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

import os
from pathlib import Path


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		{
      '''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]
  __SCREAMING_SNAKE_CASE      :	Any					=		{
      '''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'''],
  }
  __SCREAMING_SNAKE_CASE      :	Dict					=		F'''{src_lang}-{tgt_lang}'''

  __SCREAMING_SNAKE_CASE      :	Tuple					=		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(snake_case       ,			exist_ok=snake_case							)
  __SCREAMING_SNAKE_CASE      :	List[str]					=		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
lowercase_ 				=			Path(__file__).resolve().parent.parent.parent
lowercase_ 				=			repo_dir / """model_cards"""

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




'''simple docstring'''



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A__						:   Any				=    re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")





@total_ordering
@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str
    lowerCamelCase    :   Optional[str]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						_str_to_version_tuple(self.version_str      )







    def __repr__(				self      )							->	Any:
         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'







    @property
    def  lowercase_							(				self      )							->	int:
         return self.major, self.minor, self.patch







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return Version(SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return other
         raise TypeError(f'{other} (type {type(SCREAMING_SNAKE_CASE_      )}) cannot be compared to version.'      )







    def __eq__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         try:
              __lowerCamelCase  :      Union[str, Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         except (TypeError, ValueError):
              return False
         else:
              return self.tuple == other.tuple







    def __lt__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      List[Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         return self.tuple < other.tuple







    def __hash__(				self      )							->	List[str]:
         return hash(_version_tuple_to_str(self.tuple      )      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      str														=						{f.name for f in dataclasses.fields(cls      )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}      )







    def  lowercase_							(				self      )							->	str:
         return self.version_str







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						str:
     __lowerCamelCase  :      str														=						_VERSION_REG.match(UpperCAmelCase_				)
     if not res:
          raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'				)
     return tuple(int(UpperCAmelCase_				) for v in [res.group('major'				), res.group('minor'				), res.group('patch'				)]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str]				)	->						Dict:
     return ".".join(str(UpperCAmelCase_				) for v in version_tuple				)






