





"""simple docstring"""





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

import os
from pathlib import Path


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						List[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]
  __UpperCAmelCase    :						Optional[int]       =						{
      '''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'''],
  }
  __UpperCAmelCase    :						str       =						f'''{src_lang}-{tgt_lang}'''

  __UpperCAmelCase    :						List[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_  )
  __UpperCAmelCase    :						List[str]       =						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
lowercase__				:List[str]				   =       Path(__file__).resolve().parent.parent.parent
lowercase__				:Union[str, Any]				   =       repo_dir / 'model_cards'

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






"""simple docstring"""





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase__				:str				   =       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__				:list[int]				   =       [ord(letter) for letter in string.ascii_lowercase]
lowercase__				:set[int]				   =       {ord(char) for char in VALID_CHARS}

lowercase__				:list[str]				   =       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str | None:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						""
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int

  for keychar, cipherchar in zip(cycle(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(UpperCAmelCase_  )

  return decoded


def       lowerCamelCase_       (   UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						list[str]       =						[]
  for key in product(UpperCAmelCase_  ,			repeat=3  ):
    __UpperCAmelCase    :						List[Any]       =						try_key(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if encoded is not None:
      possibles.append(UpperCAmelCase_  )
  return possibles


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  return [possible for possible in possibles if common_word in possible.lower()]


def       lowerCamelCase_       (   UpperCAmelCase_ = "p059_cipher.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						list[int]
  __UpperCAmelCase    :						list[str]
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str       =						Path(UpperCAmelCase_  ).parent.joinpath(UpperCAmelCase_  ).read_text(encoding='''utf-8'''  )

  __UpperCAmelCase    :						List[str]       =						[int(UpperCAmelCase_  ) for number in data.strip().split(''','''  )]

  __UpperCAmelCase    :						List[Any]       =						filter_valid_chars(UpperCAmelCase_  )
  for common_word in COMMON_WORDS:
    __UpperCAmelCase    :						Optional[int]       =						filter_common_word(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if len(UpperCAmelCase_  ) == 1:
      break

  __UpperCAmelCase    :						Optional[Any]       =						possibles[0]
  return sum(ord(UpperCAmelCase_  ) for char in decoded_text  )


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