




# 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()

lowerCAmelCase_					    = 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
lowerCAmelCase_					    = {
    # 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
lowerCAmelCase_					    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     lowerCAmelCase_					    = """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",
]:
     lowerCAmelCase_					    = """allenai"""



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  # (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}
  lowerCAmelCase__				:  Union[str, Any]		       =     dict((re.sub(R'''@@$'''	,      ''''''	,      UpperCamelCase      ), v) if k.endswith('''@@'''      ) else (re.sub(R'''$'''	,      '''</w>'''	,      UpperCamelCase      ), v) for k, v in d.items()      )
  lowerCAmelCase__				:  Dict		       =     '''<s> <pad> </s> <unk>'''.split()
  # restore the special tokens
  for k in keep_keys:
    del da[F"""{k}</w>"""]
    lowerCAmelCase__				:  Optional[int]		       =     d[k]  # restore
  return da



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  # prep
  assert os.path.exists(UpperCamelCase      )
  os.makedirs(UpperCamelCase	,      exist_ok=UpperCamelCase      )
  print(F"""Writing results to {pytorch_dump_folder_path}"""      )

  # handle various types of models

  lowerCAmelCase__				:  Optional[int]		       =     basename(UpperCamelCase      )
  lowerCAmelCase__				:  List[str]		       =     dirname(UpperCamelCase      )

  lowerCAmelCase__				:  int		       =     fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  lowerCAmelCase__				:  str		       =     cls.hub_models()
  lowerCAmelCase__				:  Dict		       =     {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
  lowerCAmelCase__				:  int		       =     '''.'''
  # 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}"""      )
  lowerCAmelCase__				:  List[Any]		       =     hub_utils.from_pretrained(
      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      archive_map=UpperCamelCase	,      **UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     vars(chkpt['''args''']['''model''']      )

  lowerCAmelCase__				:  str		       =     args['''source_lang''']
  lowerCAmelCase__				:  Any		       =     args['''target_lang''']

  lowerCAmelCase__				:  str		       =     dirname(UpperCamelCase      )
  lowerCAmelCase__				:  Dict		       =     basename(UpperCamelCase      )

  # dicts
  lowerCAmelCase__				:  Dict		       =     os.path.join(UpperCamelCase	,      F"""dict.{src_lang}.txt"""      )
  lowerCAmelCase__				:  List[Any]		       =     os.path.join(UpperCamelCase	,      F"""dict.{tgt_lang}.txt"""      )

  lowerCAmelCase__				:  Any		       =     Dictionary.load(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     rewrite_dict_keys(src_dict.indices      )
  lowerCAmelCase__				:  int		       =     len(UpperCamelCase      )
  lowerCAmelCase__				:  Dict		       =     os.path.join(UpperCamelCase	,      '''vocab-src.json'''      )
  print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""      )
  with open(UpperCamelCase	,      '''w'''	,      encoding='''utf-8'''      ) as f:
    f.write(json.dumps(UpperCamelCase	,      ensure_ascii=UpperCamelCase	,      indent=UpperCamelCase      )      )

  # 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
  lowerCAmelCase__				:  Dict		       =     True
  for k in src_vocab.keys():
    if not k.islower():
      lowerCAmelCase__				:  Any		       =     False
      break

  lowerCAmelCase__				:  Any		       =     Dictionary.load(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[int]		       =     rewrite_dict_keys(tgt_dict.indices      )
  lowerCAmelCase__				:  Optional[Any]		       =     len(UpperCamelCase      )
  lowerCAmelCase__				:  Tuple		       =     os.path.join(UpperCamelCase	,      '''vocab-tgt.json'''      )
  print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""      )
  with open(UpperCamelCase	,      '''w'''	,      encoding='''utf-8'''      ) as f:
    f.write(json.dumps(UpperCamelCase	,      ensure_ascii=UpperCamelCase	,      indent=UpperCamelCase      )      )

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

  # model config
  lowerCAmelCase__				:  Union[str, Any]		       =     os.path.join(UpperCamelCase	,      '''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"]}"""

  lowerCAmelCase__				:  Optional[Any]		       =     {
      '''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
  lowerCAmelCase__				:  Optional[int]		       =     5
  lowerCAmelCase__				:  Optional[int]		       =     False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    lowerCAmelCase__				:  Tuple		       =     best_score_hparams[model_dir]['''length_penalty''']
  else:
    lowerCAmelCase__				:  int		       =     1.0

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

  # tokenizer config
  lowerCAmelCase__				:  Optional[Any]		       =     os.path.join(UpperCamelCase	,      UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     {
      '''langs''': [src_lang, tgt_lang],
      '''model_max_length''': 1024,
      '''do_lower_case''': do_lower_case,
  }

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

  # model
  lowerCAmelCase__				:  Optional[Any]		       =     chkpt['''models'''][0]
  lowerCAmelCase__				:  Optional[int]		       =     model.state_dict()

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

  # remove unneeded keys
  lowerCAmelCase__				:  Union[str, Any]		       =     [
      '''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(UpperCamelCase	,      UpperCamelCase      )

  lowerCAmelCase__				:  Union[str, Any]		       =     FSMTConfig.from_pretrained(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     FSMTForConditionalGeneration(UpperCamelCase      )

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

  # save
  lowerCAmelCase__				:  Union[str, Any]		       =     os.path.join(UpperCamelCase	,      UpperCamelCase      )
  print(F"""Generating {pytorch_weights_dump_path}"""      )
  torch.save(UpperCamelCase	,      UpperCamelCase      )

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







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
          # modified it, it will not be accepted here again, since `auto` values would have been overridden
          lowerCAmelCase__				:  Union[str, Any]		       =     deepcopy(__UpperCAmelCase		)
        elif os.path.exists(__UpperCAmelCase		):
          with io.open(__UpperCAmelCase      ,				'''r'''      ,				encoding='''utf-8'''		) as f:
            lowerCAmelCase__				:  int		       =     json.load(__UpperCAmelCase		)
        else:
          try:
            lowerCAmelCase__				:  Union[str, Any]		       =     baseaa.urlsafe_baadecode(__UpperCAmelCase		).decode('''utf-8'''		)
            lowerCAmelCase__				:  Optional[int]		       =     json.loads(__UpperCAmelCase		)
          except (UnicodeDecodeError, AttributeError, ValueError):
            raise ValueError(
                f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""		)

        lowerCAmelCase__				:  Optional[Any]		       =     config

        self.set_stage_and_offload()

      def 	__magic_name__(   self		):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value('''zero_optimization.stage'''      ,				-1		)

        # offload
        lowerCAmelCase__				:  Dict		       =     False
        if self.is_zeroa() or self.is_zeroa():
          lowerCAmelCase__				:  Dict		       =     set(['''cpu''', '''nvme''']		)
          lowerCAmelCase__				:  Optional[Any]		       =     set(
              [
                  self.get_value('''zero_optimization.offload_optimizer.device'''		),
                  self.get_value('''zero_optimization.offload_param.device'''		),
              ]		)
          if len(offload_devices & offload_devices_valid		) > 0:
            lowerCAmelCase__				:  List[Any]		       =     True

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  Tuple		       =     ds_key_long.split('''.'''		)
        lowerCAmelCase__				:  int		       =     nodes.pop()
        for node in nodes:
          lowerCAmelCase__				:  int		       =     config.get(__UpperCAmelCase		)
          if config is None:
            return None, ds_key

        return config, ds_key

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.find_config_node(__UpperCAmelCase		)
        if config is None:
          return default
        return config.get(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  Dict		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  str		       =     ds_key_long.split('''.'''		)
        for node in nodes:
          lowerCAmelCase__				:  Dict		       =     config
          lowerCAmelCase__				:  List[str]		       =     config.get(__UpperCAmelCase		)
          if config is None:
            if must_exist:
              raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""		)
            else:
              return

        # if found remove it
        if parent_config is not None:
          parent_config.pop(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else bool(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else not bool(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        return self._stage == 2

      def 	__magic_name__(   self		):
        return self._stage == 3







      def 	__magic_name__(   self		):
        return self._offload






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     engine







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__UpperCAmelCase      ,				**__UpperCAmelCase		)

        # Deepspeed's `engine.step` performs the following operations:
        # - gradient accumulation check
        # - gradient clipping
        # - optimizer step
        # - zero grad
        # - checking overflow
        # - lr_scheduler step (only if engine.lr_scheduler is not None)
        self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				device_placement=__UpperCAmelCase      ,				scaler=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     hasattr(self.optimizer      ,				'''overflow'''		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def 	__magic_name__(   self		):
        if self.__has_overflow__:
          return self.optimizer.overflow
        return False






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0.001      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     params
        lowerCAmelCase__				:  str		       =     lr
        lowerCAmelCase__				:  List[str]		       =     weight_decay
        lowerCAmelCase__				:  Dict		       =     kwargs






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     optimizer
        lowerCAmelCase__				:  Optional[Any]		       =     total_num_steps
        lowerCAmelCase__				:  str		       =     warmup_num_steps
        lowerCAmelCase__				:  Dict		       =     kwargs

