





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       List[str]=5						):





  """simple docstring"""



  assert masked_input.count("<mask>"						) == 1
  a__     : Dict  =torch.tensor(tokenizer.encode(snake_case_	,	add_special_tokens=snake_case_						)						).unsqueeze(0						)  # Batch size 1
  a__     : Dict  =model(snake_case_						)[0]  # The last hidden-state is the first element of the output tuple
  a__     : Any  =(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a__     : int  =logits[0, masked_index, :]
  a__     : Any  =logits.softmax(dim=0						)
  a__ ,							a__     : Optional[Any]  =prob.topk(k=snake_case_	,	dim=0						)
  a__     : str  =" ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(snake_case_						)						)]						)
  a__     : Union[str, Any]  =tokenizer.mask_token
  a__     : List[Any]  =[]
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "						)						):
    a__     : Any  =predicted_token_bpe.replace("\u2581"	,	" "						)
    if " {0}".format(snake_case_						) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(snake_case_						)	,	snake_case_						),
              values[index].item(),
              predicted_token,
          )						)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(snake_case_	,	snake_case_						),
              values[index].item(),
              predicted_token,
          )						)
  return topk_filled_outputs


UpperCAmelCase     :     Any							=	CamembertTokenizer.from_pretrained("""camembert-base""")
UpperCAmelCase     :     Optional[Any]							=	CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

UpperCAmelCase     :     int							=	'Le camembert est <mask> :)'





print(fill_mask(masked_input, model, tokenizer, topk=3))









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

import os
from pathlib import Path
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  a__     : 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]
  a__     : List[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"],
  }
  a__     : Optional[int]  =f'''{src_lang}-{tgt_lang}'''

  a__     : 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(SCREAMING_SNAKE_CASE	,	exist_ok=SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =os.path.join(SCREAMING_SNAKE_CASE	,	"README.md"						)
  print(f'''Generating {path}'''						)
  with open(SCREAMING_SNAKE_CASE	,	"w"	,	encoding="utf-8"						) as f:
    f.write(SCREAMING_SNAKE_CASE						)


# make sure we are under the root of the project
UpperCAmelCase     :     str							=	Path(__file__).resolve().parent.parent.parent
UpperCAmelCase     :     Dict							=	repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase     :     List[str]							=	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)






