




"""simple docstring"""


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

import os
from pathlib import Path


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					str		,   snake_case__							:					Optional[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  {
      """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]
  _snake_case							:		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],
  }
  _snake_case							:		Union[str, Any]								=  F"{src_lang}-{tgt_lang}"

  _snake_case							:		Dict								=  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=snake_case__		,   exist_ok=snake_case__    )
  _snake_case							:		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_						=				Path(__file__).resolve().parent.parent.parent
A_						=				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_						=				model_cards_dir / '''allenai''' / model_name
     write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)






def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


