code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  from pathlib import Path
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   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]
        lowercase			:   int					   =		{
            '''wmt19-de-en-6-6-base''': [0, 3_8.3_7],
            '''wmt19-de-en-6-6-big''': [0, 3_9.9_0],
        }
        lowercase			:   Union[str, Any]					   =		f"""{src_lang}-{tgt_lang}"""
  
        lowercase			:   List[Any]					   =		f"""
  ---
  
  language:
  - {src_lang}
  - {tgt_lang}
  thumbnail:
  tags:
  - translation
  - wmt19
  - allenai
  license: apache-2.0
  datasets:
  - wmt19
  metrics:
  - bleu
  ---
  
  # FSMT
  
  ## Model description
  
  This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
  
  For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
  
  2 models are available:
  
  * [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
  * [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
  
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import FSMTForConditionalGeneration, FSMTTokenizer
  mname = \"allenai/{model_name}\"
  tokenizer = FSMTTokenizer.from_pretrained(mname)
  model = FSMTForConditionalGeneration.from_pretrained(mname)
  
  input = \"{texts[src_lang]}\"
  input_ids = tokenizer.encode(input, return_tensors=\"pt\")
  outputs = model.generate(input_ids)
  decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print(decoded) # {texts[tgt_lang]}
  
  ```
  
  #### Limitations and bias
  
  
  ## Training data
  
  Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
  
  ## Eval results
  
  Here are the BLEU scores:
  
  model   |  transformers
  -------|---------
  {model_name}  |  {scores[model_name][1]}
  
  The score was calculated using this code:
  
  ```bash
  git clone https://github.com/huggingface/transformers
  cd transformers
  export PAIR={pair}
  export DATA_DIR=data/$PAIR
  export SAVE_DIR=data/$PAIR
  export BS=8
  export NUM_BEAMS=5
  mkdir -p $DATA_DIR
  sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
  sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
  echo $PAIR
  PYTHONPATH=\"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
  ```
  
  ## Data Sources
  
  - [training, etc.](http://www.statmt.org/wmt19/)
  - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
  
  
  ### BibTeX entry and citation info
  
  ```
  @misc{{kasai2020deep,
      title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
      author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
      year={{2020}},
      eprint={{2006.10369}},
      archivePrefix={{arXiv}},
      primaryClass={{cs.CL}}
  }}
  ```
  
  """
        model_card_dir.mkdir(parents=lowercase_,    exist_ok=lowercase_     )
        lowercase			:   Dict					   =		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   				=      Path(__file__).resolve().parent.parent.parent
  __a   				=      repo_dir / 'model_cards'
  
  for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
    __a   				=      model_cards_dir / 'allenai' / model_name
    write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)
  
  
style_context: 
  
  
  import math
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		0
        if n % 2 == 0:
              count += 1
              while n % 2 == 0:
                    lowercase			:   Any					   =		int(n / 2     )
      # the n input value must be odd so that
      # we can skip one element (ie i += 2)
  
        lowercase			:   Optional[int]					   =		3
  
        while i <= int(math.sqrt(_UpperCamelCase     )     ):
              if n % i == 0:
                    count += 1
                    while n % i == 0:
                          lowercase			:   Optional[Any]					   =		int(n / i     )
              lowercase			:   str					   =		i + 2
  
        # this condition checks the prime
        # number n is greater than 2
  
        if n > 2:
              count += 1
        return count
  
  
  if __name__ == "__main__":
    __a   				=      51_24_21_83
    print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
    print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')
  
  