



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

import os
from pathlib import Path

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :List[str]  ,      _snake_case  :int  ,      _snake_case  :str							)     ->    Optional[int]:
   _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=_snake_case  ,      exist_ok=_snake_case							)
   _A						     =					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
UpperCAmelCase_		   =				Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_		   =				repo_dir / """model_cards"""

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








import math
from decimal import Decimal, getcontext

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int = 4_000_000							)     ->    int:

   try:
      _A						     =					int(_snake_case							)
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.'''							)
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.'''							)
   _A						     =					100
   _A						     =					(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

   _A						     =					(math.floor(math.log(n * (phi + 2)  ,      _snake_case							) - 1							) // 3) * 3 + 2
   _A						     =					Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
   _A						     =					num // 2
   return int(_snake_case							)


if __name__ == "__main__":
   print(f'{solution() = }')



