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

import os
from pathlib import Path
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							{
						    '''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							:    Dict							=							{
						    '''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							:    Tuple							=							f"""{src_lang}-{tgt_lang}"""

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


# make sure we are under the root of the project
__UpperCamelCase		:       List[str]									=     Path(__file__).resolve().parent.parent.parent
__UpperCamelCase		:       int									=     repo_dir / 'model_cards'

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






import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:





					class 					lowercase__						:


											@staticmethod
											def        __A       (       *UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				Optional[Any]						):





																	'''simple docstring'''





																	pass





@is_pipeline_test
@require_vision
@require_torch
class 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							[
												    {
												        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												    }
												]
												return object_detector, examples


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(examples[0]    ,		threshold=0.0						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							len(UpperCamelCase__						)
												self.assertGreater(UpperCamelCase__    ,		0						)
												self.assertEqual(
												    UpperCamelCase__    ,		[
												        {
												            '''score''': ANY(UpperCamelCase__						),
												            '''label''': ANY(UpperCamelCase__						),
												            '''box''': {'''xmin''': ANY(UpperCamelCase__						), '''ymin''': ANY(UpperCamelCase__						), '''xmax''': ANY(UpperCamelCase__						), '''ymax''': ANY(UpperCamelCase__						)},
												        }
												        for i in range(UpperCamelCase__						)
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass


						@require_torch
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												        {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    [
												        {
												            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        }
												    ]    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												            {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												            {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        ]
												    ]    ,		)


						@require_torch
						@slow
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												        {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												        {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							object_detector(
												    [
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												    ]    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				str						):





												'''simple docstring'''





												pass


						@require_torch
						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							0.2
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												    ]    ,		)



						@require_torch
						@slow
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							2
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		top_k=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												    ]    ,		)





