



"""simple docstring"""






import os
from pathlib import Path






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Tuple						      ={
			    '''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__						:						int						      ={
			    '''wmt19-de-en-6-6-base''': [0, 38.37],
			    '''wmt19-de-en-6-6-big''': [0, 39.90],
			}
			lowerCamelCase__						:						Tuple						      =f'''{src_lang}-{tgt_lang}'''

			lowerCamelCase__						:						Any						      =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=_UpperCamelCase				,						exist_ok=_UpperCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =os.path.join(_UpperCamelCase				,						'''README.md'''      )
			print(f'''Generating {path}'''      )
			with open(_UpperCamelCase				,						'''w'''				,						encoding='''utf-8'''      ) as f:
						f.write(_UpperCamelCase      )


# make sure we are under the root of the project
_lowercase					:					Dict 		=		Path(__file__).resolve().parent.parent.parent
_lowercase					:					int 		=		repo_dir / "model_cards"

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








"""simple docstring"""







from __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()