# 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 transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCamelCase__    							= 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
UpperCamelCase__    							= {
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.1_5},
    '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
UpperCamelCase__    							= {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      UpperCamelCase__    							= '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",
]:
      UpperCamelCase__    							= 'allenai'




def        lowerCAmelCase_			(					__A							)					->    int:







      '''simple docstring'''


      UpperCAmelCase__								=  dict((re.sub(r"@@$",  "",  __A							), v) if k.endswith("@@"							) else (re.sub(r"$",  "</w>",  __A							), v) for k, v in d.items()							)
      UpperCAmelCase__								=  "<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
            del da[f"""{k}</w>"""]
            UpperCAmelCase__								=  d[k]  # restore
      return da




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''simple docstring'''


      assert os.path.exists(__A							)
      os.makedirs(__A,  exist_ok=__A							)
      print(f"""Writing results to {pytorch_dump_folder_path}"""							)

      # handle various types of models

      UpperCAmelCase__								=  basename(__A							)
      UpperCAmelCase__								=  dirname(__A							)

      UpperCAmelCase__								=  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      UpperCAmelCase__								=  cls.hub_models()
      UpperCAmelCase__								=  {"bpe": "fastbpe", "tokenizer": "moses"}
      UpperCAmelCase__								=  "."
      # 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}"""							)
      UpperCAmelCase__								=  hub_utils.from_pretrained(
          __A,  __A,  __A,  archive_map=__A,  **__A							)

      UpperCAmelCase__								=  vars(chkpt["args"]["model"]							)

      UpperCAmelCase__								=  args["source_lang"]
      UpperCAmelCase__								=  args["target_lang"]

      UpperCAmelCase__								=  dirname(__A							)
      UpperCAmelCase__								=  basename(__A							)

      # dicts
      UpperCAmelCase__								=  os.path.join(__A,  f"""dict.{src_lang}.txt"""							)
      UpperCAmelCase__								=  os.path.join(__A,  f"""dict.{tgt_lang}.txt"""							)

      UpperCAmelCase__								=  Dictionary.load(__A							)
      UpperCAmelCase__								=  rewrite_dict_keys(src_dict.indices							)
      UpperCAmelCase__								=  len(__A							)
      UpperCAmelCase__								=  os.path.join(__A,  "vocab-src.json"							)
      print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
      with open(__A,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(__A,  ensure_ascii=__A,  indent=__A							)							)

      # detect whether this is a do_lower_case situation, which can be derived by checking whether we
      # have at least one uppercase letter in the source vocab
      UpperCAmelCase__								=  True
      for k in src_vocab.keys():
            if not k.islower():
                  UpperCAmelCase__								=  False
                  break

      UpperCAmelCase__								=  Dictionary.load(__A							)
      UpperCAmelCase__								=  rewrite_dict_keys(tgt_dict.indices							)
      UpperCAmelCase__								=  len(__A							)
      UpperCAmelCase__								=  os.path.join(__A,  "vocab-tgt.json"							)
      print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
      with open(__A,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(__A,  ensure_ascii=__A,  indent=__A							)							)

      # merges_file (bpecodes)
      UpperCAmelCase__								=  os.path.join(__A,  VOCAB_FILES_NAMES["merges_file"]							)
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
            UpperCAmelCase__								=  os.path.join(__A,  __A							)
            if os.path.exists(__A							):
                  break
      with open(__A,  encoding="utf-8"							) as fin:
            UpperCAmelCase__								=  fin.read()
      UpperCAmelCase__								=  re.sub(r" \d+$",  "",  __A,  0,  re.M							)  # remove frequency number
      print(f"""Generating {merges_file}"""							)
      with open(__A,  "w",  encoding="utf-8"							) as fout:
            fout.write(__A							)

      # model config
      UpperCAmelCase__								=  os.path.join(__A,  "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"]}"""

      UpperCAmelCase__								=  {
          "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
      UpperCAmelCase__								=  5
      UpperCAmelCase__								=  False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            UpperCAmelCase__								=  best_score_hparams[model_dir]["length_penalty"]
      else:
            UpperCAmelCase__								=  1.0

      print(f"""Generating {fsmt_model_config_file}"""							)
      with open(__A,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(__A,  ensure_ascii=__A,  indent=__A							)							)

      # tokenizer config
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      UpperCAmelCase__								=  {
          "langs": [src_lang, tgt_lang],
          "model_max_length": 1_024,
          "do_lower_case": do_lower_case,
      }

      print(f"""Generating {fsmt_tokenizer_config_file}"""							)
      with open(__A,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(__A,  ensure_ascii=__A,  indent=__A							)							)

      # model
      UpperCAmelCase__								=  chkpt["models"][0]
      UpperCAmelCase__								=  model.state_dict()

      # rename keys to start with 'model.'
      UpperCAmelCase__								=  OrderedDict(("model." + k, v) for k, v in model_state_dict.items()							)

      # remove unneeded keys
      UpperCAmelCase__								=  [
          "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(__A,  __A							)

      UpperCAmelCase__								=  FSMTConfig.from_pretrained(__A							)
      UpperCAmelCase__								=  FSMTForConditionalGeneration(__A							)

      # check that it loads ok
      model_new.load_state_dict(__A,  strict=__A							)

      # save
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      print(f"""Generating {pytorch_weights_dump_path}"""							)
      torch.save(__A,  __A							)

      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__":
      UpperCamelCase__    							= 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.'
      )
      UpperCamelCase__    							= parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    							= {'tokenization_bertweet': ['BertweetTokenizer']}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      UpperCamelCase__    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



