



'''simple docstring'''



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

import os
from pathlib import Path



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    Union[str, Any]					,		lowerCamelCase :    str					,		lowerCamelCase :    Dict       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	{
	    "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]
	__lowerCAmelCase       				=	{
	    "wmt19-de-en-6-6-base": [0, 3_8.3_7],
	    "wmt19-de-en-6-6-big": [0, 3_9.9_0],
	}
	__lowerCAmelCase       				=	f'''{src_lang}-{tgt_lang}'''

	__lowerCAmelCase       				=	f'''
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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 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 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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


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


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

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
	lowerCAmelCase						:		Dict						      = model_cards_dir / '''allenai''' / model_name
	write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)




'''simple docstring'''



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image






class        UpperCAmelCase__						(		unittest.TestCase				,							UpperCamelCase__			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	load_tool("image-question-answering"  )
						self.tool.setup()
						__lowerCAmelCase       				=	load_tool("image-question-answering"      ,					remote=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )