


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

import os
from pathlib import Path




def       a__						(		snake_case__				:       List[Any]	,      snake_case__				:       Tuple	,      snake_case__				:       Optional[Any]	,      snake_case__				:       int    ):
		_UpperCAmelCase		:       Optional[int]       =    {
		    """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]
		_UpperCAmelCase		:       List[str]       =    {
		    """wmt19-de-en-6-6-base""": [0, 38.37],
		    """wmt19-de-en-6-6-big""": [0, 39.90],
		}
		_UpperCAmelCase		:       List[str]       =    f'''{src_lang}-{tgt_lang}'''

		_UpperCAmelCase		:       Optional[int]       =    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__    )
		_UpperCAmelCase		:       Tuple       =    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
SCREAMING_SNAKE_CASE__				:    Tuple         =    Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    repo_dir / 'model_cards'

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









from itertools import count
from math import asin, pi, sqrt




def       a__						(		snake_case__				:       float    ):
  return (
      (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
  ) / 4




def       a__						(		snake_case__				:       int    ):
  _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
      2 * (circles_number**2 + 1)
  )
  _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y

  _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
  _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
      1 / 2    ) - circle_bottom_arc_integral(snake_case__    )

  return triangle_area + concave_region_area




def       a__						(		snake_case__				:       float = 1 / 1000    ):
  _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4

  for n in count(1    ):
    if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
      return n

  return -1


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





