




# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  {
      '''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]
  a_		    =  {
      '''wmt19-de-en-6-6-base''': [0, 38.37],
      '''wmt19-de-en-6-6-big''': [0, 39.90],
  }
  a_		    =  f"{src_lang}-{tgt_lang}"

  a_		    =  f"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"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\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
  model_card_dir.mkdir(parents=_UpperCAmelCase      ,      exist_ok=_UpperCAmelCase			)
  a_		    =  os.path.join(_UpperCAmelCase      ,      '''README.md'''			)
  print(f"Generating {path}"			)
  with open(_UpperCAmelCase      ,      '''w'''      ,      encoding='''utf-8'''			) as f:
    f.write(_UpperCAmelCase			)


# make sure we are under the root of the project
UpperCamelCase__       				=	Path(__file__).resolve().parent.parent.parent
UpperCamelCase__       				=	repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
  UpperCamelCase__       				=	model_cards_dir / '''allenai''' / model_name
  write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__A       ={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizerFast"]


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez import BarthezTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





