code: def convert_bort_checkpoint_to_pytorch(
      bort_checkpoint_path: str, pytorch_dump_folder_path: str
  ):
      """
      Convert the original Bort checkpoint (based on MXNET and Gluonnlp) to our BERT structure-
      """
  
      # Original Bort configuration
      bort_4_8_768_1024_hparams = {
          "attention_cell": "multi_head",
          "num_layers": 4,
          "units": 1024,
          "hidden_size": 768,
          "max_length": 512,
          "num_heads": 8,
          "scaled": True,
          "dropout": 0.1,
          "use_residual": True,
          "embed_size": 1024,
          "embed_dropout": 0.1,
          "word_embed": None,
          "layer_norm_eps": 1e-5,
          "token_type_vocab_size": 2,
      }
  
      predefined_args = bort_4_8_768_1024_hparams
  
      # Let's construct the original Bort model here
      # Taken from official BERT implementation, see:
      # https://github.com/alexa/bort/blob/master/bort/bort.py
      encoder = BERTEncoder(
          attention_cell=predefined_args["attention_cell"],
          num_layers=predefined_args["num_layers"],
          units=predefined_args["units"],
          hidden_size=predefined_args["hidden_size"],
          max_length=predefined_args["max_length"],
          num_heads=predefined_args["num_heads"],
          scaled=predefined_args["scaled"],
          dropout=predefined_args["dropout"],
          output_attention=False,
          output_all_encodings=False,
          use_residual=predefined_args["use_residual"],
          activation=predefined_args.get("activation", "gelu"),
          layer_norm_eps=predefined_args.get("layer_norm_eps", None),
      )
  
      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      vocab_name = "openwebtext_ccnews_stories_books_cased"
  
      # Specify download folder to Gluonnlp's vocab
      gluon_cache_dir = os.path.join(get_home_dir(), "models")
      bort_vocab = _load_vocab(
          vocab_name, None, gluon_cache_dir, cls=Vocab
      )
  
      original_bort = nlp.model.BERTModel(
          encoder,
          len(bort_vocab),
          units=predefined_args["units"],
          embed_size=predefined_args["embed_size"],
          embed_dropout=predefined_args["embed_dropout"],
          word_embed=predefined_args["word_embed"],
          use_pooler=False,
          use_token_type_embed=False,
          token_type_vocab_size=predefined_args[
              "token_type_vocab_size"
          ],
          use_classifier=False,
          use_decoder=False,
      )
  
      original_bort.load_parameters(
          bort_checkpoint_path, cast_dtype=True, ignore_extra=True
      )
      params = original_bort._collect_params_with_prefix()
  
      # Build our config 🤗
      hf_bort_config_json = {
          "architectures": ["BertForMaskedLM"],
          "attention_probs_dropout_prob": predefined_args["dropout"],
          "hidden_act": "gelu",
          "hidden_dropout_prob": predefined_args["dropout"],
          "hidden_size": predefined_args["embed_size"],
          "initializer_range": 0.02,
          "intermediate_size": predefined_args["hidden_size"],
          "layer_norm_eps": predefined_args["layer_norm_eps"],
          "max_position_embeddings": predefined_args["max_length"],
          "model_type": "bort",
          "num_attention_heads": predefined_args["num_heads"],
          "num_hidden_layers": predefined_args["num_layers"],
          "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
          "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
          "vocab_size": len(bort_vocab),
      }
  
      hf_bort_config = BertConfig.from_dict(hf_bort_config_json)
      hf_bort_model = BertForMaskedLM(hf_bort_config)
      hf_bort_model.eval()
  
      # Parameter mapping table (Gluonnlp to Transformers)
      # * denotes layer index
      #
      # | Gluon Parameter                                                | Transformers Parameter
      # | -------------------------------------------------------------- | ----------------------
      # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
      # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
      # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
      # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
      # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
      # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
      # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`
  
      # Helper function to convert MXNET Arrays to PyTorch
      def to_torch(mx_array) -> nn.Parameter:
          return nn.Parameter(
              torch.FloatTensor(mx_array.data().asnumpy())
          )
  
      # Check param shapes and map new HF param back
      def check_and_map_params(hf_param, gluon_param):
          shape_hf = hf_param.shape
  
          gluon_param = to_torch(params[gluon_param])
          shape_gluon = gluon_param.shape
  
          assert (
              shape_hf == shape_gluon
          ), f"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"
  
          return gluon_param
  
      hf_bort_model.bert.embeddings.word_embeddings.weight = (
          check_and_map_params(
              hf_bort_model.bert.embeddings.word_embeddings.weight,
              "word_embed.0.weight",
          )
      )
      hf_bort_model.bert.embeddings.position_embeddings.weight = (
          check_and_map_params(
              hf_bort_model.bert.embeddings.position_embeddings.weight,
              "encoder.position_weight",
          )
      )
      hf_bort_model.bert.embeddings.LayerNorm.bias = (
          check_and_map_params(
              hf_bort_model.bert.embeddings.LayerNorm.bias,
              "encoder.layer_norm.beta",
          )
      )
      hf_bort_model.bert.embeddings.LayerNorm.weight = (
          check_and_map_params(
              hf_bort_model.bert.embeddings.LayerNorm.weight,
              "encoder.layer_norm.gamma",
          )
      )
  
      # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
      hf_bort_model.bert.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data
      )
  
      for i in range(hf_bort_config.num_hidden_layers):
          layer: BertLayer = hf_bort_model.bert.encoder.layer[i]
  
          # self attention
          self_attn: BertSelfAttention = layer.attention.self
  
          self_attn.key.bias.data = check_and_map_params(
              self_attn.key.bias.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias",
          )
  
          self_attn.key.weight.data = check_and_map_params(
              self_attn.key.weight.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight",
          )
          self_attn.query.bias.data = check_and_map_params(
              self_attn.query.bias.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias",
          )
          self_attn.query.weight.data = check_and_map_params(
              self_attn.query.weight.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight",
          )
          self_attn.value.bias.data = check_and_map_params(
              self_attn.value.bias.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias",
          )
          self_attn.value.weight.data = check_and_map_params(
              self_attn.value.weight.data,
              f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight",
          )
  
          # self attention output
          self_output: BertSelfOutput = layer.attention.output
  
          self_output.dense.bias = check_and_map_params(
              self_output.dense.bias,
              f"encoder.transformer_cells.{i}.proj.bias",
          )
          self_output.dense.weight = check_and_map_params(
              self_output.dense.weight,
              f"encoder.transformer_cells.{i}.proj.weight",
          )
          self_output.LayerNorm.bias = check_and_map_params(
              self_output.LayerNorm.bias,
              f"encoder.transformer_cells.{i}.layer_norm.beta",
          )
          self_output.LayerNorm.weight = check_and_map_params(
              self_output.LayerNorm.weight,
              f"encoder.transformer_cells.{i}.layer_norm.gamma",
          )
  
          # intermediate
          intermediate: BertIntermediate = layer.intermediate
  
          intermediate.dense.bias = check_and_map_params(
              intermediate.dense.bias,
              f"encoder.transformer_cells.{i}.ffn.ffn_1.bias",
          )
          intermediate.dense.weight = check_and_map_params(
              intermediate.dense.weight,
              f"encoder.transformer_cells.{i}.ffn.ffn_1.weight",
          )
  
          # output
          bert_output: BertOutput = layer.output
  
          bert_output.dense.bias = check_and_map_params(
              bert_output.dense.bias,
              f"encoder.transformer_cells.{i}.ffn.ffn_2.bias",
          )
          bert_output.dense.weight = check_and_map_params(
              bert_output.dense.weight,
              f"encoder.transformer_cells.{i}.ffn.ffn_2.weight",
          )
          bert_output.LayerNorm.bias = check_and_map_params(
              bert_output.LayerNorm.bias,
              f"encoder.transformer_cells.{i}.ffn.layer_norm.beta",
          )
          bert_output.LayerNorm.weight = check_and_map_params(
              bert_output.LayerNorm.weight,
              f"encoder.transformer_cells.{i}.ffn.layer_norm.gamma",
          )
  
      # Save space and energy 🎄
      hf_bort_model.half()
  
      # Compare output of both models
      tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
  
      input_ids = tokenizer.encode_plus(SAMPLE_TEXT)["input_ids"]
  
      # Get gluon output
      gluon_input_ids = mx.nd.array([input_ids])
      output_gluon = original_bort(
          inputs=gluon_input_ids, token_types=[]
      )
  
      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(pytorch_dump_folder_path)
      hf_bort_model = BertModel.from_pretrained(
          pytorch_dump_folder_path
      )
      hf_bort_model.eval()
  
      input_ids = tokenizer.encode_plus(
          SAMPLE_TEXT, return_tensors="pt"
      )
      output_hf = hf_bort_model(**input_ids)[0]
  
      gluon_layer = output_gluon[0].asnumpy()
      hf_layer = output_hf[0].detach().numpy()
  
      max_absolute_diff = np.max(np.abs(hf_layer - gluon_layer)).item()
      success = np.allclose(gluon_layer, hf_layer, atol=1e-3)
  
      if success:
          print("✔️ Both model do output the same tensors")
      else:
          print("❌ Both model do **NOT** output the same tensors")
          print("Absolute difference is:", max_absolute_diff)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/bort/convert_bort_original_gluonnlp_checkpoint_to_pytorch.py
imports: import argparse
  import os
  import numpy
  import torch
  from packaging import version
  from torch import nn
  import gluonnlp
  import mxnet
  from gluonnlp.base import get_home_dir
  from gluonnlp.model.bert import BERTEncoder
  from gluonnlp.model.utils import _load_vocab
  from gluonnlp.vocab import Vocab
  from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
  from transformers.models.bert.modeling_bert import BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_bort_checkpoint_to_pytorch(bort_checkpoint_path, pytorch_dump_folder_path) and returns: nn, gluon_param