


'''simple docstring'''







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

import os
from pathlib import Path






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    Optional[Any]						,lowerCamelCase  :    Tuple						,lowerCamelCase  :    str     ):
       _A  :			Optional[Any]											=      {
           '''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]
       _A  :			List[Any]											=      {
           '''wmt19-de-en-6-6-base''': [0, 38.37],
           '''wmt19-de-en-6-6-big''': [0, 39.90],
       }
       _A  :			List[Any]											=      f'{src_lang}-{tgt_lang}'

       _A  :			Union[str, Any]											=      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_     )
       _A  :			Any											=      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   :  Union[str, Any]       		=				Path(__file__).resolve().parent.parent.parent
A   :  Union[str, Any]       		=				repo_dir / "model_cards"

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










'''simple docstring'''







A   :  Optional[Any]       		=				{
    '''0000''': '''0''',
    '''0001''': '''1''',
    '''0010''': '''2''',
    '''0011''': '''3''',
    '''0100''': '''4''',
    '''0101''': '''5''',
    '''0110''': '''6''',
    '''0111''': '''7''',
    '''1000''': '''8''',
    '''1001''': '''9''',
    '''1010''': '''a''',
    '''1011''': '''b''',
    '''1100''': '''c''',
    '''1101''': '''d''',
    '''1110''': '''e''',
    '''1111''': '''f''',
}






def    lowerCAmelCase__ (       lowerCamelCase  :    str     ):
       _A  :			Union[str, Any]											=      str(lowerCamelCase     ).strip()

       # Exceptions
       if not binary_str:
              raise ValueError('Empty string was passed to the function'     )
       _A  :			Any											=      binary_str[0] == '-'
       _A  :			str											=      binary_str[1:] if is_negative else binary_str
       if not all(char in '01' for char in binary_str     ):
              raise ValueError('Non-binary value was passed to the function'     )

       _A  :			Optional[Any]											=      (
           '0' * (4 * (divmod(len(lowerCamelCase     )						,4     )[0] + 1) - len(lowerCamelCase     )) + binary_str
       )

       _A  :			Optional[int]											=      []
       for x in range(0						,len(lowerCamelCase     )						,4     ):
              hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]     )
       _A  :			int											=      '0x' + ''.join(lowerCamelCase     )

       return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
   from doctest import testmod

   testmod()






