





def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 9 / 5) + 32	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(float(snake_case       ) + 273.15	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 9 / 5) + 491.67	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) - 32) * 5 / 9	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(((float(snake_case       ) - 32) * 5 / 9) + 273.15	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(float(snake_case       ) + 459.67	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(float(snake_case       ) - 273.15	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(((float(snake_case       ) - 273.15) * 9 / 5) + 32	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 9 / 5)	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) - 491.67) * 5 / 9	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round(float(snake_case       ) - 459.67	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 5 / 9)	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 1.25 + 273.15)	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 2.25 + 32)	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 1.25)	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case = 2       )		->						float:
		return round((float(snake_case       ) * 2.25 + 32 + 459.67)	,      snake_case       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()











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

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    '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]
		__lowercase					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\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-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\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\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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=5\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 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\n```\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```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
		model_card_dir.mkdir(parents=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    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
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

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




