
"""simple docstring"""




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

import os
from pathlib import Path



def        _lowerCamelCase ( __a,							__a,							__a    ):
		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(__a,							exist_ok=__a    )
		SCREAMING_SNAKE_CASE_						 = 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
lowerCAmelCase__							=				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__							=				repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
						lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__							=				model_name.split('-')
						lowerCAmelCase__							=				model_cards_dir / 'facebook' / model_name
						write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)

"""simple docstring"""




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      TransfoXLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = [
									    '''<unk>''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''want''',
									    '''unwanted''',
									    '''wa''',
									    '''un''',
									    '''running''',
									    ''',''',
									    '''low''',
									    '''l''',
									]
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''<unk> UNwanted , running'''
									SCREAMING_SNAKE_CASE_						 = '''<unk> unwanted, running'''
									return input_text, output_text

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(vocab_file=self.vocab_file       ,       lower_case=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize('''<unk> UNwanted , running'''    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       ['''<unk>''', '''unwanted''', ''',''', '''running''']    )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       [0, 4, 8, 7]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
									SCREAMING_SNAKE_CASE_						 = [
									    '''Hello''',
									    '''(''',
									    '''bracket''',
									    ''')''',
									    '''and''',
									    '''side''',
									    '''@-@''',
									    '''scrolled''',
									    '''[''',
									    '''and''',
									    ''']''',
									    '''Henry''',
									    '''\'s''',
									    '''$''',
									    '''5''',
									    '''@,@''',
									    '''000''',
									    '''with''',
									    '''3''',
									    '''@.@''',
									    '''34''',
									    '''m''',
									    '''.''',
									    '''What''',
									    '''\'s''',
									    '''up''',
									    '''!''',
									    '''?''',
									]

									self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									self.assertEqual(tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizer()
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

									tokenizer.add_tokens(['''new1''', '''new2''']    )
									tokenizer.move_added_token('''new1'''       ,       1    )

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       original_len + 2    )
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode('''new1'''    )       ,       [1]    )
									self.assertEqual(tokenizer.decode([1]    )       ,       '''new1'''    )