





'''simple docstring'''





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 json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE__      					=     "http://www.mocksite.com/file1.txt"
SCREAMING_SNAKE_CASE__      					=     "\"text\": [\"foo\", \"foo\"]"
SCREAMING_SNAKE_CASE__      					=     "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"


class 				snake_case :
       lowerCAmelCase__  :Union[str, Any]						   =					200
       lowerCAmelCase__  :Tuple						   =					{"Content-Length": "100"}
       lowerCAmelCase__  :Any						   =					{}



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							List[Any]:
           return [bytes(UpperCAmelCase_     ,"utf-8"      )]





def        lowerCamelCase  (				*_snake_case  :						int     ,**_snake_case  :						int       ):





    '''simple docstring'''


    return MockResponse()




@pytest.mark.parametrize("urls_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						List[Any]     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    import requests

    monkeypatch.setattr(_snake_case     ,"request"     ,_snake_case       )

    lowercase__		       =						URL
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						url
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[url]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": url}
    lowercase__		       =						"dummy"
    lowercase__		       =						"downloads"
    lowercase__		       =						tmp_path
    lowercase__		       =						DownloadConfig(
        cache_dir=os.path.join(_snake_case     ,_snake_case       )     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.download(_snake_case       )
    lowercase__		       =						urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[downloaded_paths]
            lowercase__		       =						[urls]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in downloaded_paths.keys()
            lowercase__		       =						downloaded_paths.values()
            lowercase__		       =						urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_snake_case     ,_snake_case       ):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            lowercase__		       =						downloaded_path.read_text()
            assert content == CONTENT
            lowercase__		       =						downloaded_path.with_suffix(".json"       )
            assert metadata_downloaded_path.exists()
            lowercase__		       =						json.loads(metadata_downloaded_path.read_text()       )
            assert metadata_content == {"url": URL, "etag": None}




@pytest.mark.parametrize("paths_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						str(_snake_case       )
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						filename
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[filename]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": filename}
    lowercase__		       =						"dummy"
    lowercase__		       =						xz_file.parent
    lowercase__		       =						"extracted"
    lowercase__		       =						DownloadConfig(
        cache_dir=_snake_case     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.extract(_snake_case       )
    lowercase__		       =						paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[extracted_paths]
            lowercase__		       =						[paths]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in extracted_paths.keys()
            lowercase__		       =						extracted_paths.values()
            lowercase__		       =						paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_snake_case     ,_snake_case       ):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_snake_case     ,etag=_snake_case       )
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            lowercase__		       =						extracted_path.read_text()
            lowercase__		       =						text_file.read_text()
            assert extracted_file_content == expected_file_content




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    assert path.endswith(".jsonl"       )
    for num_items, line in enumerate(_snake_case     ,start=1       ):
        lowercase__		       =						json.loads(line.decode("utf-8"       )       )
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4




@pytest.mark.parametrize("archive_jsonl"     ,["tar_jsonl_path", "zip_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        _test_jsonl(_snake_case     ,_snake_case       )
    assert num_jsonl == 2




@pytest.mark.parametrize("archive_nested_jsonl"     ,["tar_nested_jsonl_path", "zip_nested_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
            _test_jsonl(_snake_case     ,_snake_case       )
    assert num_tar == 1
    assert num_jsonl == 2




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_snake_case       )     ,start=1       ):
        assert os.path.basename(_snake_case       ) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2




