





# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       :						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]
   lowerCamelCase__       :						Optional[int]        =							{
       """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],
   }
   lowerCamelCase__       :						Optional[int]        =							f"{src_lang}-{tgt_lang}"

   lowerCamelCase__       :						Optional[Any]        =							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=lowercase__      ,  exist_ok=lowercase__					)
   lowerCamelCase__       :						List[str]        =							os.path.join(lowercase__      ,  '''README.md'''					)
   print(f"Generating {path}"					)
   with open(lowercase__      ,  '''w'''      ,  encoding='''utf-8'''					) as f:
      f.write(lowercase__					)


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










from math import ceil, sqrt



def 	_a						(   UpperCAmelCase = 1000000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							0

   for outer_width in range(3      ,  (limit // 4) + 2					):
      if outer_width**2 > limit:
         lowerCamelCase__       :						List[Any]        =							max(ceil(sqrt(outer_width**2 - limit					)					)      ,  1					)
      else:
         lowerCamelCase__       :						Union[str, Any]        =							1
      if (outer_width - hole_width_lower_bound) % 2:
         hole_width_lower_bound += 1

      answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

   return answer


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



