



'''simple docstring'''





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

import os
from pathlib import Path




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						List[Any]     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						{
        "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]
    lowercase__		       =						{
        "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"],
    }
    lowercase__		       =						f'''{src_lang}-{tgt_lang}'''

    lowercase__		       =						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       )
    lowercase__		       =						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
SCREAMING_SNAKE_CASE__      					=     Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__      					=     repo_dir / "model_cards"

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









'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           lowercase__		       =						params

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array([len(UpperCAmelCase_      ) for t in data]      )

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()



       def __getitem__(				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           return (self.token_ids[index], self.lengths[index])



       def __len__(				self      )  ->							Union[str, Any]:
           return len(self.lengths      )



       def 							_a      (				self      )  ->							int:
           assert len(self.token_ids      ) == len(self.lengths      )
           assert all(self.lengths[i] == len(self.token_ids[i]      ) for i in range(len(self.lengths      )      )      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						self.params.max_model_input_size
           lowercase__		       =						self.lengths > max_len
           logger.info(F'''Splitting {sum(UpperCAmelCase_      )} too long sequences.'''      )

           def divide_chunks(UpperCAmelCase_     ,UpperCAmelCase_      ):
               return [l[i : i + n] for i in range(0     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )]

           lowercase__		       =						[]
           lowercase__		       =						[]
           if self.params.mlm:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
           else:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

           for seq_, len_ in zip(self.token_ids     ,self.lengths      ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                   new_tok_ids.append(seq_      )
                   new_lengths.append(len_      )
               else:
                   lowercase__		       =						[]
                   for sub_s in divide_chunks(seq_     ,max_len - 2      ):
                       if sub_s[0] != cls_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,0     ,UpperCAmelCase_      )
                       if sub_s[-1] != sep_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )
                       assert len(UpperCAmelCase_      ) <= max_len
                       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                       sub_seqs.append(UpperCAmelCase_      )

                   new_tok_ids.extend(UpperCAmelCase_      )
                   new_lengths.extend([len(UpperCAmelCase_      ) for l in sub_seqs]      )

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array(UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						len(self      )
           lowercase__		       =						self.lengths > 11
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''      )



       def 							_a      (				self      )  ->							List[Any]:
           if "unk_token" not in self.params.special_tok_ids:
               return
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						len(self      )
           lowercase__		       =						np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
           lowercase__		       =						(unk_occs / self.lengths) < 0.5
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''      )



       def 							_a      (				self      )  ->							Optional[int]:
           if not self.params.is_master:
               return
           logger.info(F'''{len(self      )} sequences'''      )
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[t[0] for t in batch]
           lowercase__		       =						[t[1] for t in batch]
           assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

           # Max for paddings
           lowercase__		       =						max(UpperCAmelCase_      )

           # Pad token ids
           if self.params.mlm:
               lowercase__		       =						self.params.special_tok_ids["pad_token"]
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						[list(t.astype(UpperCAmelCase_      )      ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase_      )) for t in token_ids]
           assert len(tk_      ) == len(UpperCAmelCase_      )
           assert all(len(UpperCAmelCase_      ) == max_seq_len_ for t in tk_      )

           lowercase__		       =						torch.tensor(tk_      )  # (bs, max_seq_len_)
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )  # (bs)
           return tk_t, lg_t




