




'''simple docstring'''







import os
from pathlib import Path





def 						snake_case_							(_a     :							Union[str, Any]   ,       _a     :							List[str]   ,       _a     :							Any   ,       _a     :							Tuple					):
    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      	=		{
        '''wmt19-de-en-6-6-base''': [0, 38.37],
        '''wmt19-de-en-6-6-big''': [0, 39.90],
    }
    UpperCAmelCase      	=		F"{src_lang}-{tgt_lang}"

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


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










'''simple docstring'''







def 						snake_case_							(_a     :							int = 9_9_8_0_0_1					):
    UpperCAmelCase      	=		0
    for i in range(9_9_9   ,       9_9   ,       -1					):  # 3 digit numbers range from 999 down to 100
        for j in range(9_9_9   ,       9_9   ,       -1					):
            UpperCAmelCase      	=		str(i * j					)
            if product_string == product_string[::-1] and i * j < n:
                UpperCAmelCase      	=		max(_a   ,       i * j					)
    return answer


if __name__ == "__main__":
   print(f"""{solution() = }""")




