






'''simple docstring'''

import os
from pathlib import Path
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				{
         """en""": """Machine learning is great, isn\'t it?""",
         """ru""": """Машинное обучение - это здорово, не так ли?""",
         """de""": """Maschinelles Lernen ist großartig, oder?""",
     }

     # BLUE scores as follows:
     # "pair": [fairseq, transformers]
     __lowerCamelCase						:       Optional[Any]				   =				{
         """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
         """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
         """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
         """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
     }
     __lowerCamelCase						:       int				   =				f"{src_lang}-{tgt_lang}"

     __lowerCamelCase						:       Tuple				   =				f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
     os.makedirs(a__,						exist_ok=a__     )
     __lowerCamelCase						:       Optional[int]				   =				os.path.join(a__,						"""README.md"""     )
     print(f"Generating {path}"     )
     with open(a__,						"""w""",						encoding="""utf-8"""     ) as f:
          f.write(a__     )


# make sure we are under the root of the project
UpperCAmelCase__						:List[Any]          =			Path(__file__).resolve().parent.parent.parent
UpperCAmelCase__						:Union[str, Any]          =			repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
   UpperCAmelCase__       ,				UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			model_name.split("""-""")
   UpperCAmelCase__						:Union[str, Any]          =			model_cards_dir / """facebook""" / model_name
   write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)











'''simple docstring'''

from ..utils import DummyObject, requires_backends







class 	SCREAMING_SNAKE_CASE	(      metaclass=lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			['torch', 'torchsde']




       def __init__(					self      : int	,    *A__      : Optional[Any]	,    **A__      : Dict					):


            """simple docstring"""







            requires_backends(self	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Optional[Any]	,    *A__      : str	,    **A__      : Optional[Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    *A__      : Any	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)



