


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




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Optional[Any]:
       # prep
       assert os.path.exists(snake_case					)
       os.makedirs(snake_case      ,							exist_ok=snake_case					)
       print(f'Writing results to {pytorch_dump_folder_path}'					)

       # handle various types of models

       lowercase__:       Optional[Any]        =       basename(snake_case					)
       lowercase__:       Union[str, Any]        =       dirname(snake_case					)

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

       lowercase__:       int        =       vars(chkpt['args']['model']					)

       lowercase__:       Optional[Any]        =       args['source_lang']
       lowercase__:       Dict        =       args['target_lang']

       lowercase__:       List[str]        =       dirname(snake_case					)
       lowercase__:       Any        =       basename(snake_case					)

       # dicts
       lowercase__:       int        =       os.path.join(snake_case      ,							f'dict.{src_lang}.txt'					)
       lowercase__:       str        =       os.path.join(snake_case      ,							f'dict.{tgt_lang}.txt'					)

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

       # 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
       lowercase__:       Optional[Any]        =       True
       for k in src_vocab.keys():
              if not k.islower():
                     lowercase__:       Tuple        =       False
                     break

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

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

       # model config
       lowercase__:       Any        =       os.path.join(snake_case      ,							'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"]}'

       lowercase__:       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': 0.0_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
       lowercase__:       Union[str, Any]        =       5
       lowercase__:       List[Any]        =       False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowercase__:       Tuple        =       best_score_hparams[model_dir]['length_penalty']
       else:
              lowercase__:       Union[str, Any]        =       1.0

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

       # tokenizer config
       lowercase__:       Optional[int]        =       os.path.join(snake_case      ,							snake_case					)

       lowercase__:       Any        =       {
           'langs': [src_lang, tgt_lang],
           'model_max_length': 10_24,
           'do_lower_case': do_lower_case,
       }

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

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

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

       # remove unneeded keys
       lowercase__:       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(snake_case      ,							snake_case					)

       lowercase__:       Dict        =       FSMTConfig.from_pretrained(snake_case					)
       lowercase__:       Union[str, Any]        =       FSMTForConditionalGeneration(snake_case					)

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

       # save
       lowercase__:       Tuple        =       os.path.join(snake_case      ,							snake_case					)
       print(f'Generating {pytorch_weights_dump_path}'					)
       torch.save(snake_case      ,							snake_case					)

       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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''▁'''

__lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowerCAmelCase			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowerCAmelCase			=							{'''vinai/bartpho-syllable''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="<unk>"    ,			lowerCAmelCase__="<pad>"    ,			lowerCAmelCase__="<mask>"    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			unk_token=lowerCAmelCase__    ,			sep_token=lowerCAmelCase__    ,			cls_token=lowerCAmelCase__    ,			pad_token=lowerCAmelCase__    ,			mask_token=lowerCAmelCase__    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**lowerCAmelCase__    ,			)

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       int        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(lowerCAmelCase__			)			)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
               if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase__:       Optional[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False			) ->    List[int]:




        '''simple docstring'''

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase__    ,			token_ids_a=lowerCAmelCase__    ,			already_has_special_tokens=lowerCAmelCase__			)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase__			)) + [1]
        return [1] + ([0] * len(lowerCAmelCase__			)) + [1, 1] + ([0] * len(lowerCAmelCase__			)) + [1]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       Dict        =       [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 + sep + token_ids_a + sep			) * [0]
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        else:
               return self.unk_token_id
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.vocab_file			):
               with open(lowerCAmelCase__    ,			'wb'			) as fi:
                      lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__			)

        if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
            lowerCAmelCase__			) and os.path.isfile(self.monolingual_vocab_file			):
               copyfile(self.monolingual_vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.monolingual_vocab_file			):
               with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as fp:
                      for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                    fp.write(F'{str(lowerCAmelCase__			)} \n'			)

        return out_vocab_file, out_monolingual_vocab_file


