






"""simple docstring"""



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       logging.get_logger()

      # the current default level is logging.WARNING
      lowercase							    =       logging.get_verbosity()

      logging.set_verbosity_error()
      self.assertEqual(logger.getEffectiveLevel()							,		logging.get_verbosity()       )

      logging.set_verbosity_warning()
      self.assertEqual(logger.getEffectiveLevel()							,		logging.get_verbosity()       )

      logging.set_verbosity_info()
      self.assertEqual(logger.getEffectiveLevel()							,		logging.get_verbosity()       )

      logging.set_verbosity_debug()
      self.assertEqual(logger.getEffectiveLevel()							,		logging.get_verbosity()       )

      # restore to the original level
      logging.set_verbosity(lowerCAmelCase__       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       logging.get_verbosity()

      lowercase							    =       logging.get_logger('transformers.models.bart.tokenization_bart'       )
      lowercase							    =       'Testing 1, 2, 3'

      # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
      if level_origin <= logging.WARNING:
        with CaptureLogger(lowerCAmelCase__       ) as cl:
          logger.warning(lowerCAmelCase__       )
        self.assertEqual(cl.out							,		msg + '\n'       )

      # this is setting the level for all of `transformers.*` loggers
      logging.set_verbosity_error()

      # should not be able to log warnings
      with CaptureLogger(lowerCAmelCase__       ) as cl:
        logger.warning(lowerCAmelCase__       )
      self.assertEqual(cl.out							,		''       )

      # should be able to log warnings again
      logging.set_verbosity_warning()
      with CaptureLogger(lowerCAmelCase__       ) as cl:
        logger.warning(lowerCAmelCase__       )
      self.assertEqual(cl.out							,		msg + '\n'       )

      # restore to the original level
      logging.set_verbosity(lowerCAmelCase__       )







    @mockenv(TRANSFORMERS_VERBOSITY='error'       )
    def 			UpperCamelCase_      (			self       ):
      # reset for the env var to take effect, next time some logger call is made
      transformers.utils.logging._reset_library_root_logger()
      # this action activates the env var
      lowercase							    =       logging.get_logger('transformers.models.bart.tokenization_bart'       )

      lowercase							    =       os.getenv('TRANSFORMERS_VERBOSITY'							,		lowerCAmelCase__       )
      lowercase							    =       logging.log_levels[env_level_str]

      lowercase							    =       logging.get_verbosity()
      self.assertEqual(
          lowerCAmelCase__							,		lowerCAmelCase__							,		F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'							,		)

      # restore to the original level
      lowercase							    =       ''
      transformers.utils.logging._reset_library_root_logger()







    @mockenv(TRANSFORMERS_VERBOSITY='super-error'       )
    def 			UpperCamelCase_      (			self       ):
      # reset for the env var to take effect, next time some logger call is made
      transformers.utils.logging._reset_library_root_logger()
      lowercase							    =       logging.logging.getLogger()
      with CaptureLogger(lowerCAmelCase__       ) as cl:
        # this action activates the env var
        logging.get_logger('transformers.models.bart.tokenization_bart'       )
      self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'							,		cl.out       )

      # no need to restore as nothing was changed







    def 			UpperCamelCase_      (			self       ):
      # testing `logger.warning_advice()`
      transformers.utils.logging._reset_library_root_logger()

      lowercase							    =       logging.get_logger('transformers.models.bart.tokenization_bart'       )
      lowercase							    =       'Testing 1, 2, 3'

      with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'       ):
        # nothing should be logged as env var disables this method
        with CaptureLogger(lowerCAmelCase__       ) as cl:
          logger.warning_advice(lowerCAmelCase__       )
        self.assertEqual(cl.out							,		''       )

      with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''       ):
        # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
        with CaptureLogger(lowerCAmelCase__       ) as cl:
          logger.warning_advice(lowerCAmelCase__       )
        self.assertEqual(cl.out							,		msg + '\n'       )


def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  disable_progress_bar()
  assert are_progress_bars_disabled()

  enable_progress_bar()
  assert not are_progress_bars_disabled()






"""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)


