


"""simple docstring"""


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,
)


UpperCamelCase						:				Any             =      {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				str             =      ["AlbertTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				List[str]             =      ["AlbertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				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:
	UpperCamelCase						:				Optional[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:
	UpperCamelCase						:				List[Any]             =      [
	    "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

	UpperCamelCase						:				Union[str, Any]             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""


import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     TransfoXLTokenizer
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Union[str, Any]		):
								super().setUp()

								A					       = [
								    '<unk>',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    'unwanted',
								    'wa',
								    'un',
								    'running',
								    ',',
								    'low',
								    'l',
								]
								A					       = 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 __a						(							self   :    int			,			**_lowercase   :    List[str]		):
								A					       = True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    List[Any]		):
								A					       = '<unk> UNwanted , running'
								A					       = '<unk> unwanted, running'
								return input_text, output_text







						def __a						(							self   :    List[str]		):
								A					       = TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=_lowercase		)

								A					       = tokenizer.tokenize('<unk> UNwanted , running'		)
								self.assertListEqual(_lowercase			,			['<unk>', 'unwanted', ',', 'running']		)

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







						def __a						(							self   :    int		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

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







						def __a						(							self   :    Optional[Any]		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

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







						def __a						(							self   :    str		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)
								A					       = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
								A					       = [
								    'Hello',
								    '(',
								    'bracket',
								    ')',
								    'and',
								    'side',
								    '@-@',
								    'scrolled',
								    '[',
								    'and',
								    ']',
								    'Henry',
								    '\'s',
								    '$',
								    '5',
								    '@,@',
								    '000',
								    'with',
								    '3',
								    '@.@',
								    '34',
								    'm',
								    '.',
								    'What',
								    '\'s',
								    'up',
								    '!',
								    '?',
								]

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

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







						def __a						(							self   :    Dict		):
								A					       = self.get_tokenizer()
								A					       = len(_lowercase		)

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

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





