






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

import os
from pathlib import Path






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= {
			    '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]
			SCREAMING_SNAKE_CASE						= {
			    '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],
			}
			SCREAMING_SNAKE_CASE						= F'''{src_lang}-{tgt_lang}'''

			SCREAMING_SNAKE_CASE						= 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=_UpperCAmelCase							,       exist_ok=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'README.md')
			print(F'''Generating {path}''')
			with open(_UpperCAmelCase							,       'w'							,       encoding='utf-8') as f:
						f.write(_UpperCAmelCase)


# make sure we are under the root of the project
a_		:						Union[str, Any]             =    Path(__file__).resolve().parent.parent.parent
a_		:						List[Any]             =    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_		:						Optional[int]             =    model_cards_dir / 'allenai' / model_name
				write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})

						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 42})
						self.assertEqual(image_processor.crop_size		,					{'height': 84, 'width': 84})






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)




