


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase       :      Tuple	=	get_tests_dir("fixtures/test_sentencepiece_bpe.model")






class       _UpperCamelCase    (					__UpperCAmelCase    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= BartphoTokenizer
				lowerCAmelCase 		= False
				lowerCAmelCase 		= True

				def      _UpperCAmelCase					(						self   )	->				str:
					super().setUp()

					A								=				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
					A								=				dict(zip(lowerCAmelCase_				,   range(len(lowerCAmelCase_   )   )   )   )
					A								=				{"""unk_token""": """<unk>"""}

					A								=				os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["""monolingual_vocab_file"""]   )
					with open(self.monolingual_vocab_file				,   """w"""				,   encoding="""utf-8"""   ) as fp:
						for token in vocab_tokens:
							fp.write(f'{token} {vocab_tokens[token]}\n'   )

					A								=				BartphoTokenizer(lowerCAmelCase_				,   self.monolingual_vocab_file				,   **self.special_tokens_map   )
					tokenizer.save_pretrained(self.tmpdirname   )

				def      _UpperCAmelCase					(						self				,   **a__   )	->				Optional[int]:
					kwargs.update(self.special_tokens_map   )
					return BartphoTokenizer.from_pretrained(self.tmpdirname				,   **lowerCAmelCase_   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[Any]:
					A								=				"""This is a là test"""
					A								=				"""This is a<unk><unk> test"""
					return input_text, output_text

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				BartphoTokenizer(lowerCAmelCase_				,   self.monolingual_vocab_file				,   **self.special_tokens_map   )
					A								=				"""This is a là test"""
					A								=				"""▁This ▁is ▁a ▁l à ▁t est""".split()
					A								=				tokenizer.tokenize(lowerCAmelCase_   )
					self.assertListEqual(lowerCAmelCase_				,   lowerCAmelCase_   )

					A								=				tokens + [tokenizer.unk_token]
					A								=				[4, 5, 6, 3, 3, 7, 8, 3]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_   )				,   lowerCAmelCase_   )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



