




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



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

  # handle various types of models

  lowerCAmelCase__				:  List[Any]		       =     basename(_lowercase      )
  lowerCAmelCase__				:  Any		       =     dirname(_lowercase      )

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

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

  lowerCAmelCase__				:  Dict		       =     args['source_lang']
  lowerCAmelCase__				:  List[Any]		       =     args['target_lang']

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

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

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

  # 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__				:  Dict		       =     False
      break

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

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

  # model config
  lowerCAmelCase__				:  Tuple		       =     os.path.join(_lowercase	,      '''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__				:  Any		       =     5
  lowerCAmelCase__				:  Optional[Any]		       =     False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    lowerCAmelCase__				:  List[Any]		       =     best_score_hparams[model_dir]['length_penalty']
  else:
    lowerCAmelCase__				:  Dict		       =     1.0

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

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

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

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

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

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

  # remove unneeded keys
  lowerCAmelCase__				:  Optional[int]		       =     [
      '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(_lowercase	,      _lowercase      )

  lowerCAmelCase__				:  Dict		       =     FSMTConfig.from_pretrained(_lowercase      )
  lowerCAmelCase__				:  Optional[Any]		       =     FSMTForConditionalGeneration(_lowercase      )

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

  # save
  lowerCAmelCase__				:  Any		       =     os.path.join(_lowercase	,      _lowercase      )
  print(F"""Generating {pytorch_weights_dump_path}"""      )
  torch.save(_lowercase	,      _lowercase      )

  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)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

