

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     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =BartphoTokenizer
							__UpperCAmelCase       =False
							__UpperCAmelCase       =True




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

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

												__lowerCAmelCase					=							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"""							)

												__lowerCAmelCase					=							BartphoTokenizer(_A					,						self.monolingual_vocab_file					,						**self.special_tokens_map							)
												tokenizer.save_pretrained(self.tmpdirname							)




							def        A__  ( self					,						**_A							):
												kwargs.update(self.special_tokens_map							)
												return BartphoTokenizer.from_pretrained(self.tmpdirname					,						**_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							'This is a là test'
												__lowerCAmelCase					=							'This is a<unk><unk> test'
												return input_text, output_text




							def        A__  ( self							):
												__lowerCAmelCase					=							BartphoTokenizer(_A					,						self.monolingual_vocab_file					,						**self.special_tokens_map							)
												__lowerCAmelCase					=							'This is a là test'
												__lowerCAmelCase					=							'▁This ▁is ▁a ▁l à ▁t est'.split()
												__lowerCAmelCase					=							tokenizer.tokenize(_A							)
												self.assertListEqual(_A					,						_A							)

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

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






