


from typing import TYPE_CHECKING

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


__lowerCAmelCase	: Dict  				={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: List[str]  				=['AlbertTokenizer']

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: Optional[Any]  				=[
						    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'AlbertForMaskedLM',
						    'AlbertForMultipleChoice',
						    'AlbertForPreTraining',
						    'AlbertForQuestionAnswering',
						    'AlbertForSequenceClassification',
						    'AlbertForTokenClassification',
						    'AlbertModel',
						    'AlbertPreTrainedModel',
						    'load_tf_weights_in_albert',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: Any  				=[
						    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFAlbertForMaskedLM',
						    'TFAlbertForMultipleChoice',
						    'TFAlbertForPreTraining',
						    'TFAlbertForQuestionAnswering',
						    'TFAlbertForSequenceClassification',
						    'TFAlbertForTokenClassification',
						    'TFAlbertMainLayer',
						    'TFAlbertModel',
						    'TFAlbertPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: Optional[int]  				=[
						    'FlaxAlbertForMaskedLM',
						    'FlaxAlbertForMultipleChoice',
						    'FlaxAlbertForPreTraining',
						    'FlaxAlbertForQuestionAnswering',
						    'FlaxAlbertForSequenceClassification',
						    'FlaxAlbertForTokenClassification',
						    'FlaxAlbertModel',
						    'FlaxAlbertPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_albert import AlbertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_albert_fast import AlbertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_albert import (
												    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AlbertForMaskedLM,
												    AlbertForMultipleChoice,
												    AlbertForPreTraining,
												    AlbertForQuestionAnswering,
												    AlbertForSequenceClassification,
												    AlbertForTokenClassification,
												    AlbertModel,
												    AlbertPreTrainedModel,
												    load_tf_weights_in_albert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_albert import (
												    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFAlbertForMaskedLM,
												    TFAlbertForMultipleChoice,
												    TFAlbertForPreTraining,
												    TFAlbertForQuestionAnswering,
												    TFAlbertForSequenceClassification,
												    TFAlbertForTokenClassification,
												    TFAlbertMainLayer,
												    TFAlbertModel,
												    TFAlbertPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_albert import (
												    FlaxAlbertForMaskedLM,
												    FlaxAlbertForMultipleChoice,
												    FlaxAlbertForPreTraining,
												    FlaxAlbertForQuestionAnswering,
												    FlaxAlbertForSequenceClassification,
												    FlaxAlbertForTokenClassification,
												    FlaxAlbertModel,
												    FlaxAlbertPreTrainedModel,
												)
else:
						import sys

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




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		TransfoXLTokenizer
				SCREAMING_SNAKE_CASE__				:			int						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		False


				def 		__magic_name__(       self			:str	)		->			Dict:
										super().setUp()

										__SCREAMING_SNAKE_CASE						:     List[str]					=		[
										    '''<unk>''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''unwanted''',
										    '''wa''',
										    '''un''',
										    '''running''',
										    ''',''',
										    '''low''',
										    '''l''',
										]
										__SCREAMING_SNAKE_CASE						:     Any					=		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']	)
										with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8'''	) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)


				def 		__magic_name__(       self			:Any			,			**lowerCAmelCase__			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		True
										return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:List[Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Dict					=		'''<unk> UNwanted , running'''
										__SCREAMING_SNAKE_CASE						:     List[str]					=		'''<unk> unwanted, running'''
										return input_text, output_text


				def 		__magic_name__(       self			:Any	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		tokenizer.tokenize('''<unk> UNwanted , running'''	)
										self.assertListEqual(lowerCAmelCase__			,			['''<unk>''', '''unwanted''', ''',''', '''running''']	)

										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,			[0, 4, 8, 7]	)


				def 		__magic_name__(       self			:Tuple	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Dict	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		[
										    '''Hello''',
										    '''(''',
										    '''bracket''',
										    ''')''',
										    '''and''',
										    '''side''',
										    '''@-@''',
										    '''scrolled''',
										    '''[''',
										    '''and''',
										    ''']''',
										    '''Henry''',
										    '''\'s''',
										    '''$''',
										    '''5''',
										    '''@,@''',
										    '''000''',
										    '''with''',
										    '''3''',
										    '''@.@''',
										    '''34''',
										    '''m''',
										    '''.''',
										    '''What''',
										    '''\'s''',
										    '''up''',
										    '''!''',
										    '''?''',
										]

										self.assertListEqual(tokenizer.tokenize(lowerCAmelCase__	)			,			lowerCAmelCase__	)

										self.assertEqual(tokenizer.convert_tokens_to_string(lowerCAmelCase__	)			,			lowerCAmelCase__	)



				def 		__magic_name__(       self			:str	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     Any					=		len(lowerCAmelCase__	)

										tokenizer.add_tokens(['''new1''', '''new2''']	)
										tokenizer.move_added_token('''new1'''			,			1	)

										# Check that moved token is not copied (duplicate)
										self.assertEqual(len(lowerCAmelCase__	)			,			original_len + 2	)
										# Check that token is moved to specified id
										self.assertEqual(tokenizer.encode('''new1'''	)			,			[1]	)
										self.assertEqual(tokenizer.decode([1]	)			,			'''new1'''	)
