

"""simple docstring"""




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

import os
from pathlib import Path
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					{
					    "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			=					{
					    "wmt19-de-en-6-6-base": [0, 38.37],
					    "wmt19-de-en-6-6-big": [0, 39.90],
					}
					A			=					F"""{src_lang}-{tgt_lang}"""

					A			=					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=lowercase__			,							exist_ok=lowercase__      )
					A			=					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     :    List[Any]    =		Path(__file__).resolve().parent.parent.parent
__A     :    List[str]    =		repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
	__A     :    Dict    =		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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					index
					A			=					2 * index + 1
					A			=					2 * index + 2
					if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
										A			=					left_index

					if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
										A			=					right_index

					if largest != index:
										A			,    A			=					unsorted[index], unsorted[largest]
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					len(lowercase__      )
					for i in range(n // 2 - 1			,							-1			,							-1      ):
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
					for i in range(n - 1			,							0			,							-1      ):
										A			,    A			=					unsorted[i], unsorted[0]
										heapify(lowercase__			,							0			,							lowercase__      )
					return unsorted


if __name__ == "__main__":
	__A     :    List[Any]    =		input('Enter numbers separated by a comma:\n').strip()
	__A     :    List[str]    =		[int(item) for item in user_input.split(',')]
	print(heap_sort(unsorted))





