





"""simple docstring"""






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

import os
from pathlib import Path

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     {
           '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]
       _UpperCAmelCase     =     {
           '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],
       }
       _UpperCAmelCase     =     F"""{src_lang}-{tgt_lang}"""

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


# make sure we are under the root of the project
lowerCAmelCase_			=							Path(__file__).resolve().parent.parent.parent
lowerCAmelCase_			=							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"]:
    lowerCAmelCase_			=							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 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 ChineseCLIPImageProcessor



class 	lowerCAmelCase					(      unittest.TestCase							):






       def __init__(   self    ,  a__    ,  a__=7    ,  a__=3    ,  a__=18    ,  a__=30    ,  a__=4_00    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=[0.48_145_466, 0.4_578_275, 0.40_821_073]    ,  a__=[0.26_862_954, 0.26_130_258, 0.27_577_711]    ,  a__=True    ,  ):
              _UpperCAmelCase     =     size if size is not None else {'height': 2_24, 'width': 2_24}
              _UpperCAmelCase     =     crop_size if crop_size is not None else {'height': 18, 'width': 18}
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     num_channels
              _UpperCAmelCase     =     image_size
              _UpperCAmelCase     =     min_resolution
              _UpperCAmelCase     =     max_resolution
              _UpperCAmelCase     =     do_resize
              _UpperCAmelCase     =     size
              _UpperCAmelCase     =     do_center_crop
              _UpperCAmelCase     =     crop_size
              _UpperCAmelCase     =     do_normalize
              _UpperCAmelCase     =     image_mean
              _UpperCAmelCase     =     image_std
              _UpperCAmelCase     =     do_convert_rgb






       def 							__A (   self     ):
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
                  "do_convert_rgb": self.do_convert_rgb,
              }






       def 							__A (   self    ,  a__=False    ,  a__=False    ,  a__=False     ):
              assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

              if equal_resolution:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            image_inputs.append(
                                np.random.randint(
                                    2_55    ,  size=(self.num_channels, self.max_resolution, self.max_resolution)    ,  dtype=np.uinta     )     )
              else:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     np.random.choice(np.arange(self.min_resolution    ,  self.max_resolution     )    ,  2     )
                            image_inputs.append(np.random.randint(2_55    ,  size=(self.num_channels, width, height)    ,  dtype=np.uinta     )     )

              if not numpify and not torchify:
                     # PIL expects the channel dimension as last dimension
                     _UpperCAmelCase     =     [Image.fromarray(np.moveaxis(a__    ,  0    ,  -1     )     ) for x in image_inputs]

              if torchify:
                     _UpperCAmelCase     =     [torch.from_numpy(a__     ) for x in image_inputs]

              return image_inputs



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  do_center_crop=a__     )






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     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_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class.from_dict(self.image_processor_dict     )
              self.assertEqual(image_processor.size    ,  {'height': 2_24, 'width': 2_24}     )
              self.assertEqual(image_processor.crop_size    ,  {'height': 18, 'width': 18}     )

              _UpperCAmelCase     =     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 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

              # Test not batched input
              _UpperCAmelCase     =     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
              _UpperCAmelCase     =     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 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random numpy tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  numpify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  np.ndarray     )

              # Test not batched input
              _UpperCAmelCase     =     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
              _UpperCAmelCase     =     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 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PyTorch tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  torchify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  torch.Tensor     )

              # Test not batched input
              _UpperCAmelCase     =     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
              _UpperCAmelCase     =     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'],
                  )    ,  )



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  num_channels=4    ,  do_center_crop=a__     )
              _UpperCAmelCase     =     3






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     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_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

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

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






