code: 
  
  
  # Usage:
  # ./gen-card-allenai-wmt19.py
  
  import os
  from pathlib import Path
  
  
  
  
  def       a__						(		snake_case__				:       List[Any]	,      snake_case__				:       Tuple	,      snake_case__				:       Optional[Any]	,      snake_case__				:       int    ):
  		_UpperCAmelCase		:       Optional[int]       =    {
  		    """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]
  		_UpperCAmelCase		:       List[str]       =    {
  		    """wmt19-de-en-6-6-base""": [0, 38.37],
  		    """wmt19-de-en-6-6-big""": [0, 39.90],
  		}
  		_UpperCAmelCase		:       List[str]       =    f'''{src_lang}-{tgt_lang}'''
  
  		_UpperCAmelCase		:       Optional[int]       =    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=snake_case__	,      exist_ok=snake_case__    )
  		_UpperCAmelCase		:       Tuple       =    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
  SCREAMING_SNAKE_CASE__				:    Tuple         =    Path(__file__).resolve().parent.parent.parent
  SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    repo_dir / 'model_cards'
  
  for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
  	SCREAMING_SNAKE_CASE__				:    Any         =    model_cards_dir / 'allenai' / model_name
  	write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)
  
  
  
  
  
  
style_context: 
  
  
  from itertools import count
  from math import asin, pi, sqrt
  
  
  
  
  def       a__						(		snake_case__				:       float    ):
    return (
        (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
    ) / 4
  
  
  
  
  def       a__						(		snake_case__				:       int    ):
    _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
        2 * (circles_number**2 + 1)
    )
    _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y
  
    _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
    _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
        1 / 2    ) - circle_bottom_arc_integral(snake_case__    )
  
    return triangle_area + concave_region_area
  
  
  
  
  def       a__						(		snake_case__				:       float = 1 / 1000    ):
    _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4
  
    for n in count(1    ):
      if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
        return n
  
    return -1
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  