import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 						lowercase    (     lowerCAmelCase__     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					Tuple = "▁"	,							_UpperCamelCase   :					Optional[int] = True	,							_UpperCamelCase   :					Union[str, Any] = "<unk>"	,							_UpperCamelCase   :					Dict = "</s>"	,							_UpperCamelCase   :					Dict = "<pad>"	,							)					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "pad": {"id": 0, "token": pad_token},
												    "eos": {"id": 1, "token": eos_token},
												    "unk": {"id": 2, "token": unk_token},
												}

												SCREAMING_SNAKE_CASE    			=       [None] * len(self.special_tokens      )
												for token_dict in self.special_tokens.values():
																	SCREAMING_SNAKE_CASE    			=       token_dict["token"]

												SCREAMING_SNAKE_CASE    			=       Tokenizer(Unigram()      )

												SCREAMING_SNAKE_CASE    			=       normalizers.Sequence(
												    [
												        normalizers.Nmt(),
												        normalizers.NFKC(),
												        normalizers.Replace(Regex(" {2,}"      )	,							" "      ),
												        normalizers.Lowercase(),
												    ]      )
												SCREAMING_SNAKE_CASE    			=       pre_tokenizers.Sequence(
												    [
												        pre_tokenizers.Metaspace(replacement=_lowerCamelCase	,							add_prefix_space=_lowerCamelCase      ),
												        pre_tokenizers.Digits(individual_digits=_lowerCamelCase      ),
												        pre_tokenizers.Punctuation(),
												    ]      )
												SCREAMING_SNAKE_CASE    			=       decoders.Metaspace(replacement=_lowerCamelCase	,							add_prefix_space=_lowerCamelCase      )

												SCREAMING_SNAKE_CASE    			=       TemplateProcessing(
												    single=F"$A {self.special_tokens['eos']['token']}"	,							special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]	,							)

												SCREAMING_SNAKE_CASE    			=       {
												    "model": "SentencePieceUnigram",
												    "replacement": replacement,
												    "add_prefix_space": add_prefix_space,
												}

												super().__init__(_lowerCamelCase	,							_lowerCamelCase      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict = 8_000	,							_UpperCamelCase   :					List[Any] = True	,							)					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       trainers.UnigramTrainer(
												    vocab_size=_lowerCamelCase	,							special_tokens=self.special_tokens_list	,							show_progress=_lowerCamelCase	,							)

												if isinstance(_lowerCamelCase	,							_lowerCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       [files]
												self._tokenizer.train(_lowerCamelCase	,							trainer=_lowerCamelCase      )

												self.add_unk_id()




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Any = 8_000	,							_UpperCamelCase   :					Tuple = True	,							)					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       trainers.UnigramTrainer(
												    vocab_size=_lowerCamelCase	,							special_tokens=self.special_tokens_list	,							show_progress=_lowerCamelCase	,							)

												self._tokenizer.train_from_iterator(_lowerCamelCase	,							trainer=_lowerCamelCase      )

												self.add_unk_id()





							def 	__snake_case(						self   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       json.loads(self._tokenizer.to_str()      )

												SCREAMING_SNAKE_CASE    			=       self.special_tokens["unk"]["id"]

												SCREAMING_SNAKE_CASE    			=       Tokenizer.from_str(json.dumps(_lowerCamelCase      )      )





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




