





"""simple docstring"""



# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path







def 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    Optional[Any]							,snake_case_    :    Union[str, Any]							,snake_case_    :    Dict		)  ->Tuple:



    '''simple docstring'''







    __A       :		List[str]							=	{
        '''en''': '''Machine learning is great, isn\'t it?''',
        '''ru''': '''Машинное обучение - это здорово, не так ли?''',
        '''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
    }

    # BLUE scores as follows:
    # "pair": [fairseq, transformers]
    __A       :		Optional[Any]							=	{
        '''wmt16-en-de-dist-12-1''': [28.3, 27.52],
        '''wmt16-en-de-dist-6-1''': [27.4, 27.11],
        '''wmt16-en-de-12-1''': [26.9, 25.75],
    }
    __A       :		int							=	F"""{src_lang}-{tgt_lang}"""

    __A       :		Any							=	F"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
    model_card_dir.mkdir(parents=snake_case_							,exist_ok=snake_case_		)
    __A       :		Union[str, Any]							=	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
a_					=     Path(__file__).resolve().parent.parent.parent
a_					=     repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
       a_					=     model_cards_dir / """allenai""" / model_name
       write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)











"""simple docstring"""



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->str:



    '''simple docstring'''







    __A       :		Tuple							=	tmp_path / '''file.csv'''
    __A       :		Union[str, Any]							=	textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    str		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		List[str]							=	tmp_path / '''malformed_file.csv'''
    __A       :		Optional[Any]							=	textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20,
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    Optional[int]		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		List[str]							=	tmp_path / '''csv_with_image.csv'''
    __A       :		List[str]							=	textwrap.dedent(
        F"""\
        image
        {image_file}
        """		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Tuple:



    '''simple docstring'''







    __A       :		Dict							=	tmp_path / '''csv_with_label.csv'''
    __A       :		int							=	textwrap.dedent(
        '''\
        label
        good
        bad
        good
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Dict		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		Optional[int]							=	tmp_path / '''csv_with_int_list.csv'''
    __A       :		Union[str, Any]							=	textwrap.dedent(
        '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    Optional[int]							,snake_case_    :    Optional[Any]		)  ->Dict:



    '''simple docstring'''







    __A       :		str							=	Csv()
    __A       :		List[Any]							=	csv._generate_tables([[csv_file, malformed_csv_file]]		)
    with pytest.raises(snake_case_							,match='''Error tokenizing data'''		):
        for _ in generator:
            pass
    assert any(
        record.levelname == '''ERROR'''
        and '''Failed to read file''' in record.message
        and os.path.basename(snake_case_		) in record.message
        for record in caplog.records		)







@require_pil
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Union[str, Any]:



    '''simple docstring'''







    with open(snake_case_							,encoding='''utf-8'''		) as f:
        __A       :		int							=	f.read().splitlines()[1]
    __A       :		Optional[Any]							=	Csv(encoding='''utf-8'''							,features=Features({'''image''': Image()}		)		)
    __A       :		Union[str, Any]							=	csv._generate_tables([[csv_file_with_image]]		)
    __A       :		Optional[Any]							=	pa.concat_tables([table for _, table in generator]		)
    assert pa_table.schema.field('''image'''		).type == Image()()
    __A       :		Optional[int]							=	pa_table.to_pydict()['''image''']
    assert generated_content == [{"path": image_file, "bytes": None}]







def 						__lowercase	(  snake_case_    :    Any		)  ->List[Any]:



    '''simple docstring'''







    with open(snake_case_							,encoding='''utf-8'''		) as f:
        __A       :		Optional[int]							=	f.read().splitlines()[1:]
    __A       :		List[Any]							=	Csv(encoding='''utf-8'''							,features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']		)}		)		)
    __A       :		str							=	csv._generate_tables([[csv_file_with_label]]		)
    __A       :		Any							=	pa.concat_tables([table for _, table in generator]		)
    assert pa_table.schema.field('''label'''		).type == ClassLabel(names=['''good''', '''bad''']		)()
    __A       :		Optional[Any]							=	pa_table.to_pydict()['''label''']
    assert generated_content == [ClassLabel(names=['''good''', '''bad''']		).straint(snake_case_		) for label in labels]







def 						__lowercase	(  snake_case_    :    str		)  ->Dict:



    '''simple docstring'''







    __A       :		Optional[int]							=	Csv(encoding='''utf-8'''							,sep=''','''							,converters={'''int_list''': lambda snake_case_		: [int(snake_case_		) for i in x.split()]}		)
    __A       :		Dict							=	csv._generate_tables([[csv_file_with_int_list]]		)
    __A       :		Dict							=	pa.concat_tables([table for _, table in generator]		)
    assert pa.types.is_list(pa_table.schema.field('''int_list'''		).type		)
    __A       :		int							=	pa_table.to_pydict()['''int_list''']
    assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]




