
def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       bool:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								A__  :	Dict         =							F"Input value of [number={number}] must be an integer"
								raise TypeError(__UpperCamelCase       )
				if number < 0:
								return False
				A__  :	List[str]         =							number * number
				while number > 0:
								if number % 10 != number_square % 10:
												return False
								number //= 10
								number_square //= 10
				return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE    :      int          =			{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			['BertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			[
					    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BertForMaskedLM',
					    'BertForMultipleChoice',
					    'BertForNextSentencePrediction',
					    'BertForPreTraining',
					    'BertForQuestionAnswering',
					    'BertForSequenceClassification',
					    'BertForTokenClassification',
					    'BertLayer',
					    'BertLMHeadModel',
					    'BertModel',
					    'BertPreTrainedModel',
					    'load_tf_weights_in_bert',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Tuple          =			[
					    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFBertEmbeddings',
					    'TFBertForMaskedLM',
					    'TFBertForMultipleChoice',
					    'TFBertForNextSentencePrediction',
					    'TFBertForPreTraining',
					    'TFBertForQuestionAnswering',
					    'TFBertForSequenceClassification',
					    'TFBertForTokenClassification',
					    'TFBertLMHeadModel',
					    'TFBertMainLayer',
					    'TFBertModel',
					    'TFBertPreTrainedModel',
					]
try:
					if not is_tensorflow_text_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Dict          =			['TFBertTokenizer']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			[
					    'FlaxBertForCausalLM',
					    'FlaxBertForMaskedLM',
					    'FlaxBertForMultipleChoice',
					    'FlaxBertForNextSentencePrediction',
					    'FlaxBertForPreTraining',
					    'FlaxBertForQuestionAnswering',
					    'FlaxBertForSequenceClassification',
					    'FlaxBertForTokenClassification',
					    'FlaxBertModel',
					    'FlaxBertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
					from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_fast import BertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bert import (
										    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BertForMaskedLM,
										    BertForMultipleChoice,
										    BertForNextSentencePrediction,
										    BertForPreTraining,
										    BertForQuestionAnswering,
										    BertForSequenceClassification,
										    BertForTokenClassification,
										    BertLayer,
										    BertLMHeadModel,
										    BertModel,
										    BertPreTrainedModel,
										    load_tf_weights_in_bert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bert import (
										    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBertEmbeddings,
										    TFBertForMaskedLM,
										    TFBertForMultipleChoice,
										    TFBertForNextSentencePrediction,
										    TFBertForPreTraining,
										    TFBertForQuestionAnswering,
										    TFBertForSequenceClassification,
										    TFBertForTokenClassification,
										    TFBertLMHeadModel,
										    TFBertMainLayer,
										    TFBertModel,
										    TFBertPreTrainedModel,
										)

					try:
										if not is_tensorflow_text_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_tf import TFBertTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bert import (
										    FlaxBertForCausalLM,
										    FlaxBertForMaskedLM,
										    FlaxBertForMultipleChoice,
										    FlaxBertForNextSentencePrediction,
										    FlaxBertForPreTraining,
										    FlaxBertForQuestionAnswering,
										    FlaxBertForSequenceClassification,
										    FlaxBertForTokenClassification,
										    FlaxBertModel,
										    FlaxBertPreTrainedModel,
										)

else:
					import sys

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