





import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
     from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
     from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
     from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__A									=				DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__A									=				"main"
# Default branch name
__A									=				"f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__A									=				"aaaaaaa"
# This commit does not exist, so we should 404.

__A									=				"d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__A									=				"4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"





@contextlib.contextmanager
def      lowerCAmelCase_ (       )     ->		List[Any]:







  """simple docstring"""





  print("Welcome!"		)
  yield
  print("Bye!"		)





@contextlib.contextmanager
def      lowerCAmelCase_ (       )     ->		int:







  """simple docstring"""





  print("Bonjour!"		)
  yield
  print("Au revoir!"		)



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   assert transformers.__spec__ is not None
   assert importlib.util.find_spec("transformers") is not None



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @unittest.mock.patch("sys.stdout"       , new_callable=io.StringIO)
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   List[Any])       ->Dict:



   '''simple docstring'''



   with ContextManagers([]):
     print("Transformers are awesome!")
   # The print statement adds a new line at the end of the output
   self.assertEqual(mock_stdout.getvalue()       , "Transformers are awesome!\n")

 @unittest.mock.patch("sys.stdout"       , new_callable=io.StringIO)
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[str])       ->Any:



   '''simple docstring'''



   with ContextManagers([context_en()]):
     print("Transformers are awesome!")
   # The output should be wrapped with an English welcome and goodbye
   self.assertEqual(mock_stdout.getvalue()       , "Welcome!\nTransformers are awesome!\nBye!\n")

 @unittest.mock.patch("sys.stdout"       , new_callable=io.StringIO)
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict)       ->List[Any]:



   '''simple docstring'''



   with ContextManagers([context_fr(), context_en()]):
     print("Transformers are awesome!")
   # The output should be wrapped with an English and French welcome and goodbye
   self.assertEqual(mock_stdout.getvalue()       , "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n")

 @require_torch
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Union[str, Any]:



   '''simple docstring'''



   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels"])
   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels", "next_sentence_label"])
   self.assertEqual(find_labels(UpperCAmelCase_)       , ["start_positions", "end_positions"])



   class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







    '''simple docstring'''
    pass



   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels"])

 @require_tf
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels"])
   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels", "next_sentence_label"])
   self.assertEqual(find_labels(UpperCAmelCase_)       , ["start_positions", "end_positions"])



   class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







    '''simple docstring'''
    pass



   self.assertEqual(find_labels(UpperCAmelCase_)       , ["labels"])







 @require_flax
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   self.assertEqual(find_labels(UpperCAmelCase_)       , [])
   self.assertEqual(find_labels(UpperCAmelCase_)       , [])
   self.assertEqual(find_labels(UpperCAmelCase_)       , [])



   class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







    '''simple docstring'''
    pass



   self.assertEqual(find_labels(UpperCAmelCase_)       , [])





"""simple docstring"""




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

import os
from pathlib import Path


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					{
						    '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]
						SCREAMING_SNAKE_CASE													=					{
						    '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'],
						}
						SCREAMING_SNAKE_CASE													=					F'{src_lang}-{tgt_lang}'

						SCREAMING_SNAKE_CASE													=					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(SCREAMING_SNAKE_CASE_ ,			exist_ok=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			'README.md'  )
						print(F'Generating {path}'  )
						with open(SCREAMING_SNAKE_CASE_ ,			'w' ,			encoding='utf-8'  ) as f:
												f.write(SCREAMING_SNAKE_CASE_  )


# 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-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	__UpperCamelCase,__UpperCamelCase,__UpperCamelCase						=      model_name.split('''-''')
	__UpperCamelCase						=      model_cards_dir / '''facebook''' / model_name
	write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)


