


# 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()

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




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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   # prep
   assert os.path.exists(lowerCAmelCase__   )
   os.makedirs(lowerCAmelCase__		,				exist_ok=lowerCAmelCase__   )
   print(f'''Writing results to {pytorch_dump_folder_path}'''   )

   # handle various types of models

   __a					=  basename(lowerCAmelCase__   )
   __a					=  dirname(lowerCAmelCase__   )

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

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

   __a					=  args["""source_lang"""]
   __a					=  args["""target_lang"""]

   __a					=  dirname(lowerCAmelCase__   )
   __a					=  basename(lowerCAmelCase__   )

   # dicts
   __a					=  os.path.join(lowerCAmelCase__		,				f'''dict.{src_lang}.txt'''   )
   __a					=  os.path.join(lowerCAmelCase__		,				f'''dict.{tgt_lang}.txt'''   )

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

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

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

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

   # model config
   __a					=  os.path.join(lowerCAmelCase__		,				"""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']}'''

   __a					=  {
       """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""": 0.0_2,
       """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
   __a					=  5
   __a					=  False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      __a					=  best_score_hparams[model_dir]["""length_penalty"""]
   else:
      __a					=  1.0

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

   # tokenizer config
   __a					=  os.path.join(lowerCAmelCase__		,				lowerCAmelCase__   )

   __a					=  {
       """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(lowerCAmelCase__		,				"""w"""		,				encoding="""utf-8"""   ) as f:
      f.write(json.dumps(lowerCAmelCase__		,				ensure_ascii=lowerCAmelCase__		,				indent=lowerCAmelCase__   )   )

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

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

   # remove unneeded keys
   __a					=  [
       """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(lowerCAmelCase__		,				lowerCAmelCase__   )

   __a					=  FSMTConfig.from_pretrained(lowerCAmelCase__   )
   __a					=  FSMTForConditionalGeneration(lowerCAmelCase__   )

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

   # save
   __a					=  os.path.join(lowerCAmelCase__		,				lowerCAmelCase__   )
   print(f'''Generating {pytorch_weights_dump_path}'''   )
   torch.save(lowerCAmelCase__		,				lowerCAmelCase__   )

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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE							    =							{'mobilebert-uncased': 5_1_2}


SCREAMING_SNAKE_CASE							    =							{}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      MobileBertTokenizer


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=True  ,       __A="[UNK]"  ,       __A="[SEP]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A=True  ,       __A=None  ,       **__A  ,       ):
        super().__init__(
            __A  ,       tokenizer_file=__A  ,       do_lower_case=__A  ,       unk_token=__A  ,       sep_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       tokenize_chinese_chars=__A  ,       strip_accents=__A  ,       **__A  ,       )

        __a					=  json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
        if (
            normalizer_state.get("""lowercase"""  ,       __A    ) != do_lower_case
            or normalizer_state.get("""strip_accents"""  ,       __A    ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""  ,       __A    ) != tokenize_chinese_chars
        ):
           __a					=  getattr(__A  ,       normalizer_state.pop("""type"""    )    )
           __a					=  do_lower_case
           __a					=  strip_accents
           __a					=  tokenize_chinese_chars
           __a					=  normalizer_class(**__A    )

        __a					=  do_lower_case


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a					=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

        return output


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [self.cls_token_id]
        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  self._tokenizer.model.save(__A  ,       name=__A    )
        return tuple(__A    )



