


'''simple docstring'''

import os
from pathlib import Path






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Any				)      ->     int:







     '''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						=		{
         '''wmt16-en-de-dist-12-1''': [28.3, 27.52],
         '''wmt16-en-de-dist-6-1''': [27.4, 27.11],
         '''wmt16-en-de-12-1''': [26.9, 25.75],
     }
     _a						=		F"{src_lang}-{tgt_lang}"

     _a						=		F"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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=a_      ,  exist_ok=a_				)
     _a						=		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
lowercase__							=		Path(__file__).resolve().parent.parent.parent
lowercase__							=		repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
   lowercase__							=		model_cards_dir / "allenai" / model_name
   write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ,			lowerCamelCase__ ):






     def  a_					(			self		)       ->    Tuple:
          _a						=		load_tool("text-classification"		)
          self.tool.setup()
          _a						=		load_tool("text-classification"     ,							remote=__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.remote_tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    str:
          _a						=		self.remote_tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)

