



'''simple docstring'''






import math
import os

import cva
import numpy as np

A_							:				Optional[Any]						=			255.0




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_	)->	float:





							'''simple docstring'''


							_UpperCAmelCase						:		str      =				np.mean((original - contrast) ** 2	)
							if mse == 0:
														return 100

							return 20 * math.logaa(PIXEL_MAX / math.sqrt(__a	)	)




def 					snake_case_      (  )->	None:





							'''simple docstring'''


							_UpperCAmelCase						:		str      =				os.path.dirname(os.path.realpath(__a	)	)
							# Loading images (original image and compressed image)
							_UpperCAmelCase						:		Union[str, Any]      =				cva.imread(os.path.join(__a ,     """image_data/original_image.png"""	)	)
							_UpperCAmelCase						:		Optional[Any]      =				cva.imread(os.path.join(__a ,     """image_data/compressed_image.png"""	) ,     1	)

							_UpperCAmelCase						:		str      =				cva.imread(os.path.join(__a ,     """image_data/PSNR-example-base.png"""	)	)
							_UpperCAmelCase						:		Optional[int]      =				cva.imread(
							    os.path.join(__a ,     """image_data/PSNR-example-comp-10.jpg"""	) ,     1	)

							# Value expected: 29.73dB
							print("""-- First Test --"""	)
							print(F'''PSNR value is {peak_signal_to_noise_ratio(__a ,     __a	)} dB'''	)

							# # Value expected: 31.53dB (Wikipedia Example)
							print("""\n-- Second Test --"""	)
							print(F'''PSNR value is {peak_signal_to_noise_ratio(__a ,     __a	)} dB'''	)


if __name__ == "__main__":
			main()








'''simple docstring'''






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

import os
from pathlib import Path




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_	)->	str:





       '''simple docstring'''


       _UpperCAmelCase						:		Optional[int]      =				{
           """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						:		List[str]      =				{
           """wmt19-de-en-6-6-base""": [0, 3_8.3_7],
           """wmt19-de-en-6-6-big""": [0, 3_9.9_0],
       }
       _UpperCAmelCase						:		str      =				F'''{src_lang}-{tgt_lang}'''

       _UpperCAmelCase						:		List[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_	)
       _UpperCAmelCase						:		Optional[int]      =				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_							:				Dict						=			Path(__file__).resolve().parent.parent.parent
A_							:				Tuple						=			repo_dir / """model_cards"""

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



