



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

import os
from pathlib import Path





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :List[Any]   ,			a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			{
							    """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]
							UpperCamelCase__				     =			{
							    """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"""],
							}
							UpperCamelCase__				     =			f"""{src_lang}-{tgt_lang}"""

							UpperCamelCase__				     =			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(a__   ,			exist_ok=a__       )
							UpperCamelCase__				     =			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
UpperCamelCase__											= Path(__file__).resolve().parent.parent.parent
UpperCamelCase__											= repo_dir / "model_cards"

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











UpperCamelCase__											= "ABCDEFGHIJKLMNOPQRSTUVWXYZ"





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			input("""Enter message: """       )
							UpperCamelCase__				     =			input("""Enter key [alphanumeric]: """       )
							UpperCamelCase__				     =			input("""Encrypt/Decrypt [e/d]: """       )

							if mode.lower().startswith("""e"""       ):
														UpperCamelCase__				     =			"""encrypt"""
														UpperCamelCase__				     =			encrypt_message(a__   ,			a__       )
							elif mode.lower().startswith("""d"""       ):
														UpperCamelCase__				     =			"""decrypt"""
														UpperCamelCase__				     =			decrypt_message(a__   ,			a__       )

							print(f"""\n{mode.title()}ed message:"""       )
							print(a__       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""encrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""decrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			key.upper()

							for symbol in message:
														UpperCamelCase__				     =			LETTERS.find(symbol.upper()       )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]       )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]       )

																					num %= len(a__       )

																					if symbol.isupper():
																												translated.append(LETTERS[num]       )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()       )

																					key_index += 1
																					if key_index == len(a__       ):
																												UpperCamelCase__				     =			0
														else:
																					translated.append(a__       )
							return "".join(a__       )


if __name__ == "__main__":
					main()




