





# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      Tuple					 =		{
        'en': 'Machine learning is great, isn\'t it?',
        'ru': 'Машинное обучение - это здорово, не так ли?',
        'de': 'Maschinelles Lernen ist großartig, oder?',
    }

    # BLUE scores as follows:
    # "pair": [fairseq, transformers]
    lowerCamelCase__    :      Tuple					 =		{
        'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
        'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
        'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
        'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
    }
    lowerCamelCase__    :      str					 =		F"""{src_lang}-{tgt_lang}"""

    lowerCamelCase__    :      Any					 =		F"""
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR's WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

"""
    os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		os.path.join(_UpperCAmelCase						,	'README.md' )
    print(F"""Generating {path}""" )
    with open(_UpperCAmelCase						,	'w'						,	encoding='utf-8' ) as f:
        f.write(_UpperCAmelCase )


# make sure we are under the root of the project
_UpperCAmelCase				:       Tuple	   =				Path(__file__).resolve().parent.parent.parent
_UpperCAmelCase				:       List[Any]	   =				repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    _UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase				:       int	   =				model_name.split("""-""")
    _UpperCAmelCase				:       Tuple	   =				model_cards_dir / """facebook""" / model_name
    write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)












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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_nllb import NllbTokenizer
else:
    _UpperCAmelCase				:       int	   =				None


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       Optional[int]	   =				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       List[Any]	   =				{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase				:       List[str]	   =				{
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_UpperCAmelCase				:       Optional[int]	   =				["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 UpperCAmelCase__													=		NllbTokenizer

 UpperCAmelCase__													=		[]
 UpperCAmelCase__													=		[]
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      int=None			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      str="<s>"			,     UpperCAmelCase  :      Optional[Any]="</s>"			,     UpperCAmelCase  :      str="</s>"			,     UpperCAmelCase  :      Tuple="<s>"			,     UpperCAmelCase  :      Optional[Any]="<unk>"			,     UpperCAmelCase  :      List[str]="<pad>"			,     UpperCAmelCase  :      Union[str, Any]="<mask>"			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      int=None			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      Any=False			,     **UpperCAmelCase  :      Optional[int]			,     )			->	Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     lowerCamelCase__    :      List[Any]					 =		AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase			,     UpperCAmelCase	) else mask_token
     lowerCamelCase__    :      Union[str, Any]					 =		legacy_behaviour
     super().__init__(
         vocab_file=UpperCAmelCase			,     tokenizer_file=UpperCAmelCase			,     bos_token=UpperCAmelCase			,     eos_token=UpperCAmelCase			,     sep_token=UpperCAmelCase			,     cls_token=UpperCAmelCase			,     unk_token=UpperCAmelCase			,     pad_token=UpperCAmelCase			,     mask_token=UpperCAmelCase			,     src_lang=UpperCAmelCase			,     tgt_lang=UpperCAmelCase			,     additional_special_tokens=UpperCAmelCase			,     legacy_behaviour=UpperCAmelCase			,     **UpperCAmelCase			,     )

     lowerCamelCase__    :      List[Any]					 =		vocab_file
     lowerCamelCase__    :      Dict					 =		False if not self.vocab_file else True

     lowerCamelCase__    :      Optional[Any]					 =		FAIRSEQ_LANGUAGE_CODES.copy()

     if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         _additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in _additional_special_tokens]	)

     self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}	)
     lowerCamelCase__    :      str					 =		{
         lang_code: self.convert_tokens_to_ids(UpperCAmelCase	) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     lowerCamelCase__    :      int					 =		src_lang if src_lang is not None else 'eng_Latn'
     lowerCamelCase__    :      List[Any]					 =		self.convert_tokens_to_ids(self._src_lang	)
     lowerCamelCase__    :      str					 =		tgt_lang
     self.set_src_lang_special_tokens(self._src_lang	)
 @property
 def      A_	(      self  :      int	)			->	str:
     return self._src_lang
 @src_lang.setter
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Any					 =		new_src_lang
     self.set_src_lang_special_tokens(self._src_lang	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Dict					 =		[self.sep_token_id]
     lowerCamelCase__    :      List[str]					 =		[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]
 def      A_	(      self  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str]			,     UpperCAmelCase  :      Optional[str]			,     **UpperCAmelCase  :      List[str]	)			->	Dict:
     if src_lang is None or tgt_lang is None:
         raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'	)
     lowerCamelCase__    :      Optional[int]					 =		src_lang
     lowerCamelCase__    :      Optional[int]					 =		self(UpperCAmelCase			,     add_special_tokens=UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		tgt_lang_id
     return inputs
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str = "eng_Latn"			,     UpperCAmelCase  :      Optional[List[str]] = None			,     UpperCAmelCase  :      str = "fra_Latn"			,     **UpperCAmelCase  :      Dict			,     )			->	BatchEncoding:
     lowerCamelCase__    :      Any					 =		src_lang
     lowerCamelCase__    :      int					 =		tgt_lang
     return super().prepare_seqaseq_batch(UpperCAmelCase			,     UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	Optional[int]:
     return self.set_src_lang_special_tokens(self.src_lang	)
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     return self.set_tgt_lang_special_tokens(self.tgt_lang	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Optional[Any]	)			->	None:
     lowerCamelCase__    :      int					 =		self.convert_tokens_to_ids(UpperCAmelCase	)

     if self.legacy_behaviour:
         lowerCamelCase__    :      int					 =		[]
         lowerCamelCase__    :      str					 =		[self.eos_token_id, self.cur_lang_code]
     else:
         lowerCamelCase__    :      int					 =		[self.cur_lang_code]
         lowerCamelCase__    :      Tuple					 =		[self.eos_token_id]

     lowerCamelCase__    :      Any					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      Optional[Any]					 =		self.convert_ids_to_tokens(self.suffix_tokens	)

     lowerCamelCase__    :      str					 =		processors.TemplateProcessing(
         single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,     self.prefix_tokens + self.suffix_tokens	)	)			,     )
 def      A_	(      self  :      int			,     UpperCAmelCase  :      str	)			->	None:
     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_tokens_to_ids(UpperCAmelCase	)
     if self.legacy_behaviour:
         lowerCamelCase__    :      Dict					 =		[]
         lowerCamelCase__    :      Union[str, Any]					 =		[self.eos_token_id, self.cur_lang_code]
     else:
         lowerCamelCase__    :      Any					 =		[self.cur_lang_code]
         lowerCamelCase__    :      Optional[Any]					 =		[self.eos_token_id]

     lowerCamelCase__    :      Union[str, Any]					 =		self.convert_ids_to_tokens(self.prefix_tokens	)
     lowerCamelCase__    :      List[Any]					 =		self.convert_ids_to_tokens(self.suffix_tokens	)

     lowerCamelCase__    :      Optional[int]					 =		processors.TemplateProcessing(
         single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,     self.prefix_tokens + self.suffix_tokens	)	)			,     )







 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
             'tokenizer.'	)

     if not os.path.isdir(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
         return
     lowerCamelCase__    :      int					 =		os.path.join(
         UpperCAmelCase			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

     if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCAmelCase	):
         copyfile(self.vocab_file			,     UpperCAmelCase	)

     return (out_vocab_file,)





