


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

import os
from pathlib import Path






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->List[str]:


 '''simple docstring'''
 snake_case :			Optional[Any]							=   {
     '''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]
 snake_case :			List[str]							=   {
     '''wmt19-de-en-6-6-base''': [0, 38.37],
     '''wmt19-de-en-6-6-big''': [0, 39.90],
 }
 snake_case :			Any							=   f'''{src_lang}-{tgt_lang}'''

 snake_case :			Optional[Any]							=   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							)
 snake_case :			Union[str, Any]							=   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
a					:				List[str]       =      Path(__file__).resolve().parent.parent.parent
a					:				int       =      repo_dir / 'model_cards'

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=10			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   []
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255			,  size=(image_processor_tester.num_channels, width, height)			,  dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  snake_case :			Optional[int]							=   [Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for frame in video]

 if torchify:
  snake_case :			int							=   [torch.from_numpy(__lowerCamelCase							) for frame in video]

 return video






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 snake_case :			Any							=   []
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   snake_case :			Optional[int]							=   image_processor_tester.max_resolution
  else:
   snake_case	,       snake_case :			str							=   np.random.choice(
       np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution							)			,  2							)
   snake_case :			Any							=   prepare_video(
       image_processor_tester=__lowerCamelCase			,  width=__lowerCamelCase			,  height=__lowerCamelCase			,  numpify=__lowerCamelCase			,  torchify=__lowerCamelCase			,  )
  video_inputs.append(__lowerCamelCase							)

 return video_inputs

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=7						,    a__=3						,    a__=4						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    a__=True						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Tuple							=   size if size is not None else {'''shortest_edge''': 18}
        snake_case :			List[str]							=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        snake_case :			Optional[Any]							=   parent
        snake_case :			List[Any]							=   batch_size
        snake_case :			Optional[int]							=   num_channels
        snake_case :			Union[str, Any]							=   num_frames
        snake_case :			List[Any]							=   image_size
        snake_case :			Tuple							=   min_resolution
        snake_case :			List[Any]							=   max_resolution
        snake_case :			Tuple							=   do_resize
        snake_case :			List[Any]							=   size
        snake_case :			List[Any]							=   do_normalize
        snake_case :			List[Any]							=   image_mean
        snake_case :			List[str]							=   image_std
        snake_case :			Tuple							=   do_center_crop
        snake_case :			str							=   crop_size



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   TvltImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   TvltImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(a__						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(a__						,    '''image_std'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_center_crop'''       )       )
        self.assertTrue(hasattr(a__						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL videos
        snake_case :			List[str]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[Any]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Union[str, Any]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    numpify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    np.ndarray       )

        # Test not batched input
        snake_case :			List[str]							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Tuple							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			str							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    torchify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Tuple							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[int]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )






