


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,
)


_lowerCAmelCase:						str   				=       {
    '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:
	_lowerCAmelCase:						List[str]   				=       ['BertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Optional[int]   				=       [
	    '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:
	_lowerCAmelCase:						Dict   				=       [
	    '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:
	_lowerCAmelCase:						Dict   				=       ['TFBertTokenizer']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						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

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









"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return abs(lowerCamelCase       ) if a == 0 else greatest_common_divisor(b % a,						lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    y, x % y
				return abs(lowerCamelCase       )





def 				lowercase__		(					):
				try:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    input('Enter two integers separated by comma (,): '       ).split(','       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[0]       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[1]       )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(lowerCamelCase,						lowerCamelCase       )}"""       )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase,						lowerCamelCase       )}"""       )
				except (IndexError, UnboundLocalError, ValueError):
								print('Wrong input'       )


if __name__ == "__main__":
			main()

