





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

import os
from pathlib import Path


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      Tuple					 =		{
        'en': 'Machine learning is great, isn\'t it?',
        'ru': 'Машинное обучение - это здорово, не так ли?',
        'de': 'Maschinelles Lernen ist großartig, oder?',
    }

    # BLUE scores as follows:
    # "pair": [fairseq, transformers]
    lowerCamelCase__    :      Tuple					 =		{
        'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
        'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
        'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
        'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
    }
    lowerCamelCase__    :      str					 =		F"""{src_lang}-{tgt_lang}"""

    lowerCamelCase__    :      Any					 =		F"""
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
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

- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR's WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

"""
    os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		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
_UpperCAmelCase				:       Tuple	   =				Path(__file__).resolve().parent.parent.parent
_UpperCAmelCase				:       List[Any]	   =				repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    _UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase				:       int	   =				model_name.split("""-""")
    _UpperCAmelCase				:       Tuple	   =				model_cards_dir / """facebook""" / model_name
    write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)












from datetime import datetime

import requests





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				bytes:







			'''simple docstring'''

			UpperCAmelCase   		=				'''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
			UpperCAmelCase   		=				requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
			return requests.get(UpperCamelCase__ ).content


if __name__ == "__main__":
				__A				:    Union[str, Any]       =   input("Enter Video/IGTV url: ").strip()
				__A				:    Tuple       =   F'{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'
				with open(file_name, "wb") as fp:
								fp.write(download_video(url))
				print(F'Done. Video saved to disk as {file_name}.')



