



"""simple docstring"""
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:
  _lowerCAmelCase       :Union[str, Any]	=   None


_lowerCAmelCase       :List[Any]	=   logging.get_logger(__name__)


_lowerCAmelCase       :str	=   {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase       :Optional[int]	=   {
    """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"""
        ),
    },
}

_lowerCAmelCase       :List[Any]	=   {
    """facebook/nllb-large-en-ro""": 1_024,
    """facebook/nllb-200-distilled-600M""": 1_024,
}

# fmt: off
_lowerCAmelCase       :List[str]	=   ["""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 				_UpperCAmelCase				(					a ):





     '''simple docstring'''



     a__   							=VOCAB_FILES_NAMES
     a__   							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__   							=PRETRAINED_VOCAB_FILES_MAP
     a__   							=['''input_ids''', '''attention_mask''']
     a__   							=NllbTokenizer

     a__   							=[]
     a__   							=[]





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

         _UpperCAmelCase				:    List[str]											=       vocab_file
         _UpperCAmelCase				:    List[Any]											=       False if not self.vocab_file else True

         _UpperCAmelCase				:    List[str]											=       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}						)
         _UpperCAmelCase				:    List[Any]											=       {
             lang_code: self.convert_tokens_to_ids(a_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         _UpperCAmelCase				:    List[str]											=       src_lang if src_lang is not None else "eng_Latn"
         _UpperCAmelCase				:    int											=       self.convert_tokens_to_ids(self._src_lang						)
         _UpperCAmelCase				:    Any											=       tgt_lang
         self.set_src_lang_special_tokens(self._src_lang						)





     @property
     def 					__lowerCAmelCase      (   self						)				->   str:
         return self._src_lang





     @src_lang.setter
     def 					__lowerCAmelCase      (   self			,      A						)				->   None:
         _UpperCAmelCase				:    str											=       new_src_lang
         self.set_src_lang_special_tokens(self._src_lang						)





     def 					__lowerCAmelCase      (   self			,      A			,      A = 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 					__lowerCAmelCase      (   self			,      A			,      A = None						)				->   List[int]:
         _UpperCAmelCase				:    int											=       [self.sep_token_id]
         _UpperCAmelCase				:    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 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A			,      **A						)				->   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'''						)
         _UpperCAmelCase				:    Optional[Any]											=       src_lang
         _UpperCAmelCase				:    List[str]											=       self(a_			,      add_special_tokens=a_			,      return_tensors=a_			,      **a_						)
         _UpperCAmelCase				:    List[Any]											=       self.convert_tokens_to_ids(a_						)
         _UpperCAmelCase				:    Optional[Any]											=       tgt_lang_id
         return inputs





     def 					__lowerCAmelCase      (   self			,      A			,      A = "eng_Latn"			,      A = None			,      A = "fra_Latn"			,      **A			,      )				->   BatchEncoding:
         _UpperCAmelCase				:    Union[str, Any]											=       src_lang
         _UpperCAmelCase				:    Dict											=       tgt_lang
         return super().prepare_seqaseq_batch(a_			,      a_			,      **a_						)





     def 					__lowerCAmelCase      (   self						)				->   str:
         return self.set_src_lang_special_tokens(self.src_lang						)





     def 					__lowerCAmelCase      (   self						)				->   List[str]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang						)





     def 					__lowerCAmelCase      (   self			,      A						)				->   None:
         _UpperCAmelCase				:    Dict											=       self.convert_tokens_to_ids(a_						)

         if self.legacy_behaviour:
             _UpperCAmelCase				:    List[Any]											=       []
             _UpperCAmelCase				:    Optional[Any]											=       [self.eos_token_id, self.cur_lang_code]
         else:
             _UpperCAmelCase				:    List[str]											=       [self.cur_lang_code]
             _UpperCAmelCase				:    List[str]											=       [self.eos_token_id]

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

         _UpperCAmelCase				:    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 					__lowerCAmelCase      (   self			,      A						)				->   None:
         _UpperCAmelCase				:    Optional[int]											=       self.convert_tokens_to_ids(a_						)
         if self.legacy_behaviour:
             _UpperCAmelCase				:    Optional[int]											=       []
             _UpperCAmelCase				:    Optional[int]											=       [self.eos_token_id, self.cur_lang_code]
         else:
             _UpperCAmelCase				:    str											=       [self.cur_lang_code]
             _UpperCAmelCase				:    Dict											=       [self.eos_token_id]

         _UpperCAmelCase				:    Dict											=       self.convert_ids_to_tokens(self.prefix_tokens						)
         _UpperCAmelCase				:    Any											=       self.convert_ids_to_tokens(self.suffix_tokens						)

         _UpperCAmelCase				:    Union[str, Any]											=       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 					__lowerCAmelCase      (   self			,      A			,      A = 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(a_						):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory.'						)
             return
         _UpperCAmelCase				:    List[Any]											=       os.path.join(
             a_			,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

         return (out_vocab_file,)






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "en": "Machine learning is great, isn't it?",
	    "ru": "Машинное обучение - это здорово, не так ли?",
	    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	lowercase						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

'''
	model_card_dir.mkdir(parents=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


