




"""simple docstring"""







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

import os
from pathlib import Path





def 							lowercase   (snake_case__				:	str ,    snake_case__				:	Any ,    snake_case__				:	str ,    snake_case__				:	Optional[Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			{
				    """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]
				lowerCAmelCase		=			{
				    """wmt19-de-en-6-6-base""": [0, 38.37],
				    """wmt19-de-en-6-6-big""": [0, 39.90],
				}
				lowerCAmelCase		=			f'''{src_lang}-{tgt_lang}'''

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


# make sure we are under the root of the project
a								=							Path(__file__).resolve().parent.parent.parent
a								=							repo_dir / 'model_cards'

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











"""simple docstring"""







def 							lowercase   (snake_case__				:	int	)       ->					int:


				'''simple docstring'''






				barcode //= 10  # exclude the last digit
				lowerCAmelCase		=			False
				lowerCAmelCase		=			0

				# extract and check each digit
				while barcode != 0:
								lowerCAmelCase		=			1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								lowerCAmelCase		=			not checker

				return (10 - (s % 10)) % 10





def 							lowercase   (snake_case__				:	int	)       ->					bool:


				'''simple docstring'''






				return len(str(snake_case__	)	) == 13 and get_check_digit(snake_case__	) == barcode % 10





def 							lowercase   (snake_case__				:	str	)       ->					int:


				'''simple docstring'''






				if str(snake_case__	).isalpha():
								lowerCAmelCase		=			f'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(snake_case__	)
				elif int(snake_case__	) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""	)
				else:
								return int(snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a								=							get_barcode(input('Barcode: ').strip())

		if is_valid(barcode):
				print(f"""'{barcode}' is a valid barcode.""")
		else:
				print(f"""'{barcode}' is NOT a valid barcode.""")





