
import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     RoCBertTokenizer
	_A								=     None
	_A								=     False
	_A								=     True
	_A								=     filter_non_english







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> int:
								super().setUp()

								__UpperCamelCase	:   Tuple			   =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
								__UpperCamelCase	:   Tuple			   =					{}
								__UpperCamelCase	:   List[str]			   =					{}
								for i, value in enumerate(a						):
															__UpperCamelCase	:   Dict			   =					i
															__UpperCamelCase	:   Union[str, Any]			   =					i
								__UpperCamelCase	:   Dict			   =					os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]						)
								__UpperCamelCase	:   Optional[Any]			   =					os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["word_shape_file"]						)
								__UpperCamelCase	:   str			   =					os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["word_pronunciation_file"]						)
								with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"						) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)
								with open(self.word_shape_file     ,   "w"     ,   encoding="utf-8"						) as word_shape_writer:
															json.dump(a     ,   a     ,   ensure_ascii=a						)
								with open(self.word_pronunciation_file     ,   "w"     ,   encoding="utf-8"						) as word_pronunciation_writer:
															json.dump(a     ,   a     ,   ensure_ascii=a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								__UpperCamelCase	:   int			   =					self.tokenizer_class(self.vocab_file     ,   self.word_shape_file     ,   self.word_pronunciation_file						)

								__UpperCamelCase	:   Any			   =					tokenizer.tokenize("你好[SEP]你是谁"						)
								self.assertListEqual(a     ,   ["你", "好", "[SEP]", "你", "是", "谁"]						)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a						)     ,   [5, 6, 2, 5, 7, 8]						)
								self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(a						)     ,   [5, 6, 2, 5, 7, 8]						)
								self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(a						)     ,   [5, 6, 2, 5, 7, 8]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					RoCBertBasicTokenizer()

								self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"						)     ,   ["ah", "\u535A", "\u63A8", "zz"]						)







	def    _lowerCamelCase				(					self		:str						)		-> Union[str, Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					RoCBertBasicTokenizer(do_lower_case=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "						)     ,   ["hello", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)     ,   ["hello"]						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Any			   =					RoCBertBasicTokenizer(do_lower_case=a     ,   strip_accents=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)     ,   ["hällo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)     ,   ["h\u00E9llo"]						)







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   List[str]			   =					RoCBertBasicTokenizer(do_lower_case=a     ,   strip_accents=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)     ,   ["hallo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)     ,   ["hello"]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					RoCBertBasicTokenizer(do_lower_case=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)     ,   ["hallo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)     ,   ["hello"]						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								__UpperCamelCase	:   int			   =					RoCBertBasicTokenizer(do_lower_case=a						)

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







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[Any]:
								__UpperCamelCase	:   Tuple			   =					RoCBertBasicTokenizer(do_lower_case=a     ,   strip_accents=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)     ,   ["HäLLo", "!", "how", "Are", "yoU", "?"]						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								__UpperCamelCase	:   int			   =					RoCBertBasicTokenizer(do_lower_case=a     ,   strip_accents=a						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)     ,   ["HaLLo", "!", "how", "Are", "yoU", "?"]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> int:
								__UpperCamelCase	:   Optional[int]			   =					RoCBertBasicTokenizer(do_lower_case=a     ,   never_split=["[UNK]"]						)

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







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> List[Any]:
								__UpperCamelCase	:   Tuple			   =					["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

								__UpperCamelCase	:   List[Any]			   =					{}
								for i, token in enumerate(a						):
															__UpperCamelCase	:   List[str]			   =					i
								__UpperCamelCase	:   List[Any]			   =					RoCBertWordpieceTokenizer(vocab=a     ,   unk_token="[UNK]"						)

								self.assertListEqual(tokenizer.tokenize(""						)     ,   []						)

								self.assertListEqual(tokenizer.tokenize("unwanted running"						)     ,   ["un", "##want", "##ed", "runn", "##ing"]						)

								self.assertListEqual(tokenizer.tokenize("unwantedX running"						)     ,   ["[UNK]", "runn", "##ing"]						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict:
								self.assertTrue(_is_whitespace(" "						)						)
								self.assertTrue(_is_whitespace("\t"						)						)
								self.assertTrue(_is_whitespace("\r"						)						)
								self.assertTrue(_is_whitespace("\n"						)						)
								self.assertTrue(_is_whitespace("\u00A0"						)						)

								self.assertFalse(_is_whitespace("A"						)						)
								self.assertFalse(_is_whitespace("-"						)						)







	def    _lowerCamelCase				(					self		:Dict						)		-> Optional[int]:
								self.assertTrue(_is_control("\u0005"						)						)

								self.assertFalse(_is_control("A"						)						)
								self.assertFalse(_is_control(" "						)						)
								self.assertFalse(_is_control("\t"						)						)
								self.assertFalse(_is_control("\r"						)						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Tuple:
								self.assertTrue(_is_punctuation("-"						)						)
								self.assertTrue(_is_punctuation("$"						)						)
								self.assertTrue(_is_punctuation("`"						)						)
								self.assertTrue(_is_punctuation("."						)						)

								self.assertFalse(_is_punctuation("A"						)						)
								self.assertFalse(_is_punctuation(" "						)						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(a						) for t in ["Test", "\xad", "test"]]     ,   [["[UNK]"], [], ["[UNK]"]]						)

								if self.test_rust_tokenizer:
															__UpperCamelCase	:   List[str]			   =					self.get_rust_tokenizer()
															self.assertListEqual(
															    [rust_tokenizer.tokenize(a						) for t in ["Test", "\xad", "test"]]     ,   [["[UNK]"], [], ["[UNK]"]]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> int:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																						__UpperCamelCase	:   Union[str, Any]			   =					self.rust_tokenizer_class.from_pretrained(a     ,   **a						)

																						__UpperCamelCase	:   int			   =					f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
																						__UpperCamelCase	:   int			   =					tokenizer_r.encode_plus(
																						    a     ,   return_attention_mask=a     ,   return_token_type_ids=a     ,   return_offsets_mapping=a     ,   add_special_tokens=a     ,   )

																						__UpperCamelCase	:   Tuple			   =					tokenizer_r.do_lower_case if hasattr(a     ,   "do_lower_case"						) else False
																						__UpperCamelCase	:   str			   =					(
																						    [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), "A"),
																						        ((1, 2), ","),
																						        ((3, 5), "na"),
																						        ((5, 6), "##ï"),
																						        ((6, 8), "##ve"),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), "Allen"),
																						        ((2_1, 2_3), "##NL"),
																						        ((2_3, 2_4), "##P"),
																						        ((2_5, 3_3), "sentence"),
																						        ((3_3, 3_4), "."),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						    if not do_lower_case
																						    else [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), "a"),
																						        ((1, 2), ","),
																						        ((3, 8), "naive"),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), "allen"),
																						        ((2_1, 2_3), "##nl"),
																						        ((2_3, 2_4), "##p"),
																						        ((2_5, 3_3), "sentence"),
																						        ((3_3, 3_4), "."),
																						        ((0, 0), tokenizer_r.sep_token),
																						    ]
																						)

																						self.assertEqual(
																						    [e[1] for e in expected_results]     ,   tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]						)						)
																						self.assertEqual([e[0] for e in expected_results]     ,   tokens["offset_mapping"]						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[int]:
								__UpperCamelCase	:   List[Any]			   =					["的", "人", "有"]
								__UpperCamelCase	:   int			   =					"".join(a						)
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																						__UpperCamelCase	:   str			   =					True
																						__UpperCamelCase	:   Tuple			   =					self.tokenizer_class.from_pretrained(a     ,   **a						)
																						__UpperCamelCase	:   Union[str, Any]			   =					self.rust_tokenizer_class.from_pretrained(a     ,   **a						)

																						__UpperCamelCase	:   Optional[int]			   =					tokenizer_p.encode(a     ,   add_special_tokens=a						)
																						__UpperCamelCase	:   Union[str, Any]			   =					tokenizer_r.encode(a     ,   add_special_tokens=a						)

																						__UpperCamelCase	:   Dict			   =					tokenizer_r.convert_ids_to_tokens(a						)
																						__UpperCamelCase	:   Tuple			   =					tokenizer_p.convert_ids_to_tokens(a						)

																						# it is expected that each Chinese character is not preceded by "##"
																						self.assertListEqual(a     ,   a						)
																						self.assertListEqual(a     ,   a						)

																						__UpperCamelCase	:   Optional[Any]			   =					False
																						__UpperCamelCase	:   Any			   =					self.rust_tokenizer_class.from_pretrained(a     ,   **a						)
																						__UpperCamelCase	:   int			   =					self.tokenizer_class.from_pretrained(a     ,   **a						)

																						__UpperCamelCase	:   str			   =					tokenizer_r.encode(a     ,   add_special_tokens=a						)
																						__UpperCamelCase	:   Union[str, Any]			   =					tokenizer_p.encode(a     ,   add_special_tokens=a						)

																						__UpperCamelCase	:   int			   =					tokenizer_r.convert_ids_to_tokens(a						)
																						__UpperCamelCase	:   Union[str, Any]			   =					tokenizer_p.convert_ids_to_tokens(a						)

																						# it is expected that only the first Chinese character is not preceded by "##".
																						__UpperCamelCase	:   Union[str, Any]			   =					[
																						    f'##{token}' if idx != 0 else token for idx, token in enumerate(a						)
																						]
																						self.assertListEqual(a     ,   a						)
																						self.assertListEqual(a     ,   a						)







	@slow
	def    _lowerCamelCase				(					self		:List[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   List[Any]			   =					self.tokenizer_class(self.vocab_file     ,   self.word_shape_file     ,   self.word_pronunciation_file						)

								__UpperCamelCase	:   Dict			   =					tokenizer.encode("你好"     ,   add_special_tokens=a						)
								__UpperCamelCase	:   Dict			   =					tokenizer.encode("你是谁"     ,   add_special_tokens=a						)

								__UpperCamelCase	:   Optional[Any]			   =					tokenizer.build_inputs_with_special_tokens(a						)
								__UpperCamelCase	:   List[str]			   =					tokenizer.build_inputs_with_special_tokens(a     ,   a						)

								assert encoded_sentence == [1] + text + [2]
								assert encoded_pair == [1] + text + [2] + text_a + [2]







	def    _lowerCamelCase				(					self		:List[str]						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					self.get_tokenizers(do_lower_case=a						)
								for tokenizer in tokenizers:
															with self.subTest(f'{tokenizer.__class__.__name__}'						):
																						__UpperCamelCase	:   Union[str, Any]			   =					"你好，你是谁"
																						__UpperCamelCase	:   str			   =					tokenizer.tokenize(a						)
																						__UpperCamelCase	:   Optional[int]			   =					tokenizer.convert_tokens_to_ids(a						)
																						__UpperCamelCase	:   int			   =					tokenizer.convert_tokens_to_shape_ids(a						)
																						__UpperCamelCase	:   Dict			   =					tokenizer.convert_tokens_to_pronunciation_ids(a						)
																						__UpperCamelCase	:   Tuple			   =					tokenizer.prepare_for_model(
																						    a     ,   a     ,   a     ,   add_special_tokens=a						)

																						__UpperCamelCase	:   List[str]			   =					tokenizer.encode_plus(a     ,   add_special_tokens=a						)

																						self.assertEqual(a     ,   a						)

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

import os
from pathlib import Path




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Any)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[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]
							__UpperCamelCase	:   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"],
							}
							__UpperCamelCase	:   str			   =					F'{src_lang}-{tgt_lang}'

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


# make sure we are under the root of the project
lowercase  :					List[str]													=       Path(__file__).resolve().parent.parent.parent
lowercase  :					Union[str, Any]													=       repo_dir / 'model_cards'

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