

"""simple docstring"""




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

import os
from pathlib import Path
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					{
     """en""": """Machine learning is great, isn't it?""",
     """ru""": """Машинное обучение - это здорово, не так ли?""",
     """de""": """Maschinelles Lernen ist großartig, oder?""",
 }

 # BLUE scores as follows:
 # "pair": [fairseq, transformers]
 UpperCAmelCase__					:  int					   =					{
     """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
     """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
     """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
     """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
 }
 UpperCAmelCase__					:  Dict					   =					F"{src_lang}-{tgt_lang}"

 UpperCAmelCase__					:  List[str]					   =					F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
 os.makedirs(__UpperCamelCase							,    exist_ok=__UpperCamelCase      )
 UpperCAmelCase__					:  Tuple					   =					os.path.join(__UpperCamelCase							,    """README.md"""      )
 print(F"Generating {path}"      )
 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""      ) as f:
  f.write(__UpperCamelCase      )


# make sure we are under the root of the project
__UpperCAmelCase =				Path(__file__).resolve().parent.parent.parent
__UpperCAmelCase =				repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      __UpperCAmelCase,		__UpperCAmelCase,		__UpperCAmelCase =				model_name.split('-')
      __UpperCAmelCase =				model_cards_dir / 'facebook' / model_name
      write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)








"""simple docstring"""




def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					[]
 if len(__UpperCamelCase      ) == 1:
  return [nums.copy()]
 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Tuple					   =					nums.pop(0      )
  UpperCAmelCase__					:  List[str]					   =					permute(__UpperCamelCase      )
  for perm in permutations:
   perm.append(__UpperCamelCase      )
  result.extend(__UpperCamelCase      )
  nums.append(__UpperCamelCase      )
 return result
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 def backtrack(__UpperCamelCase      ):
  if start == len(__UpperCamelCase      ) - 1:
   output.append(nums[:]      )
  else:
   for i in range(__UpperCamelCase							,    len(__UpperCamelCase      )      ):
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					nums[i], nums[start]
    backtrack(start + 1      )
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					nums[i], nums[start]  # backtrack

 UpperCAmelCase__					:  List[str]					   =					[]
 backtrack(0      )
 return output


if __name__ == "__main__":
      import doctest

      # use res to print the data in permute2 function
      __UpperCAmelCase =				permutea([1, 2, 3])
      print(res)
      doctest.testmod()





