



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

import os
from pathlib import Path






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  {
				    '''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__  :       List[str]				=  {
				    '''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__  :       Optional[int]				=  f"""{src_lang}-{tgt_lang}"""

				lowerCAmelCase__  :       List[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(lowerCamelCase_ ,exist_ok=lowerCamelCase_)
				lowerCAmelCase__  :       List[str]				=  os.path.join(lowerCamelCase_ ,'''README.md''')
				print(f"""Generating {path}""")
				with open(lowerCamelCase_ ,'''w''' ,encoding='''utf-8''') as f:
								f.write(lowerCamelCase_)


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

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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	Any            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""encoder-decoder"""
		snake_case_			=True



		def __init__(self	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						lowerCAmelCase__  :       Optional[Any]				=  kwargs.pop('''encoder'''				)
						lowerCAmelCase__  :       Any				=  encoder_config.pop('''model_type'''				)
						lowerCAmelCase__  :       str				=  kwargs.pop('''decoder'''				)
						lowerCAmelCase__  :       Tuple				=  decoder_config.pop('''model_type'''				)

						from ..auto.configuration_auto import AutoConfig

						lowerCAmelCase__  :       Tuple				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  True



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase				)  ->    PretrainedConfig:

						"""simple docstring"""






						logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
						lowerCAmelCase__  :       Optional[int]				=  True
						lowerCAmelCase__  :       List[str]				=  True

						return cls(encoder=encoder_config.to_dict()	,decoder=decoder_config.to_dict()	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  copy.deepcopy(self.__dict__				)
						lowerCAmelCase__  :       Optional[Any]				=  self.encoder.to_dict()
						lowerCAmelCase__  :       str				=  self.decoder.to_dict()
						lowerCAmelCase__  :       Optional[int]				=  self.__class__.model_type
						return output

