code: 
  
  
  
  
  
  # Usage:
  # ./gen-card-allenai-wmt19.py
  
  import os
  from pathlib import Path
  
  def     a						(      _lowercase   :     Tuple ,						_lowercase   :     str ,						_lowercase   :     List[str] ,						_lowercase   :     Union[str, Any]       )     ->Dict:
  		UpperCamelCase__         =	{
  		    """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__         =	{
  		    """wmt19-de-en-6-6-base""": [0, 38.37],
  		    """wmt19-de-en-6-6-big""": [0, 39.90],
  		}
  		UpperCamelCase__         =	F"""{src_lang}-{tgt_lang}"""
  
  		UpperCamelCase__         =	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       )
  		UpperCamelCase__         =	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
  __lowerCAmelCase     =			Path(__file__).resolve().parent.parent.parent
  __lowerCAmelCase     =			repo_dir / "model_cards"
  
  for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
  				__lowerCAmelCase     =			model_cards_dir / "allenai" / model_name
  				write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
  				if not swapped:
  						break  # Stop iteration if the collection is sorted.
  		return collection
  
  
  if __name__ == "__main__":
  				import doctest
  				import time
  
  				doctest.testmod()
  
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  