






import os
from pathlib import Path




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      str          =       {
       """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]
   lowercase_		:      str          =       {
       """wmt19-de-en-6-6-base""": [0, 38.37],
       """wmt19-de-en-6-6-big""": [0, 39.90],
   }
   lowercase_		:      List[str]          =       F"""{src_lang}-{tgt_lang}"""

   lowercase_		:      List[str]          =       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=_lowerCamelCase  ,						exist_ok=_lowerCamelCase							)
   lowercase_		:      Dict          =       os.path.join(_lowerCamelCase  ,						"README.md"							)
   print(F"""Generating {path}"""							)
   with open(_lowerCamelCase  ,						"w"  ,						encoding="utf-8"							) as f:
      f.write(_lowerCamelCase							)


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












'''simple docstring'''
UpperCamelCase__	=     (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
UpperCamelCase__	=     (-153, -1, 0, 1.2, 200, "A", [], {}, None)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Initialization of sum and number of digits.
   lowercase_		:      Optional[int]          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Optional[Any]          =       n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   lowercase_		:      int          =       n
   while temp > 0:
      lowercase_		:      Any          =       temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Init a "histogram" of the digits
   lowercase_		:      Optional[Any]          =       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   lowercase_		:      Tuple          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Tuple          =       n
   while temp > 0:
      lowercase_      ,						lowercase_		:      Optional[Any]          =       divmod(_UpperCamelCase  ,						10							)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(_UpperCamelCase  ,						range(len(_UpperCamelCase							)							)							):
      total += cnt * i**digit_total

   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False
   lowercase_		:      List[str]          =       len(str(_UpperCamelCase							)							)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(_UpperCamelCase							) ** expo for i in str(_UpperCamelCase							)							)




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Dict          =       int(input("Enter an integer to see if it is an Armstrong number: "							).strip()							)
   print(F"""{num} is {"" if armstrong_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if narcissistic_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if pluperfect_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()




