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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    {
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    int         =    [
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    Dict         =    [
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )
  
  else:
   import sys
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  