



lowercase_													=      {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowercase_													=      {value: key for key, value in encode_dict.items()}






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    str:
					__lowerCAmelCase						 =""""""
					for letter in word.lower():
										if letter.isalpha() or letter == " ":
															encoded += encode_dict[letter]
										else:
															raise Exception("""encode() accepts only letters of the alphabet and spaces"""						)
					return encoded






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    str:
					if set(__lowerCamelCase						) - {"A", "B", " "} != set():
										raise Exception("""decode() accepts only 'A', 'B' and spaces"""						)
					__lowerCAmelCase						 =""""""
					for word in coded.split():
										while len(__lowerCamelCase						) != 0:
															decoded += decode_dict[word[:5]]
															__lowerCAmelCase						 =word[5:]
										decoded += " "
					return decoded.strip()


if __name__ == "__main__":
				from doctest import testmod

				testmod()









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,
)


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

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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      [
				    '''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:
				lowercase_													=      [
				    '''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:
				lowercase_													=      [
				    '''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

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




