code: # coding=utf-8
  # Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
  #
  # 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.
  """ PyTorch T5 model. """
  
  
  import copy
  import itertools
  import logging
  import math
  import os
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from .configuration_t5 import T5Config
  from .file_utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_utils import PreTrainedModel, prune_linear_layer
  
  
  logger = logging.getLogger(__name__)
  
  ####################################################
  # This dict contrains shortcut names and associated url
  # for the pretrained weights provided with the models
  ####################################################
  T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "t5-small": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin",
      "t5-base": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-pytorch_model.bin",
      "t5-large": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-pytorch_model.bin",
      "t5-3b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-pytorch_model.bin",
      "t5-11b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-pytorch_model.bin",
  }
  
  
  ####################################################
  # This is a conversion method from TF 1.0 to PyTorch
  # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
  ####################################################
  def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
      """ Load tf checkpoints in a pytorch model.
      """
      try:
          import re
          import numpy as np
          import tensorflow as tf
      except ImportError:
          logger.error(
              "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions."
          )
          raise
      tf_path = os.path.abspath(tf_checkpoint_path)
      logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
      # Load weights from TF model
      init_vars = tf.train.list_variables(tf_path)
      names = []
      tf_weights = {}
      for name, shape in init_vars:
          logger.info("Loading TF weight {} with shape {}".format(name, shape))
          array = tf.train.load_variable(tf_path, name)
          names.append(name)
          tf_weights[name] = array
  
      for txt_name in names:
          name = txt_name.split("/")
          # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
          # which are not required for using pretrained model
          if any(
              n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
              for n in name
          ):
              logger.info("Skipping {}".format("/".join(name)))
              tf_weights.pop(txt_name, None)
              continue
          if "_slot_" in name[-1]:
              logger.info("Skipping {}".format("/".join(name)))
              tf_weights.pop(txt_name, None)
              continue
          pointer = model
          array = tf_weights[txt_name]
          for m_name in name:
              if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                  scope_names = re.split(r"_(\d+)", m_name)
              else:
                  scope_names = [m_name]
              if scope_names[0] in ["kernel", "scale", "embedding"]:
                  pointer = getattr(pointer, "weight")
              # elif scope_names[0] == 'scale':
              #     pointer = getattr(pointer, 'weight')
              # elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta':
              #     pointer = getattr(pointer, 'bias')
              # elif scope_names[0] == 'squad':
              #     pointer = getattr(pointer, 'classifier')
              else:
                  try:
                      pointer = getattr(pointer, scope_names[0])
                  except AttributeError:
                      logger.info("Skipping {}".format("/".join(name)))
                      continue
              if len(scope_names) >= 2:
                  num = int(scope_names[1])
                  pointer = pointer[num]
          if scope_names[0] not in ["kernel", "scale", "embedding"]:
              pointer = getattr(pointer, "weight")
          if scope_names[0] != "embedding":
              logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, name))
              array = np.transpose(array)
          try:
              assert pointer.shape == array.shape
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          logger.info("Initialize PyTorch weight {}".format(name))
          pointer.data = torch.from_numpy(array.astype(np.float32))
          tf_weights.pop(txt_name, None)
  
      logger.info("Weights not copied to PyTorch model: {}".format(", ".join(tf_weights.keys())))
      # logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
      return model
  
  
  ####################################################
  # PyTorch Models are constructed by sub-classing
  # - torch.nn.Module for the layers and
  # - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module)
  ####################################################
  
  
  class T5LayerNorm(nn.Module):
      def __init__(self, hidden_size, eps=1e-6):
          """ Construct a layernorm module in the T5 style
              No bias and no substraction of mean.
          """
          super().__init__()
          self.weight = nn.Parameter(torch.ones(hidden_size))
          self.variance_epsilon = eps
  
      def forward(self, x):
          variance = x.pow(2).mean(-1, keepdim=True)
          x = x / torch.sqrt(variance + self.variance_epsilon)
          return self.weight * x
  
  
  class T5DenseReluDense(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
          self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
          self.dropout = nn.Dropout(config.dropout_rate)
  
      def forward(self, hidden_states):
          h = self.wi(hidden_states)
          h = F.relu(h)
          h = self.dropout(h)
          h = self.wo(h)
          return h
  
  
  class T5LayerFF(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.DenseReluDense = T5DenseReluDense(config)
          self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
          self.dropout = nn.Dropout(config.dropout_rate)
  
      def forward(self, hidden_states):
          norm_x = self.layer_norm(hidden_states)
          y = self.DenseReluDense(norm_x)
          layer_output = hidden_states + self.dropout(y)
          return layer_output
  
  
  class T5Attention(nn.Module):
      NEW_ID = itertools.count()
  
      def __init__(self, config, has_relative_attention_bias=False):
          super().__init__()
          self.layer_id = next(T5Attention.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.dropout = config.dropout_rate
          self.inner_dim = self.n_heads * self.d_kv
  
          # Mesh TensorFlow initialization to avoid scaling before softmax
          self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
          self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
          self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
          self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
  
          if self.has_relative_attention_bias:
              self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          mask = torch.ones(self.n_heads, self.d_kv)
          heads = set(heads) - self.pruned_heads
          for head in heads:
              head -= sum(1 if h < head else 0 for h in self.pruned_heads)
              mask[head] = 0
          mask = mask.view(-1).contiguous().eq(1)
          index = torch.arange(len(mask))[mask].long()
          # Prune linear layers
          self.q = prune_linear_layer(self.q, index)
          self.k = prune_linear_layer(self.k, index)
          self.v = prune_linear_layer(self.v, index)
          self.o = prune_linear_layer(self.o, index, dim=1)
          # Update hyper params
          self.n_heads = self.n_heads - len(heads)
          self.inner_dim = self.d_kv * self.n_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      @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 += (n < 0).to(torch.long) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
              n = torch.abs(n)
          else:
              n = torch.max(n, torch.zeros_like(n))
          # now n is in the range [0, inf)
  
          # half of the buckets are for exact increments in positions
          max_exact = num_buckets // 2
          is_small = n < max_exact
  
          # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
          val_if_large = max_exact + (
              torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
          ).to(torch.long)
          val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
  
          ret += torch.where(is_small, n, val_if_large)
          return ret
  
      def compute_bias(self, qlen, klen):
          """ Compute binned relative position bias """
          context_position = torch.arange(qlen, dtype=torch.long)[:, None]
          memory_position = torch.arange(klen, dtype=torch.long)[None, :]
          relative_position = memory_position - context_position  # shape (qlen, klen)
          rp_bucket = self._relative_position_bucket(
              relative_position,  # shape (qlen, klen)
              bidirectional=not self.is_decoder,
              num_buckets=self.relative_attention_num_buckets,
          )
          rp_bucket = rp_bucket.to(self.relative_attention_bias.weight.device)
          values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
          values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, qlen, klen)
          return values
  
      def forward(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None):
          """
          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 = input.size()
          if kv is None:
              klen = qlen if cache is None else cache["slen"] + qlen
          else:
              klen = kv.size(1)
  
          def shape(x):
              """  projection """
              return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2)
  
          def unshape(x):
              """  compute context """
              return x.transpose(1, 2).contiguous().view(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 = torch.cat([k_, k], dim=2)  # (bs, n_heads, klen, dim_per_head)
                      v = torch.cat([v_, v], dim=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 = torch.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  # (bs, n_heads, qlen, klen)
  
          scores += position_bias
          weights = F.softmax(scores.float(), dim=-1).type_as(scores)  # (bs, n_heads, qlen, klen)
          weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = torch.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 T5LayerSelfAttention(nn.Module):
      def __init__(self, config, has_relative_attention_bias=False):
          super().__init__()
          self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
          self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
          self.dropout = nn.Dropout(config.dropout_rate)
  
      def forward(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None):
          norm_x = self.layer_norm(hidden_states)
          attention_output = self.SelfAttention(
              norm_x, mask=attention_mask, position_bias=position_bias, head_mask=head_mask
          )
          y = attention_output[0]
          layer_output = hidden_states + self.dropout(y)
          outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
          return outputs
  
  
  class T5LayerCrossAttention(nn.Module):
      def __init__(self, config, has_relative_attention_bias=False):
          super().__init__()
          self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
          self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
          self.dropout = nn.Dropout(config.dropout_rate)
  
      def forward(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None):
          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
          )
          y = attention_output[0]
          layer_output = hidden_states + self.dropout(y)
          outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
          return outputs
  
  
  class T5Block(nn.Module):
      def __init__(self, config, has_relative_attention_bias=False):
          super().__init__()
          self.is_decoder = config.is_decoder
          self.layer = nn.ModuleList()
          self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
          if self.is_decoder:
              self.layer.append(T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias))
              self.layer.append(T5LayerFF(config))
          else:
              self.layer.append(T5LayerFF(config))
  
      def forward(
          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,
      ):
          self_attention_outputs = self.layer[0](
              hidden_states, attention_mask=attention_mask, position_bias=position_bias, head_mask=head_mask
          )
          hidden_states = self_attention_outputs[0]
          outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights
  
          if not self.is_decoder:
              hidden_states = self.layer[1](hidden_states)
          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,
              )
              hidden_states = cross_attention_outputs[0]
              outputs = (
                  outputs + cross_attention_outputs[1:]
              )  # Keep cross-attention outputs and relative position weights
              hidden_states = self.layer[2](hidden_states)
  
          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 T5PreTrainedModel(PreTrainedModel):
      """ An abstract class to handle weights initialization and
          a simple interface for downloading and loading pretrained models.
      """
  
      config_class = T5Config
      pretrained_model_archive_map = T5_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = load_tf_weights_in_t5
      base_model_prefix = "transformer"
      encoder_outputs_batch_dim_idx = 0  # outputs shaped (bs, ...)
  
      @property
      def dummy_inputs(self):
          input_ids = torch.tensor(DUMMY_INPUTS)
          input_mask = torch.tensor(DUMMY_MASK)
          dummy_inputs = {
              "decoder_input_ids": input_ids,
              "input_ids": input_ids,
              "decoder_attention_mask": input_mask,
          }
          return dummy_inputs
  
      def _init_weights(self, module):
          """ Initialize the weights """
          factor = self.config.initializer_factor  # Used for testing weights initialization
          if isinstance(module, T5LayerNorm):
              module.weight.data.fill_(factor * 1.0)
          elif isinstance(module, (T5Model, T5ForConditionalGeneration)):
              # Mesh TensorFlow embeddings initialization
              # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
              module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
          elif isinstance(module, T5DenseReluDense):
              # Mesh TensorFlow FF initialization
              # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
              # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
              module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
              if hasattr(module.wi, "bias") and module.wi.bias is not None:
                  module.wi.bias.data.zero_()
              module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
              if hasattr(module.wo, "bias") and module.wo.bias is not None:
                  module.wo.bias.data.zero_()
          elif isinstance(module, T5Attention):
              # Mesh TensorFlow attention initialization to avoid scaling before softmax
              # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
              d_model = self.config.d_model
              d_kv = self.config.d_kv
              n_heads = self.config.num_heads
              module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * d_kv) ** -0.5))
              module.k.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
              module.v.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
              module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * d_kv) ** -0.5))
              if module.has_relative_attention_bias:
                  module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
  
  
  class T5Stack(T5PreTrainedModel):
      def __init__(self, config, embed_tokens=None):
          super().__init__(config)
          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.block = nn.ModuleList(
              [T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
          )
          self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
          self.dropout = nn.Dropout(config.dropout_rate)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embed_tokens
  
      def get_output_embeddings(self):
          return self.embed_tokens
  
      def set_input_embeddings(self, new_embeddings):
          self.embed_tokens = new_embeddings
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          inputs_embeds=None,
          head_mask=None,
      ):
  
          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 = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-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 = torch.ones(batch_size, seq_length).to(inputs_embeds.device)
          if self.is_decoder and encoder_attention_mask is None:
              encoder_seq_length = encoder_hidden_states.shape[1]
              encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(inputs_embeds.device)
  
          # 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.
          if attention_mask.dim() == 3:
              extended_attention_mask = attention_mask[:, None, :, :]
          elif attention_mask.dim() == 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 = torch.arange(seq_length, device=inputs_embeds.device)
                  causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
                  causal_mask = causal_mask.to(attention_mask)
                  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 -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 = (extended_attention_mask == extended_attention_mask.transpose(-1, -2))
  
          extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
          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]
              if encoder_attention_mask.dim() == 3:
                  encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
              if encoder_attention_mask.dim() == 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 transposition
              # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
              # encoder_extended_attention_mask = (encoder_extended_attention_mask == encoder_extended_attention_mask.transpose(-1, -2))
  
              encoder_extended_attention_mask = encoder_extended_attention_mask.to(
                  dtype=next(self.parameters()).dtype
              )  # fp16 compatibility
              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:
              if head_mask.dim() == 1:
                  head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                  head_mask = head_mask.expand(self.config.num_layers, -1, -1, -1, -1)
              elif head_mask.dim() == 2:
                  head_mask = (
                      head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
                  )  # We can specify head_mask for each layer
              head_mask = head_mask.to(
                  dtype=next(self.parameters()).dtype
              )  # switch to fload if need + fp16 compatibility
          else:
              head_mask = [None] * self.config.num_layers
  
          all_hidden_states = ()
          all_attentions = ()
          position_bias = None
          encoder_decoder_position_bias = None
  
          hidden_states = self.dropout(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],
              )
              # layer_outputs is a tuple with:
              # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
              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],)  # We keep only self-attention weights for now
  
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.dropout(hidden_states)
  
          # 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)
  
  
  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 PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
      refer to the PyTorch 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
  
      .. _`torch.nn.Module`:
          https://pytorch.org/docs/stable/nn.html#module
  
      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:
          input_ids (:obj:`torch.LongTensor` 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:`torch.FloatTensor` 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(torch.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_input_ids (:obj:`torch.LongTensor` 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.
          decoder_attention_mask (:obj:`torch.BoolTensor` 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:`torch.FloatTensor` 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:`torch.FloatTensor` 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:`torch.FloatTensor` 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 T5Model(T5PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.shared = nn.Embedding(config.vocab_size, config.d_model)
  
          encoder_config = copy.deepcopy(config)
          self.encoder = T5Stack(encoder_config, self.shared)
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          self.decoder = T5Stack(decoder_config, self.shared)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, new_embeddings):
          self.shared = new_embeddings
          self.encoder.set_input_embeddings(new_embeddings)
          self.decoder.set_input_embeddings(new_embeddings)
  
      def _prune_heads(self, heads_to_prune):
          """ Prunes heads of the model.
              heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
              See base class PreTrainedModel
          """
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(heads)
  
      @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_outputs=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          head_mask=None,
      ):
          r"""
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
          last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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, T5Model
  
              tokenizer = T5Tokenizer.from_pretrained('t5-small')
              model = T5Model.from_pretrained('t5-small')
              input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt")  # Batch size 1
              outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
              last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
  
          """
  
          # Encode if needed (training, first prediction pass)
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask
              )
  
          hidden_states = encoder_outputs[0]
  
          # Decode
          decoder_outputs = self.decoder(
              input_ids=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 T5ForConditionalGeneration(T5PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.model_dim = config.d_model
  
          self.shared = nn.Embedding(config.vocab_size, config.d_model)
  
          encoder_config = copy.deepcopy(config)
          self.encoder = T5Stack(encoder_config, self.shared)
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          self.decoder = T5Stack(decoder_config, self.shared)
  
          self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, new_embeddings):
          self.shared = new_embeddings
          self.encoder.set_input_embeddings(new_embeddings)
          self.decoder.set_input_embeddings(new_embeddings)
  
      def get_output_embeddings(self):
          return self.lm_head
  
      def get_encoder(self):
          return self.encoder
  
      @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_outputs=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          lm_labels=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          head_mask=None,
      ):
          r"""
          lm_labels (:obj:`torch.LongTensor` 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).
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_label` is provided):
              Classification loss (cross entropy).
          prediction_scores (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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, T5ForConditionalGeneration
  
          tokenizer = T5Tokenizer.from_pretrained('t5-small')
          model = T5ForConditionalGeneration.from_pretrained('t5-small')
          input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt")  # Batch size 1
          outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, lm_labels=input_ids)
          loss, prediction_scores = outputs[:2]
  
          tokenizer = T5Tokenizer.from_pretrained('t5-small')
          model = T5ForConditionalGeneration.from_pretrained('t5-small')
          input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="pt")  # Batch size 1
          outputs = model.generate(input_ids)
          """
  
          # 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=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask
              )
  
          hidden_states = encoder_outputs[0]
  
          # Decode
          decoder_outputs = self.decoder(
              input_ids=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,
          )
  
          sequence_output = decoder_outputs[0]
          # Rescale output before projecting on vocab
          # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
          sequence_output = sequence_output * (self.model_dim ** -0.5)
          lm_logits = self.lm_head(sequence_output)
  
          decoder_outputs = (lm_logits,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
          if lm_labels is not None:
              shift_logits = lm_logits[..., :-1, :].contiguous()
              shift_labels = lm_labels[..., 1:].contiguous()
              loss_fct = CrossEntropyLoss(ignore_index=-100)
              loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
              decoder_outputs = (
                  loss,
              ) + decoder_outputs  # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
  
          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 {
              "decoder_input_ids": 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
  