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 math
  import os
  import warnings
  
  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_model_forward,
      replace_return_docstrings,
  )
  from .modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, Seq2SeqLMOutput, Seq2SeqModelOutput
  from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "T5Config"
  _TOKENIZER_FOR_DOC = "T5Tokenizer"
  
  ####################################################
  # This dict contains shortcut names and associated url
  # for the pretrained weights provided with the models
  ####################################################
  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
  ]
  
  
  ####################################################
  # 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
              ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
          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 subtraction of mean.
          """
          super().__init__()
          self.weight = nn.Parameter(torch.ones(hidden_size))
          self.variance_epsilon = eps
  
      def forward(self, x):
          # layer norm should always be calculated in float32
          variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
          x = x / torch.sqrt(variance + self.variance_epsilon)
  
          if self.weight.dtype == torch.float16:
              x = x.to(torch.float16)
          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):
      def __init__(self, config: T5Config, has_relative_attention_bias=False, is_bidirectional=False):
          super().__init__()
          self.is_bidirectional = is_bidirectional
          self.is_decoder = config.is_decoder
          self.has_relative_attention_bias = has_relative_attention_bias
  
          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
          heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, self.d_kv, self.pruned_heads)
          # 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=self.is_bidirectional,
              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,
          past_key_value=None,
          head_mask=None,
          query_length=None,
          use_cache=False,
          output_attentions=False,
      ):
          """
          Self-attention (if kv is None) or attention over source sentence (provided by kv).
          """
          # Input is (bs, qlen, dim)
          # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
          # past_key_value[0] is (bs, n_heads, q_len - 1, dim_per_head)
          bs, qlen, dim = input.size()
  
          if past_key_value is not None:
              assert self.is_decoder is True, "Encoder cannot cache past key value states"
              assert (
                  len(past_key_value) == 2
              ), "past_key_value should have 2 past states: keys and values. Got {} past states".format(
                  len(past_key_value)
              )
              real_qlen = qlen + past_key_value[0].shape[2] if query_length is None else query_length
          else:
              real_qlen = qlen
  
          if kv is None:
              klen = real_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 past_key_value is None:
              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 past_key_value is not None:
              if kv is None:
                  k_, v_ = past_key_value
                  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 = past_key_value
  
          if self.is_decoder and use_cache is True:
              present_key_value_state = ((k, v),)
          else:
              present_key_value_state = (None,)
  
          # (bs, n_heads, qlen, klen)
          scores = torch.matmul(
              q, k.transpose(3, 2)
          )  # equivalent of torch.einsum("bnqd,bnkd->bnqk", q, k), compatible with onnx op>9
  
          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(real_qlen, klen)
  
              # 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[:, :, -qlen:, :]
  
              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,) + present_key_value_state
  
          if 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, is_bidirectional=not config.is_decoder
          )
          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,
          past_key_value=None,
          use_cache=False,
          output_attentions=False,
      ):
          norm_x = self.layer_norm(hidden_states)
          attention_output = self.SelfAttention(
              norm_x,
              mask=attention_mask,
              position_bias=position_bias,
              head_mask=head_mask,
              past_key_value=past_key_value,
              use_cache=use_cache,
              output_attentions=output_attentions,
          )
          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, is_bidirectional=True
          )
          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,
          past_key_value=None,
          use_cache=False,
          query_length=None,
          output_attentions=False,
      ):
          norm_x = self.layer_norm(hidden_states)
          attention_output = self.EncDecAttention(
              norm_x,
              mask=attention_mask,
              kv=kv,
              position_bias=position_bias,
              head_mask=head_mask,
              past_key_value=past_key_value,
              use_cache=use_cache,
              query_length=query_length,
              output_attentions=output_attentions,
          )
          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))
  
      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,
          past_key_value=None,
          use_cache=False,
          output_attentions=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
  
              error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
                  expected_num_past_key_values,
                  "2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "",
                  len(past_key_value),
              )
              assert len(past_key_value) == expected_num_past_key_values, error_message
  
              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,
              head_mask=head_mask,
              past_key_value=self_attn_past_key_value,
              use_cache=use_cache,
              output_attentions=output_attentions,
          )
          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 = present_key_value_state[0].shape[2]
              else:
                  query_length = None
  
              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,
                  past_key_value=cross_attn_past_key_value,
                  query_length=query_length,
                  use_cache=use_cache,
                  output_attentions=output_attentions,
              )
              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)
          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)
  
  
  class T5PreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = T5Config
      load_tf_weights = load_tf_weights_in_t5
      base_model_prefix = "transformer"
  
      @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))
  
      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 T5 it is usually set to the pad_token_id. See T5 docs for more information"
  
          # shift inputs to the right
          shifted_input_ids = input_ids.new_zeros(input_ids.shape)
          shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
          shifted_input_ids[..., 0] = decoder_start_token_id
  
          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.masked_fill_(shifted_input_ids == -100, pad_token_id)
  
          assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values"
  
          return shifted_input_ids
  
  
  class T5Stack(T5PreTrainedModel):
      def __init__(self, config, embed_tokens=None):
          super().__init__(config)
  
          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,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
  
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if input_ids is not None and inputs_embeds is not None:
              err_msg_prefix = "decoder_" if self.is_decoder else ""
              raise ValueError(
                  f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}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:
              err_msg_prefix = "decoder_" if self.is_decoder else ""
              raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
  
          if inputs_embeds is None:
              assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
              inputs_embeds = self.embed_tokens(input_ids)
  
          batch_size, seq_length = input_shape
  
          # required mask seq length can be calculated via length of past
          mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
  
          if use_cache is True:
              assert self.is_decoder, ":obj:`use_cache` can only be set to `True` if {} is used as a decoder".format(
                  self
              )
  
          if attention_mask is None:
              attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
          if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
              encoder_seq_length = encoder_hidden_states.shape[1]
              encoder_attention_mask = torch.ones(
                  batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
              )
  
          # initialize past_key_values with `None` if past does not exist
          if past_key_values is None:
              past_key_values = [None] * len(self.block)
  
          # ourselves in which case we just need to make it broadcastable to all heads.
          extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)
  
          if self.is_decoder and encoder_attention_mask is not None:
              encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
          else:
              encoder_extended_attention_mask = None
  
          # Prepare head mask if needed
          head_mask = self.get_head_mask(head_mask, self.config.num_layers)
          present_key_value_states = () if use_cache else None
          all_hidden_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
          position_bias = None
          encoder_decoder_position_bias = None
  
          hidden_states = self.dropout(inputs_embeds)
  
          for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
              if 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],
                  past_key_value=past_key_value,
                  use_cache=use_cache,
                  output_attentions=output_attentions,
              )
              # 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]
  
              if i == 0:
                  # We share the position biases between the layers - the first layer store them
                  # layer_outputs = hidden-states, key-value-states (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
                  position_bias = layer_outputs[3 if output_attentions else 2]
                  if self.is_decoder and encoder_hidden_states is not None:
                      encoder_decoder_position_bias = layer_outputs[5 if output_attentions else 3]
              # append next layer key value states
              if use_cache:
                  present_key_value_states = present_key_value_states + (present_key_value_state,)
  
              if output_attentions:
                  all_attentions = all_attentions + (layer_outputs[2],)  # 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 output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [hidden_states, present_key_value_states, all_hidden_states, all_attentions]
                  if v is not None
              )
          return BaseModelOutputWithPast(
              last_hidden_state=hidden_states,
              past_key_values=present_key_value_states,
              hidden_states=all_hidden_states,
              attentions=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
      <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 :class:`~transformers.PreTrainedModel`. 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 PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      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. T5 is a model with relative position embeddings so you
              should be able to pad the inputs on both the right and the left.
  
              Indices can be obtained using :class:`~transformers.T5Tokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              detail.
  
              To know more on how to prepare :obj:`input_ids` for pretraining take a look a `T5 Training
              <./t5.html#training>`__.
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(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.html#attention-mask>`__
          decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Provide for sequence to sequence training. T5 uses the :obj:`pad_token_id` as the starting token for
              :obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last
              :obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`).
  
              To know more on how to prepare :obj:`decoder_input_ids` for pretraining take a look at `T5 Training
              <./t5.html#training>`__. If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset,
              :obj:`decoder_input_ids` takes the value of :obj:`input_ids`.
          decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default.
          encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
              Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`:
              `attentions`) :obj:`last_hidden_state` of shape :obj:`(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 (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
              (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
              instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
          head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
              representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
              have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
              :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
  
              If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
              takes the value of :obj:`inputs_embeds`.
  
          use_cache (:obj:`bool`, `optional`):
              If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
              decoding (see :obj:`past_key_values`).
  
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  @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: T5Config):
          super().__init__(config)
          self.shared = nn.Embedding(config.vocab_size, config.d_model)
  
          encoder_config = copy.deepcopy(config)
          encoder_config.use_cache = False
          encoder_config.is_encoder_decoder = False
          self.encoder = T5Stack(encoder_config, self.shared)
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          decoder_config.is_encoder_decoder = False
          decoder_config.num_layers = config.num_decoder_layers
          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 get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      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_model_forward(T5_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          head_mask=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          **kwargs,
      ):
          r"""
          Returns:
  
          Example::
  
              >>> from transformers import T5Tokenizer, T5Model
  
              >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
              >>> model = T5Model.from_pretrained('t5-small')
  
              >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
              >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
              >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)
  
              >>> last_hidden_states = outputs.last_hidden_state
          """
          if "decoder_past_key_value_states" in kwargs:
              warnings.warn(
                  "The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
                  FutureWarning,
              )
              past_key_values = kwargs.pop("decoder_past_key_value_states")
          if "decoder_past_key_values" in kwargs:
              warnings.warn(
                  "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
                  FutureWarning,
              )
              past_key_values = kwargs.pop("decoder_past_key_values")
          assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
  
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # 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,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
              )
          elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
              encoder_outputs = BaseModelOutput(
                  last_hidden_state=encoder_outputs[0],
                  hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                  attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
              )
  
          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,
              past_key_values=past_key_values,
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=attention_mask,
              head_mask=head_mask,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return Seq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              past_key_values=decoder_outputs.past_key_values,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING)
  class T5ForConditionalGeneration(T5PreTrainedModel):
      authorized_missing_keys = [r"encoder\.embed_tokens\.weight", r"decoder\.embed_tokens\.weight", r"lm_head\.weight"]
  
      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)
          encoder_config.use_cache = False
          encoder_config.is_encoder_decoder = False
          self.encoder = T5Stack(encoder_config, self.shared)
  
          decoder_config = copy.deepcopy(config)
          decoder_config.is_decoder = True
          decoder_config.is_encoder_decoder = False
          decoder_config.num_layers = config.num_decoder_layers
          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
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          head_mask=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          **kwargs,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[-100, 0, ...,
              config.vocab_size - 1]`. All labels set to ``-100`` are ignored (masked), the loss is only computed for
              labels in ``[0, ..., config.vocab_size]``
          kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
              Used to hide legacy arguments that have been deprecated.
  
          Returns:
  
          Examples::
  
              >>> from transformers import T5Tokenizer, T5ForConditionalGeneration
  
              >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
              >>> model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True)
  
              >>> input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
              labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt').input_ids
              >>> outputs = model(input_ids=input_ids, labels=labels)
              >>> loss = outputs.loss
              >>> logits = outputs.logits
  
              >>> input_ids = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="pt").input_ids  # Batch size 1
              >>> outputs = model.generate(input_ids)
          """
  
          if "lm_labels" in kwargs:
              warnings.warn(
                  "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
                  FutureWarning,
              )
              labels = kwargs.pop("lm_labels")
          if "decoder_past_key_value_states" in kwargs:
              warnings.warn(
                  "The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
                  FutureWarning,
              )
              past_key_values = kwargs.pop("decoder_past_key_value_states")
          if "decoder_past_key_values" in kwargs:
              warnings.warn(
                  "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
                  FutureWarning,
              )
              past_key_values = kwargs.pop("decoder_past_key_values")
          assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
  
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # 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,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
              )
          elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
              encoder_outputs = BaseModelOutput(
                  last_hidden_state=encoder_outputs[0],
                  hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                  attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
              )
  
          hidden_states = encoder_outputs[0]
  
          if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
              # get decoder inputs from shifting lm labels to the right
              decoder_input_ids = self._shift_right(labels)
  
          # If decoding with past key value states, only the last tokens
          # should be given as an input
          if past_key_values is not None:
              assert labels is None, "Decoder should not use cached key value states when training."
              if decoder_input_ids is not None:
                  decoder_input_ids = decoder_input_ids[:, -1:]
              if decoder_inputs_embeds is not None:
                  decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
  
          # Decode
          decoder_outputs = self.decoder(
              input_ids=decoder_input_ids,
              attention_mask=decoder_attention_mask,
              inputs_embeds=decoder_inputs_embeds,
              past_key_values=past_key_values,
              encoder_hidden_states=hidden_states,
              encoder_attention_mask=attention_mask,
              head_mask=head_mask,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          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)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss(ignore_index=-100)
              loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
              # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
  
          if not return_dict:
              output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
              return ((loss,) + output) if loss is not None else output
  
          return Seq2SeqLMOutput(
              loss=loss,
              logits=lm_logits,
              past_key_values=decoder_outputs.past_key_values,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
      def prepare_inputs_for_generation(self, input_ids, past, attention_mask, use_cache, encoder_outputs, **kwargs):
  
          # cut decoder_input_ids if past is used
          if past is not None:
              input_ids = input_ids[:, -1:]
  
          return {
              "decoder_input_ids": input_ids,
              "past_key_values": past,
              "encoder_outputs": encoder_outputs,
              "attention_mask": attention_mask,
              "use_cache": use_cache,
          }
  
      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 + (
                      layer_past_state.index_select(0, beam_idx),
                  )
  
              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
  
apis: torch.sqrt torch.nn.ModuleList tensorflow.train.load_variable torch.cat torch.nn.Dropout numpy.transpose torch.nn.functional.dropout torch.all torch.arange torch.ones tensorflow.train.list_variables torch.tensor torch.nn.Linear torch.zeros_like torch.nn.Embedding torch.nn.CrossEntropyLoss torch.nn.functional.relu torch.where torch.abs torch.full_like torch.matmul