






'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A:      List[Any]  				=				get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 							__magic_name__     (	__lowerCAmelCase,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Dict        =  GPTSwaTokenizer
	SCREAMING_SNAKE_CASE_					:      str        =  False
	SCREAMING_SNAKE_CASE_					:      Dict        =  True
	SCREAMING_SNAKE_CASE_					:      str        =  False
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_      :   Dict        =			GPTSwaTokenizer(_UpperCamelCase  ,      eos_token='<unk>'  ,      bos_token='<unk>'  ,      pad_token='<unk>'       )

							tokenizer.save_pretrained(self.tmpdirname       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Optional[int]        =			"""This is a test"""
							lowercase_      :   Any        =			"""This is a test"""
							return input_text, output_text
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   List[str]        =			"""<s>"""
							lowercase_      :   str        =			1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase       )  ,      _UpperCamelCase       )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase       )  ,      _UpperCamelCase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Tuple        =			list(self.get_tokenizer().get_vocab().keys()       )

							self.assertEqual(vocab_keys[0]  ,      '<unk>'       )
							self.assertEqual(vocab_keys[1]  ,      '<s>'       )
							self.assertEqual(vocab_keys[-1]  ,      'j'       )
							self.assertEqual(len(_UpperCamelCase       )  ,      2000       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							self.assertEqual(self.get_tokenizer().vocab_size  ,      2000       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Optional[Any]        =			GPTSwaTokenizer(_UpperCamelCase       )

							lowercase_      :   Optional[int]        =			tokenizer.tokenize('This is a test'       )
							self.assertListEqual(_UpperCamelCase  ,      ['▁This', '▁is', '▁a', '▁t', 'est']       )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase       )  ,      [465, 287, 265, 631, 842]       )

							lowercase_      :   Union[str, Any]        =			tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
							# fmt: off
							self.assertListEqual(
							    _UpperCamelCase  ,      ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']  ,      )
							# fmt: on

							lowercase_      :   Any        =			tokenizer.convert_tokens_to_ids(_UpperCamelCase       )
							self.assertListEqual(
							    _UpperCamelCase  ,      [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]  ,      )

							lowercase_      :   List[Any]        =			tokenizer.convert_ids_to_tokens(_UpperCamelCase       )
							# fmt: off
							self.assertListEqual(
							    _UpperCamelCase  ,      ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']       )
							# fmt: on
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   List[str]        =			GPTSwaTokenizer(_UpperCamelCase       )
							lowercase_      :   Optional[int]        =			["""This is a test""", """I was born in 92000, and this is falsé."""]
							lowercase_      :   Optional[Any]        =			[
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(_UpperCamelCase  ,      _UpperCamelCase       ):
													self.assertListEqual(tokenizer.encode_fast(_UpperCamelCase       )  ,      _UpperCamelCase       )

							# Test that decode_fast returns the input text
							for text, token_ids in zip(_UpperCamelCase  ,      _UpperCamelCase       ):
													self.assertEqual(tokenizer.decode_fast(_UpperCamelCase       )  ,      _UpperCamelCase       )







	@slow
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   Dict        =			[
							    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
							    """Hey there, how are you doing this fine day?""",
							    """This is a text with a trailing spaces followed by a dot     .""",
							    """Häj sväjs lillebrör! =)""",
							    """Det är inget fel på Mr. Cool""",
							]

							# fmt: off
							lowercase_      :   List[str]        =			{"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCamelCase  ,      model_name='AI-Sweden/gpt-sw3-126m'  ,      sequences=_UpperCamelCase  ,      )







'''simple docstring'''

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

import os
from pathlib import Path






def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Optional[int]						,			a		:	Tuple							)			->			Optional[int]:





						"""simple docstring"""
						lowercase_      :   Any        =			{
						    '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]
						lowercase_      :   List[str]        =			{
						    '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'],
						}
						lowercase_      :   Optional[Any]        =			f"{src_lang}-{tgt_lang}"

						lowercase_      :   Optional[Any]        =			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							)
						lowercase_      :   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
A:      List[str]  				=				Path(__file__).resolve().parent.parent.parent
A:      List[str]  				=				repo_dir / "model_cards"

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