code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  # Usage:
  # ./gen-card-allenai-wmt19.py
  
  import os
  from pathlib import Path
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = {
  		    "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]
  		_snake_case			 = {
  		    "wmt19-de-en-6-6-base": [0, 38.37],
  		    "wmt19-de-en-6-6-big": [0, 39.90],
  		}
  		_snake_case			 = f'''{src_lang}-{tgt_lang}'''
  
  		_snake_case			 = 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_						)
  		_snake_case			 = 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							:		List[str]										=						Path(__file__).resolve().parent.parent.parent
  _lowerCamelCase							:		List[Any]										=						repo_dir / 'model_cards'
  
  for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
  						_lowerCamelCase							:		int										=						model_cards_dir / 'allenai' / model_name
  						write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCamelCase							:		int										=						logging.get_logger(__name__)
  
  _lowerCamelCase							:		Any										=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCamelCase							:		Dict										=						{
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCamelCase							:		List[str]										=						{
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCamelCase							:		Tuple										=						{
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				VOCAB_FILES_NAMES
  					lowercase__					=				PRETRAINED_VOCAB_FILES_MAP
  					lowercase__					=				PRETRAINED_INIT_CONFIGURATION
  					lowercase__					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					lowercase__					=				MPNetTokenizer
  					lowercase__					=				["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=True,						__UpperCamelCase="<s>",						__UpperCamelCase="</s>",						__UpperCamelCase="</s>",						__UpperCamelCase="<s>",						__UpperCamelCase="[UNK]",						__UpperCamelCase="<pad>",						__UpperCamelCase="<mask>",						__UpperCamelCase=True,						__UpperCamelCase=None,						**__UpperCamelCase,						):
  							super().__init__(
  							    __UpperCamelCase,						tokenizer_file=__UpperCamelCase,						do_lower_case=__UpperCamelCase,						bos_token=__UpperCamelCase,						eos_token=__UpperCamelCase,						sep_token=__UpperCamelCase,						cls_token=__UpperCamelCase,						unk_token=__UpperCamelCase,						pad_token=__UpperCamelCase,						mask_token=__UpperCamelCase,						tokenize_chinese_chars=__UpperCamelCase,						strip_accents=__UpperCamelCase,						**__UpperCamelCase,						)
  
  							_snake_case			 = json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
  							if (
  							    pre_tok_state.get("lowercase",						__UpperCamelCase				) != do_lower_case
  							    or pre_tok_state.get("strip_accents",						__UpperCamelCase				) != strip_accents
  							):
  									_snake_case			 = getattr(__UpperCamelCase,						pre_tok_state.pop("type"				)				)
  									_snake_case			 = do_lower_case
  									_snake_case			 = strip_accents
  									_snake_case			 = pre_tok_class(**__UpperCamelCase				)
  
  							_snake_case			 = do_lower_case
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if self._mask_token is None:
  									if self.verbose:
  											logger.error("Using mask_token, but it is not set yet."				)
  									return None
  							return str(self._mask_token				)
  
  
  
  
  
  
  					@mask_token.setter
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = AddedToken(__UpperCamelCase,						lstrip=__UpperCamelCase,						rstrip=__UpperCamelCase				) if isinstance(__UpperCamelCase,						__UpperCamelCase				) else value
  							_snake_case			 = value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase=None				):
  							_snake_case			 = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
  							if token_ids_a is None:
  									return output
  
  							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
  							_snake_case			 = [self.sep_token_id]
  							_snake_case			 = [self.cls_token_id]
  
  							if token_ids_a is None:
  									return len(cls + token_ids_a + sep				) * [0]
  							return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
  							_snake_case			 = self._tokenizer.model.save(__UpperCamelCase,						name=__UpperCamelCase				)
  							return tuple(__UpperCamelCase				)
  
  
  
  
  
  