
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


__lowerCAmelCase					   =       get_tests_dir('fixtures/test_sentencepiece_bpe.model')





class     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					List[str]    		=	BartphoTokenizer
	UpperCamelCase_  :					Any    		=	False
	UpperCamelCase_  :					Any    		=	True







	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				super().setUp()

				SCREAMING_SNAKE_CASE     						= ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
				SCREAMING_SNAKE_CASE     						= dict(zip(lowercase   ,     range(len(lowercase			)			)			)			)
				SCREAMING_SNAKE_CASE     						= {'''unk_token''': '''<unk>'''}

				SCREAMING_SNAKE_CASE     						= 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"""			)

				SCREAMING_SNAKE_CASE     						= BartphoTokenizer(lowercase   ,     self.monolingual_vocab_file   ,     **self.special_tokens_map			)
				tokenizer.save_pretrained(self.tmpdirname			)







	def  snake_case__     (     self      :Dict   ,     **lowercase      :int			)		->      Dict:







				"""simple docstring"""






				kwargs.update(self.special_tokens_map			)
				return BartphoTokenizer.from_pretrained(self.tmpdirname   ,     **lowercase			)







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :str			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''This is a là test'''
				SCREAMING_SNAKE_CASE     						= '''This is a<unk><unk> test'''
				return input_text, output_text







	def  snake_case__     (     self      :Dict			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BartphoTokenizer(lowercase   ,     self.monolingual_vocab_file   ,     **self.special_tokens_map			)
				SCREAMING_SNAKE_CASE     						= '''This is a là test'''
				SCREAMING_SNAKE_CASE     						= '''▁This ▁is ▁a ▁l à ▁t est'''.split()
				SCREAMING_SNAKE_CASE     						= tokenizer.tokenize(lowercase			)
				self.assertListEqual(lowercase   ,     lowercase			)

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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)