

# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			List[Any]   ,				lowercase_:			Union[str, Any] )					->				str:
	A__							:   str					       =		{
	    """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]
	A__							:   Union[str, Any]					       =		{
	    """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],
	}
	A__							:   Optional[int]					       =		f"""{src_lang}-{tgt_lang}"""

	A__							:   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=lowercase_   ,				exist_ok=lowercase_ )
	A__							:   str					       =		os.path.join(lowercase_   ,				"""README.md""" )
	print(f"""Generating {path}""" )
	with open(lowercase_   ,				"""w"""   ,				encoding="""utf-8""" ) as f:
		f.write(lowercase_ )


# make sure we are under the root of the project
A_	:			str  	=      Path(__file__).resolve().parent.parent.parent
A_	:			List[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"]:
	A_	:			Optional[int]  	=      model_cards_dir / 'allenai' / model_name
	write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				A__							:   Dict					       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



