


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def    a   (							A__      ,    A__      ,    A__      ,    A__							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Any						     =		{
          '''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]
      SCREAMING_SNAKE_CASE__       :  str						     =		{
          '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
          '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
          '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
      }
      SCREAMING_SNAKE_CASE__       :  int						     =		f"""{src_lang}-{tgt_lang}"""

      SCREAMING_SNAKE_CASE__       :  str						     =		f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=A__      ,    exist_ok=A__							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		os.path.join(A__      ,    '''README.md'''							)
      print(f"""Generating {path}"""							)
      with open(A__      ,    '''w'''      ,    encoding='''utf-8'''							) as f:
            f.write(A__							)


# make sure we are under the root of the project
a_ :Optional[Any]  							=			Path(__file__).resolve().parent.parent.parent
a_ :Union[str, Any]  							=			repo_dir / 'model_cards'

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
    a_ :Union[str, Any]  							=			model_cards_dir / 'allenai' / model_name
    write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)












from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

_lowerCamelCase      :						Dict							=  6_378_137.0
_lowerCamelCase      :						Union[str, Any]							=  6_356_752.314_245
_lowerCamelCase      :						List[Any]							=  6378137







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       (AXIS_A - AXIS_B) / AXIS_A
       # Parametric latitudes
       # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)

       # Compute central angle between two points
       # using haversine theta. sigma =  haversine_distance / equatorial radius
       A__ 					=       haversine_distance(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		) / EQUATORIAL_RADIUS

       # Intermediate P and Q values
       A__ 					=       (b_lata + b_lata) / 2
       A__ 					=       (b_lata - b_lata) / 2

       # Intermediate X value
       # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
       A__ 					=       (sin(lowercase_		) ** 2) * (cos(lowercase_		) ** 2)
       A__ 					=       cos(sigma / 2		) ** 2
       A__ 					=       (sigma - sin(lowercase_		)) * (x_numerator / x_demonimator)

       # Intermediate Y value
       # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
       A__ 					=       (cos(lowercase_		) ** 2) * (sin(lowercase_		) ** 2)
       A__ 					=       sin(sigma / 2		) ** 2
       A__ 					=       (sigma + sin(lowercase_		)) * (y_numerator / y_denominator)

       return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
  import doctest

  doctest.testmod()


