
"""simple docstring"""





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

import os
from pathlib import Path



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		Optional[int]:


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


# make sure we are under the root of the project
_a														=		Path(__file__).resolve().parent.parent.parent
_a														=		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														=		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 numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






