




'''simple docstring'''



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

import os
from pathlib import Path
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple	,					__A    :			List[Any]	,					__A    :			List[Any]  )			->			int:
   _SCREAMING_SNAKE_CASE	=				{
       "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	=				{
       "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	=				f"""{src_lang}-{tgt_lang}"""

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


# make sure we are under the root of the project
lowerCamelCase_			=						Path(__file__).resolve().parent.parent.parent
lowerCamelCase_			=						repo_dir / 'model_cards'

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







'''simple docstring'''



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase_			=						logging.get_logger(__name__)


lowerCamelCase_			=						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Tuple	,					__A    :			List[str]	,					__A    :			List[str]  )			->			List[Any]:
   if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
      raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()  )}."""  )

   if tokenizer_name is None:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES
   else:
      _SCREAMING_SNAKE_CASE	=				{tokenizer_name: getattr(__A	,					tokenizer_name + "Fast"  )}

   logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""  )

   for tokenizer_name in tokenizer_names:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES[tokenizer_name]

      _SCREAMING_SNAKE_CASE	=				True
      if checkpoint_name is None:
         _SCREAMING_SNAKE_CASE	=				list(tokenizer_class.max_model_input_sizes.keys()  )
      else:
         _SCREAMING_SNAKE_CASE	=				[checkpoint_name]

      logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""  )

      for checkpoint in checkpoint_names:
         logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""  )

         # Load tokenizer
         _SCREAMING_SNAKE_CASE	=				tokenizer_class.from_pretrained(__A	,					force_download=__A  )

         # Save fast tokenizer
         logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""  )

         # For organization names we create sub-directories
         if "/" in checkpoint:
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				checkpoint.split("/"  )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
         elif add_prefix:
            _SCREAMING_SNAKE_CASE	=				checkpoint
            _SCREAMING_SNAKE_CASE	=				dump_path
         else:
            _SCREAMING_SNAKE_CASE	=				None
            _SCREAMING_SNAKE_CASE	=				dump_path

         logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()  )[0]:
            _SCREAMING_SNAKE_CASE	=				list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
            _SCREAMING_SNAKE_CASE	=				file_path.split(__A  )[-1][0]
            if next_char == "/":
               _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
               _SCREAMING_SNAKE_CASE	=				None

            logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         _SCREAMING_SNAKE_CASE	=				tokenizer.save_pretrained(
             __A	,					legacy_format=__A	,					filename_prefix=__A  )
         logger.info(f"""=> File names {file_names}"""  )

         for file_name in file_names:
            if not file_name.endswith("tokenizer.json"  ):
               os.remove(__A  )
               logger.info(f"""=> removing {file_name}"""  )


if __name__ == "__main__":
 lowerCamelCase_			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
 )
 parser.add_argument(
     '--tokenizer_name',
     default=None,
     type=str,
     help=(
         f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         'download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--checkpoint_name',
     default=None,
     type=str,
     help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
 )
 parser.add_argument(
     '--force_download',
     action='store_true',
     help='Re-download checkpoints.',
 )
 lowerCamelCase_			=						parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

