






"""simple docstring"""





import os
from pathlib import Path







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """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'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe 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.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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=SCREAMING_SNAKE_CASE ,	exist_ok=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'README.md'					)
    print(F'Generating {path}'					)
    with open(SCREAMING_SNAKE_CASE ,	'w' ,	encoding='utf-8'					) as f:
        f.write(SCREAMING_SNAKE_CASE					)


# make sure we are under the root of the project
A__					: List[str]=	Path(__file__).resolve().parent.parent.parent
A__					: Dict=	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__					: Optional[Any]=	model_cards_dir / """allenai""" / model_name
  write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00_00_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    set()
    UpperCamelCase__         =    int((limit - 24) ** (1 / 2)					)

    UpperCamelCase__         =    set(range(3 ,	prime_square_limit + 1 ,	2					)					)
    primes.add(2					)
    for p in range(3 ,	prime_square_limit + 1 ,	2					):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p ,	prime_square_limit + 1 ,	SCREAMING_SNAKE_CASE					)					)					)

    for primea in primes:
        UpperCamelCase__         =    primea * primea
        for primea in primes:
            UpperCamelCase__         =    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                UpperCamelCase__         =    primea * primea * primea * primea
                UpperCamelCase__         =    square + cube + tetr
                if total >= limit:
                    break
                ret.add(SCREAMING_SNAKE_CASE					)

    return len(SCREAMING_SNAKE_CASE					)


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




