          ...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
          >>> ).input_ids  # Batch size 1
          >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
  
          >>> # forward pass
          >>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
          >>> last_hidden_states = outputs.last_hidden_state
          ```"""
          # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
          if head_mask is not None and decoder_head_mask is None:
              warnings.warn(_HEAD_MASK_WARNING_MSG, FutureWarning)
              decoder_head_mask = head_mask
  
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              head_mask=head_mask,
              decoder_head_mask=decoder_head_mask,
              encoder_outputs=encoder_outputs,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          # Encode if needed (training, first prediction pass)
          if inputs["encoder_outputs"] is None:
              inputs["encoder_outputs"] = self.encoder(
                  inputs["input_ids"],
                  attention_mask=inputs["attention_mask"],
                  encoder_hidden_states=None,
                  encoder_attention_mask=None,
                  inputs_embeds=inputs["inputs_embeds"],
                  head_mask=inputs["head_mask"],
                  past_key_values=None,
                  use_cache=False,
                  output_attentions=inputs["output_attentions"],
                  output_hidden_states=inputs["output_hidden_states"],
                  return_dict=inputs["return_dict"],
                  training=inputs["training"],
              )
  
          hidden_states = inputs["encoder_outputs"][0]
  
          # Decode
          decoder_outputs = self.decoder(
              inputs["decoder_input_ids"],
              attention_mask=inputs["decoder_attention_mask"],
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=inputs["attention_mask"],
              inputs_embeds=inputs["decoder_inputs_embeds"],
              head_mask=inputs["decoder_head_mask"],
              encoder_head_mask=inputs["head_mask"],
              past_key_values=inputs["past_key_values"],
              use_cache=inputs["use_cache"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          past = decoder_outputs[1] if inputs["use_cache"] else None
  
          if not inputs["return_dict"]:
              if past is not None:
                  decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
              return decoder_outputs + inputs["encoder_outputs"]
  
          return TFSeq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              past_key_values=past,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
              encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
              encoder_attentions=inputs["encoder_outputs"].attentions,
          )
  
      def serving_output(self, output):
          pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
          dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
          dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
          cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
          enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
          enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
  
          return TFSeq2SeqModelOutput(
              last_hidden_state=output.last_hidden_state,
              past_key_values=pkv,
              decoder_hidden_states=dec_hs,
              decoder_attentions=dec_attns,
              encoder_last_hidden_state=output.encoder_last_hidden_state,
              cross_attentions=cross_attns,
              encoder_hidden_states=enc_hs,
              encoder_attentions=enc_attns,
          )
  
  
  @add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
  class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
      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
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
  
          encoder_config = copy.deepcopy(config)
          encoder_config.use_cache = False
          self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          decoder_config.num_layers = config.num_decoder_layers
          self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
  
          if not config.tie_word_embeddings:
              lm_head_initializer = tf.keras.initializers.RandomNormal(mean=0, stddev=config.initializer_factor)
              self.lm_head = tf.keras.layers.Dense(
                  config.vocab_size, use_bias=False, name="lm_head", kernel_initializer=lm_head_initializer
              )  # Update init weights as in flax
  
      def get_output_embeddings(self):
          if self.config.tie_word_embeddings:
              return self.get_input_embeddings()
          else:
              # in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
              # value has a shape (num_tokens, dim) then needs to be transposed
              return tf.transpose(self.lm_head.kernel)
  
      def set_output_embeddings(self, value):
          if self.config.tie_word_embeddings:
              self.set_input_embeddings(value)
          else:
              lm_head_initializer = tf.keras.initializers.RandomNormal(mean=0, stddev=self.config.initializer_factor)
              self.lm_head = tf.keras.layers.Dense(
                  shape_list(value)[0], use_bias=False, name="lm_head", kernel_initializer=lm_head_initializer
              )  # Update init weights as in flax
              # in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
              # value has a shape (num_tokens, dim) then needs to be transposed
              transposed_value = tf.transpose(value)
              self.lm_head.kernel = transposed_value
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          r"""
          labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
              config.vocab_size - 1]`.
  
          Returns:
  
          Examples:
  
          ```python
          >>> from transformers import T5Tokenizer, TFT5ForConditionalGeneration
  
          >>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
          >>> model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
  
          >>> # training
          >>> inputs = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="tf").input_ids
          >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="tf").input_ids
          >>> outputs = model(inputs, labels=labels)
          >>> loss = outputs.loss
          >>> logits = outputs.logits
  
          >>> # inference
          >>> inputs = tokenizer(
          ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="tf"
          >>> ).input_ids  # Batch size 1
          >>> outputs = model.generate(inputs)
          >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
          >>> # studies have shown that owning a dog is good for you
          ```"""
          # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
          if head_mask is not None and decoder_head_mask is None:
              warnings.warn(_HEAD_MASK_WARNING_MSG, FutureWarning)
              decoder_head_mask = head_mask
  
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              head_mask=head_mask,
              decoder_head_mask=decoder_head_mask,
              encoder_outputs=encoder_outputs,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              labels=labels,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          # Encode if needed (training, first prediction pass)
          if inputs["encoder_outputs"] is None:
              inputs["encoder_outputs"] = self.encoder(
                  inputs["input_ids"],
                  attention_mask=inputs["attention_mask"],
                  inputs_embeds=inputs["inputs_embeds"],
                  head_mask=inputs["head_mask"],
                  output_attentions=inputs["output_attentions"],
                  output_hidden_states=inputs["output_hidden_states"],
                  return_dict=inputs["return_dict"],
                  training=inputs["training"],
              )
  
          hidden_states = inputs["encoder_outputs"][0]
  
          if (
              inputs["labels"] is not None
              and inputs["decoder_input_ids"] is None
              and inputs["decoder_inputs_embeds"] is None
          ):
              # get decoder inputs from shifting lm labels to the right
              inputs["decoder_input_ids"] = self._shift_right(inputs["labels"])
  
          # Decode
          decoder_outputs = self.decoder(
              inputs["decoder_input_ids"],
              attention_mask=inputs["decoder_attention_mask"],
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=inputs["attention_mask"],
              inputs_embeds=inputs["decoder_inputs_embeds"],
              head_mask=inputs["decoder_head_mask"],
              past_key_values=inputs["past_key_values"],
              use_cache=inputs["use_cache"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          sequence_output = decoder_outputs[0]
  
          # T5v1.1 does not tie output word embeddings and thus does not require downscaling
          if self.config.tie_word_embeddings:
              sequence_output = sequence_output * (self.model_dim**-0.5)
              logits = self.shared(sequence_output, mode="linear")
          else:
              logits = self.lm_head(sequence_output)
  
          logits = tf.cast(logits, tf.float32)
  
          loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], logits)
  
          past = decoder_outputs[1] if inputs["use_cache"] else None
          if not inputs["return_dict"]:
              if past is not None:
                  decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
              output = (logits,) + decoder_outputs[1:] + inputs["encoder_outputs"]
              return ((loss,) + output) if loss is not None else output
  
          # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
          elif isinstance(inputs["encoder_outputs"], tuple):
              last_hidden_state = inputs["encoder_outputs"][0]
              hidden_states = None
              attentions = None
              idx = 0
              if inputs["output_hidden_states"]:
                  idx += 1
                  hidden_states = inputs["encoder_outputs"][idx]
              if inputs["output_attentions"]:
                  idx += 1
                  attentions = inputs["encoder_outputs"][idx]
  
              inputs["encoder_outputs"] = TFBaseModelOutput(
                  last_hidden_state=last_hidden_state,
                  hidden_states=hidden_states,
                  attentions=attentions,
              )
  
          return TFSeq2SeqLMOutput(
              loss=loss,
              logits=logits,
              past_key_values=past,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
              encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
              encoder_attentions=inputs["encoder_outputs"].attentions,
          )
  
      def serving_output(self, output):
          pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
          dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
          dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
          cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
          enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
          enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
  
          return TFSeq2SeqLMOutput(
              logits=output.logits,
              past_key_values=pkv,
              decoder_hidden_states=dec_hs,
              decoder_attentions=dec_attns,
              cross_attentions=cross_attns,
              encoder_last_hidden_state=output.encoder_last_hidden_state,
              encoder_hidden_states=enc_hs,
              encoder_attentions=enc_attns,
          )
  
      def prepare_inputs_for_generation(
          self,
          input_ids,
          past=None,
          attention_mask=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          use_cache=None,
          encoder_outputs=None,
          **kwargs
      ):
  
          # cut decoder_input_ids if past is used
          if past is not None:
              input_ids = input_ids[:, -1:]
  
          return {
              "input_ids": None,  # needs to be passed to make Keras.layer.__call__ happy
              "decoder_input_ids": input_ids,
              "past_key_values": past,
              "encoder_outputs": encoder_outputs,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "use_cache": use_cache,
          }
  
      def _update_model_kwargs_for_xla_generation(self, outputs, model_kwargs, current_pos, max_length):
          # TODO(Pvp, Joao, Matt) - this function can be cleaned a bit and refactored
          # quite some duplicated code patterns it seems
          # also the `attention_mask` is currently used in a somewhat hacky to
          # correctly influence the `past_key_values` - not sure if this is the way to go
          # Let's keep that for a future PR.
          past = outputs.past_key_values
          is_past_initialized = model_kwargs.pop("past", None) is not None
          decoder_attention_mask = model_kwargs.pop("decoder_attention_mask", None)
          batch_size = past[0][0].shape[0]
  
          if not is_past_initialized:
              # past[0].shape[3] is seq_length of prompt
              num_padding_values = max_length - past[0][0].shape[2] - 1
  
              padding_values = np.zeros((4, 2), dtype=np.int32)
              padding_values[2, 1] = num_padding_values
              padding_values = tf.constant(padding_values)
  
              new_past = ()
              for past_layer in past:
                  new_past_layer = list(past_layer)
                  for i in range(len(new_past_layer[:2])):
                      new_past_layer[i] = tf.pad(past_layer[i], padding_values)
                  new_past += (tuple(new_past_layer),)
  
              # 1 one for decoder_start_token_id, Zeros for the currently-unfilled locations in the past tensor, ones for the actual input_ids
              decoder_attention_mask = tf.concat(
                  [
                      tf.ones((batch_size, 1), dtype=tf.int32),
                      tf.zeros((batch_size, num_padding_values), dtype=tf.int32),
                      tf.ones((batch_size, 1), dtype=tf.int32),
                  ],
                  axis=1,
              )
          else:
              slice_start_base = tf.constant([0, 0, 1, 0])
              decoder_attention_mask_update_slice = tf.ones((batch_size, 1), dtype=decoder_attention_mask.dtype)
              # correct 5 here
              new_past_index = current_pos - 1
  
              new_past = ()
              for past_layer in past:
                  new_past_layer = list(past_layer)
                  for i in range(len(new_past_layer[:2])):
                      update_slice = past_layer[i][:, :, -1:]
                      # Write the last slice to the first open location in the padded past array
                      # and then truncate the last slice off the array
                      new_past_layer[i] = dynamic_update_slice(
                          past_layer[i][:, :, :-1], update_slice, slice_start_base * new_past_index
                      )
                  new_past += (tuple(new_past_layer),)
  
              update_start = tf.constant([0, 1], dtype=tf.int32) * new_past_index
              decoder_attention_mask = dynamic_update_slice(
                  decoder_attention_mask, decoder_attention_mask_update_slice, update_start
              )
  
          # set `attention_mask` and `past`
          model_kwargs["decoder_attention_mask"] = decoder_attention_mask
          model_kwargs["past"] = new_past
  
          return model_kwargs
  
      def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
          return self._shift_right(labels)
  
      def _reorder_cache(self, past, beam_idx):
          # if decoder past is not included in output
          # speedy decoding is disabled and no need to reorder
          if past is None:
              logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
              return past
  
          reordered_decoder_past = ()
          for layer_past_states in past:
              # get the correct batch idx from layer past batch dim
              # batch dim of `past` is at 2nd position
              reordered_layer_past_states = ()
              for layer_past_state in layer_past_states:
                  # need to set correct `past` for each of the four key / value states
                  reordered_layer_past_states = reordered_layer_past_states + (
                      tf.gather(layer_past_state, beam_idx, axis=0),
                  )
  
              assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
              assert len(reordered_layer_past_states) == len(layer_past_states)
  
              reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
          return reordered_decoder_past
  
  
  @add_start_docstrings(
      "The bare T5 Model transformer outputting encoder's raw hidden-states" "without any specific head on top.",
      T5_START_DOCSTRING,
  )
  class TFT5EncoderModel(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
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
  
          encoder_config = copy.deepcopy(config)
          encoder_config.use_cache = False
          self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
  
      def get_encoder(self):
          return self.encoder
  
      @add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_ids,
          attention_mask=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs,
      ):
          r"""
          Returns:
  
          Examples:
  
          ```python
          >>> from transformers import T5Tokenizer, TFT5EncoderModel
  
          >>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
          >>> model = TFT5EncoderModel.from_pretrained("t5-small")
  
          >>> input_ids = tokenizer(
          ...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
          >>> ).input_ids  # Batch size 1
          >>> outputs = model(input_ids)
          ```"""
          inputs = input_processing(
              func=self.call,
              config=self.config,
              input_ids=input_ids,
              attention_mask=attention_mask,
              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,
          )
  
          encoder_outputs = self.encoder(
              input_ids,
              attention_mask=inputs["attention_mask"],
              encoder_hidden_states=None,
              encoder_attention_mask=None,
              inputs_embeds=inputs["inputs_embeds"],
              head_mask=head_mask,
              past_key_values=None,
              use_cache=False,
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          if not inputs["return_dict"]:
              return encoder_outputs
  
          return TFBaseModelOutput(
              last_hidden_state=encoder_outputs.last_hidden_state,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
      # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
      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 TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
  