



'''simple docstring'''

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

import os
from pathlib import Path



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Optional[Any]:
		_a  :   Dict				 =							{
		    '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  :   List[str]				 =							{
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		_a  :   List[str]				 =							f"""{src_lang}-{tgt_lang}"""

		_a  :   Tuple				 =							f"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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 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 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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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=lowerCAmelCase_		,    exist_ok=lowerCAmelCase_						)
		_a  :   List[Any]				 =							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
__lowerCAmelCase		    =     Path(__file__).resolve().parent.parent.parent
__lowerCAmelCase		    =     repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
							__lowerCAmelCase		    =     model_cards_dir / '''allenai''' / model_name
							write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)









'''simple docstring'''

import math



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   list:
		if n <= 0:
				raise ValueError('Only positive integers have prime factors'						)
		_a  :   Tuple				 =							[]
		while n % 2 == 0:
				pf.append(2						)
				_a  :   str				 =							int(n / 2						)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				while n % i == 0:
						pf.append(lowerCAmelCase_						)
						_a  :   List[Any]				 =							int(n / i						)
		if n > 2:
				pf.append(lowerCAmelCase_						)
		return pf



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   int				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   str				 =							int(n / 2						)
		div *= temp
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   List[str]				 =							1
				while n % i == 0:
						temp += 1
						_a  :   List[str]				 =							int(n / i						)
				div *= temp
		if n > 1:
				div *= 2
		return div



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   List[str]				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   Tuple				 =							int(n / 2						)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   str				 =							1
				while n % i == 0:
						temp += 1
						_a  :   Any				 =							int(n / i						)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							n
		for x in set(prime_factors(lowerCAmelCase_						)						):
				s *= (x - 1) / x
		return int(lowerCAmelCase_						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




