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

import os
from pathlib import Path



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		{
					    '''en''': '''Machine learning is great, isn\'t it?''',
					    '''ru''': '''Машинное обучение - это здорово, не так ли?''',
					    '''de''': '''Maschinelles Lernen ist großartig, oder?''',
					}

					# BLUE scores as follows:
					# "pair": [fairseq, transformers]
					lowercase					=		{
					    '''ru-en''': ['''[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)''', '''39.20'''],
					    '''en-ru''': ['''[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)''', '''33.47'''],
					    '''en-de''': ['''[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)''', '''42.83'''],
					    '''de-en''': ['''[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)''', '''41.35'''],
					}
					lowercase					=		f'{src_lang}-{tgt_lang}'

					lowercase					=		f'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
					os.makedirs(lowerCAmelCase__				,    exist_ok=lowerCAmelCase__	)
					lowercase					=		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
lowercase__	:int										=					Path(__file__).resolve().parent.parent.parent
lowercase__	:Optional[Any]										=					repo_dir / "model_cards"

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowercase__						,  lowercase__						,  lowercase__	:Any										=					model_name.split("-")
							lowercase__	:Optional[int]										=					model_cards_dir / "facebook" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

					# Iterate over image & apply kernel
					for x in range(image.shape[1]	):
										for y in range(image.shape[0]	):
															lowercase					=		(
															    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
															).sum()
															lowercase					=		int(summation > 0	)
					return output


if __name__ == "__main__":
							# read original image
							lowercase__	:str										=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
							lowercase__	:List[str]										=					np.array(Image.open(lena_path))
							# kernel to be applied
							lowercase__	:Union[str, Any]										=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
							lowercase__	:Optional[int]										=					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
							# Save the output image
							lowercase__	:str										=					Image.fromarray(output).convert("RGB")
							pil_img.save("result_dilation.png")




