text: # coding=utf-8
  # Copyright 2018 The 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.
  
  # Note: if you intend to run this script make sure you look under scripts/fsmt/
  # to locate the appropriate script to do the work correctly. There is a set of scripts to:
  # - download and prepare data and run the conversion script
  # - perform eval to get the best hparam into the config
  # - generate model_cards - useful if you have multiple models from the same paper
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from os.path import basename, dirname
  
  import fairseq
  import torch
  from fairseq import hub_utils
  from fairseq.data.dictionary import Dictionary
  
  from transformers4 import FSMTConfig, FSMTForConditionalGeneration
  from transformers4.file_utils import WEIGHTS_NAME
  from transformers4.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
  from transformers4.tokenization_utils_base import TOKENIZER_CONFIG_FILE
  from transformers4.utils import logging
  
  
  logging.set_verbosity_warning()
  
  json_indent = 2
  
  # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
  # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
  #
  # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
  # * `early_stopping`: `False` consistently scored better
  # * `length_penalty` varied, so will assign the best one depending on the model
  best_score_hparams = {
      # fairseq:
      "wmt19-ru-en": {"length_penalty": 1.1},
      "wmt19-en-ru": {"length_penalty": 1.15},
      "wmt19-en-de": {"length_penalty": 1.0},
      "wmt19-de-en": {"length_penalty": 1.1},
      # allenai:
      "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
      "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
      "wmt16-en-de-12-1": {"length_penalty": 0.8},
      "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
      "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
  }
  
  # this remaps the different models to their organization names
  org_names = {}
  for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      org_names[m] = "facebook"
  for m in [
      "wmt16-en-de-dist-12-1",
      "wmt16-en-de-dist-6-1",
      "wmt16-en-de-12-1",
      "wmt19-de-en-6-6-base",
      "wmt19-de-en-6-6-big",
  ]:
      org_names[m] = "allenai"
  
  
  def rewrite_dict_keys(d):
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      d2 = dict((re.sub(r"@@$", "", k), v) if k.endswith("@@") else (re.sub(r"$", "</w>", k), v) for k, v in d.items())
      keep_keys = "<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
          del d2[f"{k}</w>"]
          d2[k] = d[k]  # restore
      return d2
  
  
  def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder_path):
  
      # prep
      assert os.path.exists(fsmt_checkpoint_path)
      os.makedirs(pytorch_dump_folder_path, exist_ok=True)
      print(f"Writing results to {pytorch_dump_folder_path}")
  
      # handle various types of models
  
      checkpoint_file = basename(fsmt_checkpoint_path)
      fsmt_folder_path = dirname(fsmt_checkpoint_path)
  
      cls = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      models = cls.hub_models()
      kwargs = {"bpe": "fastbpe", "tokenizer": "moses"}
      data_name_or_path = "."
      # note: since the model dump is old, fairseq has upgraded its model some
      # time later, and it does a whole lot of rewrites and splits on the saved
      # weights, therefore we can't use torch.load() directly on the model file.
      # see: upgrade_state_dict(state_dict) in fairseq_model.py
      print(f"using checkpoint {checkpoint_file}")
      chkpt = hub_utils.from_pretrained(
          fsmt_folder_path, checkpoint_file, data_name_or_path, archive_map=models, **kwargs
      )
  
      args = vars(chkpt["args"]["model"])
  
      src_lang = args["source_lang"]
      tgt_lang = args["target_lang"]
  
      data_root = dirname(pytorch_dump_folder_path)
      model_dir = basename(pytorch_dump_folder_path)
  
      # dicts
      src_dict_file = os.path.join(fsmt_folder_path, f"dict.{src_lang}.txt")
      tgt_dict_file = os.path.join(fsmt_folder_path, f"dict.{tgt_lang}.txt")
  
      src_dict = Dictionary.load(src_dict_file)
      src_vocab = rewrite_dict_keys(src_dict.indices)
      src_vocab_size = len(src_vocab)
      src_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-src.json")
      print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records")
      with open(src_vocab_file, "w", encoding="utf-8") as f:
          f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))
  
      # detect whether this is a do_lower_case situation, which can be derived by checking whether we
      # have at least one upcase letter in the source vocab
      do_lower_case = True
      for k in src_vocab.keys():
          if not k.islower():
              do_lower_case = False
              break
  
      tgt_dict = Dictionary.load(tgt_dict_file)
      tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
      tgt_vocab_size = len(tgt_vocab)
      tgt_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-tgt.json")
      print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records")
      with open(tgt_vocab_file, "w", encoding="utf-8") as f:
          f.write(json.dumps(tgt_vocab, ensure_ascii=False, indent=json_indent))
  
      # merges_file (bpecodes)
      merges_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["merges_file"])
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
          fsmt_merges_file = os.path.join(fsmt_folder_path, fn)
          if os.path.exists(fsmt_merges_file):
              break
      with open(fsmt_merges_file, encoding="utf-8") as fin:
          merges = fin.read()
      merges = re.sub(r" \d+$", "", merges, 0, re.M)  # remove frequency number
      print(f"Generating {merges_file}")
      with open(merges_file, "w", encoding="utf-8") as fout:
          fout.write(merges)
  
      # model config
      fsmt_model_config_file = os.path.join(pytorch_dump_folder_path, "config.json")
  
      # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
      # may have to modify the tokenizer if a different type is used by a future model
      assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
      assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"
  
      model_conf = {
          "architectures": ["FSMTForConditionalGeneration"],
          "model_type": "fsmt",
          "activation_dropout": args["activation_dropout"],
          "activation_function": "relu",
          "attention_dropout": args["attention_dropout"],
          "d_model": args["decoder_embed_dim"],
          "dropout": args["dropout"],
          "init_std": 0.02,
          "max_position_embeddings": args["max_source_positions"],
          "num_hidden_layers": args["encoder_layers"],
          "src_vocab_size": src_vocab_size,
          "tgt_vocab_size": tgt_vocab_size,
          "langs": [src_lang, tgt_lang],
          "encoder_attention_heads": args["encoder_attention_heads"],
          "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
          "encoder_layerdrop": args["encoder_layerdrop"],
          "encoder_layers": args["encoder_layers"],
          "decoder_attention_heads": args["decoder_attention_heads"],
          "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
          "decoder_layerdrop": args["decoder_layerdrop"],
          "decoder_layers": args["decoder_layers"],
          "bos_token_id": 0,
          "pad_token_id": 1,
          "eos_token_id": 2,
          "is_encoder_decoder": True,
          "scale_embedding": not args["no_scale_embedding"],
          "tie_word_embeddings": args["share_all_embeddings"],
      }
  
      # good hparam defaults to start with
      model_conf["num_beams"] = 5
      model_conf["early_stopping"] = False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
          model_conf["length_penalty"] = best_score_hparams[model_dir]["length_penalty"]
      else:
          model_conf["length_penalty"] = 1.0
  
      print(f"Generating {fsmt_model_config_file}")
      with open(fsmt_model_config_file, "w", encoding="utf-8") as f:
          f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))
  
      # tokenizer config
      fsmt_tokenizer_config_file = os.path.join(pytorch_dump_folder_path, TOKENIZER_CONFIG_FILE)
  
      tokenizer_conf = {
          "langs": [src_lang, tgt_lang],
          "model_max_length": 1024,
          "do_lower_case": do_lower_case,
      }
  
      print(f"Generating {fsmt_tokenizer_config_file}")
      with open(fsmt_tokenizer_config_file, "w", encoding="utf-8") as f:
          f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))
  
      # model
      model = chkpt["models"][0]
      model_state_dict = model.state_dict()
  
      # rename keys to start with 'model.'
      model_state_dict = OrderedDict(("model." + k, v) for k, v in model_state_dict.items())
  
      # remove unneeded keys
      ignore_keys = [
          "model.model",
          "model.encoder.version",
          "model.decoder.version",
          "model.encoder_embed_tokens.weight",
          "model.decoder_embed_tokens.weight",
          "model.encoder.embed_positions._float_tensor",
          "model.decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
          model_state_dict.pop(k, None)
  
      config = FSMTConfig.from_pretrained(pytorch_dump_folder_path)
      model_new = FSMTForConditionalGeneration(config)
  
      # check that it loads ok
      model_new.load_state_dict(model_state_dict, strict=False)
  
      # save
      pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
      print(f"Generating {pytorch_weights_dump_path}")
      torch.save(model_state_dict, pytorch_weights_dump_path)
  
      print("Conversion is done!")
      print("\nLast step is to upload the files to s3")
      print(f"cd {data_root}")
      print(f"transformers-cli upload {model_dir}")
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--fsmt_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help="Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts, bpecodes, etc.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      args = parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
  