



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

import os
from pathlib import Path






def      __lowercase       (			a__							,       a__							,       a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= {
							    '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]
							__SCREAMING_SNAKE_CASE									= {
							    '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'],
							}
							__SCREAMING_SNAKE_CASE									= f"""{src_lang}-{tgt_lang}"""

							__SCREAMING_SNAKE_CASE									= 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__       )
							__SCREAMING_SNAKE_CASE									= 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__		:					Dict							=Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__		:					Optional[Any]							=repo_dir / '''model_cards'''

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











import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def      __lowercase       (			a__       )    ->			List[Any]:  # picklable for multiprocessing
							return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      __lowercase       (			)    ->			Any:
							with parallel_backend('spark'       ):
														assert ParallelBackendConfig.backend_name == "spark"

							__SCREAMING_SNAKE_CASE									= [1, 2, 3]
							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=2       )

							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=-1       )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'							,       [2, -1]       )
def      __lowercase       (			a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= [1, 2]
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': [1, 2], 'b': [3, 4]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 1}, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2, 'c': 3, 'd': 4}
							__SCREAMING_SNAKE_CASE									= [2, 3]
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': [2, 3], 'b': [4, 5]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 2}, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3, 'c': 4, 'd': 5}

							with parallel_backend('spark'       ):
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa






