
"""simple docstring"""







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							lowerCamelCase__        =		get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCamelCase__        =		128_022
lowerCamelCase__        =		128_028




@require_sentencepiece
class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	Union[str, Any]           =     MaMaaaTokenizer
		A_						:	Union[str, Any]           =     False
		A_						:	List[str]           =     False
		A_						:	Union[str, Any]           =     True

		def 							__lowerCamelCase							(				self			):
			super().setUp()

			__lowerCAmelCase      :						Dict						      =				['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
			__lowerCAmelCase      :						List[str]						      =				dict(zip(_SCREAMING_SNAKE_CASE	,		range(len(_SCREAMING_SNAKE_CASE			)			)			)			)
			__lowerCAmelCase      :						Union[str, Any]						      =				Path(self.tmpdirname			)
			save_json(_SCREAMING_SNAKE_CASE	,		save_dir / VOCAB_FILES_NAMES['vocab_file']			)
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
				copyfile(_SCREAMING_SNAKE_CASE	,		save_dir / VOCAB_FILES_NAMES['spm_file']			)

			__lowerCAmelCase      :						int						      =				MaMaaaTokenizer.from_pretrained(self.tmpdirname			)
			tokenizer.save_pretrained(self.tmpdirname			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return MaMaaaTokenizer.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			return (
			    "This is a test",
			    "This is a test",
			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'</s>'
			__lowerCAmelCase      :						Dict						      =				0

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE			)	,		_SCREAMING_SNAKE_CASE			)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE			)	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.get_tokenizer()
			__lowerCAmelCase      :						Optional[int]						      =				list(tokenizer.get_vocab().keys()			)

			self.assertEqual(vocab_keys[0]	,		'</s>'			)
			self.assertEqual(vocab_keys[1]	,		'<unk>'			)
			self.assertEqual(vocab_keys[-1]	,		'<s>'			)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		tokenizer.vocab_size + len(tokenizer.get_added_vocab()			)			)

		@unittest.skip('Skip this test while all models are still to be uploaded.'			)
		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Any						      =				tokenizer.tokenize('This is a test'			)
			self.assertListEqual(_SCREAMING_SNAKE_CASE	,		['▁This', '▁is', '▁a', '▁t', 'est']			)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE			)	,		[2, 3, 4, 5, 6]	,		)

			__lowerCAmelCase      :						int						      =				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]			)
			self.assertListEqual(_SCREAMING_SNAKE_CASE	,		['▁This', '▁is', '▁a', '▁t', 'est']			)

			__lowerCAmelCase      :						Tuple						      =				tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE			)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		'This is a test'			)







		@slow
		def 							__lowerCamelCase							(				self			):
			# fmt: off
			__lowerCAmelCase      :						str						      =				{'input_ids': [[12_80_22, 11_01_08, 3_97, 11, 3_82_72, 22_47, 12_48_11, 2_85, 1_81_05, 15_86, 2_07, 7, 3_95_34, 44_28, 3_97, 10_19, 1_81_05, 15_86, 2_07, 7, 4_13_37, 1_67_86, 2_41, 7, 2_02_14, 17, 12_56_90, 1_03_98, 7, 4_43_78, 5_80_69, 6_83_42, 77_98, 73_43, 11, 2_99, 3_33_10, 4, 1_58, 3_73_50, 9_40_77, 45_69, 2_99, 3_33_10, 90, 4, 5_28_40, 2_90, 4, 3_12_70, 1_12, 2_99, 6_82, 4, 5_28_40, 3_99_53, 1_40_79, 1_93, 5_25_19, 9_08_94, 1_78_94, 12_06_97, 11, 4_04_45, 5_51, 17, 10_19, 5_25_19, 9_08_94, 1_77_56, 9_63, 11, 4_04_45, 4_80, 17, 97_92, 11_20, 51_73, 13_93, 62_40, 1_67_86, 2_41, 12_09_96, 28, 12_45, 13_93, 11_82_40, 1_11_23, 10_19, 9_36_12, 26_91, 1_06_18, 9_80_58, 12_04_09, 19_28, 2_79, 4, 4_06_83, 3_67, 1_78, 2_07, 10_19, 1_03, 10_31_21, 5_06, 6_52_96, 5, 2], [12_80_22, 2_12_17, 3_67, 1_17, 12_54_50, 1_28, 7_19, 7, 73_08, 40, 9_36_12, 1_26_69, 11_16, 1_67_04, 71, 1_77_85, 36_99, 1_55_92, 35, 1_44, 95_84, 2_41, 1_19_43, 7_13, 9_50, 7_99, 22_47, 8_84_27, 1_50, 1_49, 11_88_13, 12_07_06, 10_19, 10_69_06, 8_15_18, 28, 12_24, 2_27_99, 3_97, 5, 2, 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, 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], [12_80_22, 16_58, 12_33_11, 51_55, 55_78, 47_22, 2_79, 1_49_47, 23_66, 11_20, 11_97, 14, 13_48, 92_32, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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, 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, 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, 1, 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], [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, 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]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=_SCREAMING_SNAKE_CASE	,		model_name='facebook/m2m100_418M'	,		revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'	,		)




@require_torch
@require_sentencepiece
@require_tokenizers
class 			A__	(     unittest.TestCase):
		A_						:	Optional[int]           =     'facebook/m2m100_418M'
		A_						:	Tuple           =     [
		    'In my opinion, there are two levels of response from the French government.',
		    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
		]
		A_						:	Optional[Any]           =     [
		    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
		    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
		]

		# fmt: off
		A_						:	List[str]           =     [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]

		@classmethod
		def 							__lowerCamelCase							(				cls			):
			__lowerCAmelCase      :						MaMaaaTokenizer						      =				MaMaaaTokenizer.from_pretrained(
			    cls.checkpoint_name	,		src_lang='en'	,		tgt_lang='fr'			)
			__lowerCAmelCase      :						Dict						      =				1
			return cls

		def 							__lowerCamelCase							(				self			):
			self.assertEqual(self.tokenizer.get_lang_id('ar'			)	,		12_80_06			)
			self.assertEqual(self.tokenizer.get_lang_id('en'			)	,		12_80_22			)
			self.assertEqual(self.tokenizer.get_lang_id('ro'			)	,		12_80_76			)
			self.assertEqual(self.tokenizer.get_lang_id('mr'			)	,		12_80_63			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.tokenizer.get_vocab()
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		self.tokenizer.vocab_size			)
			self.assertEqual(vocab['<unk>']	,		3			)
			self.assertIn(self.tokenizer.get_lang_token('en'			)	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'en'
			__lowerCAmelCase      :						Union[str, Any]						      =				self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
			self.assertListEqual(self.expected_src_tokens	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			self.assertIn(_SCREAMING_SNAKE_CASE	,		self.tokenizer.all_special_ids			)
			# fmt: off
			__lowerCAmelCase      :						int						      =				[FR_CODE, 53_64, 82, 86_42, 4, 2_94, 47, 8, 1_40_28, 1_36, 32_86, 97_06, 6, 9_07_97, 6, 14_40_12, 1_62, 8_81_28, 3_00_61, 5, 2]
			# fmt: on
			__lowerCAmelCase      :						Any						      =				self.tokenizer.decode(_SCREAMING_SNAKE_CASE	,		skip_special_tokens=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				self.tokenizer.decode(generated_ids[1:]	,		skip_special_tokens=_SCREAMING_SNAKE_CASE			)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			self.assertNotIn(self.tokenizer.eos_token	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				tempfile.mkdtemp()
			__lowerCAmelCase      :						Tuple						      =				self.tokenizer.lang_token_to_id
			self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				MaMaaaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)
			self.assertDictEqual(new_tok.lang_token_to_id	,		_SCREAMING_SNAKE_CASE			)

		@require_torch
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'en'
			__lowerCAmelCase      :						Tuple						      =				'fr'

			__lowerCAmelCase      :						int						      =				self.tokenizer(self.src_text	,		text_target=self.tgt_text	,		padding=_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			)

			__lowerCAmelCase      :						Dict						      =				shift_tokens_right(
			    batch['labels']	,		self.tokenizer.pad_token_id	,		self.tokenizer.eos_token_id			)

			for k in batch:
				__lowerCAmelCase      :						int						      =				batch[k].tolist()
			# batch = {k: v.tolist() for k,v in batch.items()}
			# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
			# batch.decoder_inputs_ids[0][0] ==
			assert batch.input_ids[1][0] == EN_CODE
			assert batch.input_ids[1][-1] == 2
			assert batch.labels[1][0] == FR_CODE
			assert batch.labels[1][-1] == 2
			assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

		@require_torch
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'mr'
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id('mr'			)]			)
			self.assertListEqual(self.tokenizer.suffix_tokens	,		[self.tokenizer.eos_token_id]			)

			__lowerCAmelCase      :						Union[str, Any]						      =				'zh'
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id('zh'			)]			)
			self.assertListEqual(self.tokenizer.suffix_tokens	,		[self.tokenizer.eos_token_id]			)

		@require_torch
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'mr'
			self.tokenizer._switch_to_target_mode()
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id('mr'			)]			)
			self.assertListEqual(self.tokenizer.suffix_tokens	,		[self.tokenizer.eos_token_id]			)
			self.tokenizer._switch_to_input_mode()
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)

			__lowerCAmelCase      :						Optional[Any]						      =				'zh'
			self.tokenizer._switch_to_target_mode()
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id('zh'			)]			)
			self.assertListEqual(self.tokenizer.suffix_tokens	,		[self.tokenizer.eos_token_id]			)
			self.tokenizer._switch_to_input_mode()
			self.assertListEqual(self.tokenizer.prefix_tokens	,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)







		@require_torch
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.tokenizer._build_translation_inputs('A test'	,		return_tensors='pt'	,		src_lang='en'	,		tgt_lang='ar'			)

			self.assertEqual(
			    nested_simplify(_SCREAMING_SNAKE_CASE			)	,		{
			        # en_XX, A, test, EOS
			        'input_ids': [[12_80_22, 58, 41_83, 2]],
			        'attention_mask': [[1, 1, 1, 1]],
			        # ar_AR
			        'forced_bos_token_id': 12_80_06,
			    }	,		)

"""simple docstring"""







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

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    '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]
	__lowerCAmelCase      :						Union[str, Any]						      =				{
	    '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'],
	}
	__lowerCAmelCase      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

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


# 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)