"""simple docstring"""





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,
)


__A  =				{
    '''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:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''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:
				__A  =				[
				    '''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:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''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

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
"""simple docstring"""





from functools import lru_cache


def    lowercase_   (   _lowerCamelCase:	int      ) ->      set:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     2
							__lowerCamelCase					:		Tuple		   =     set()
							while i * i <= n:
														if n % i:
																					i += 1
														else:
																					n //= i
																					factors.add(_lowerCamelCase      )
							if n > 1:
														factors.add(_lowerCamelCase      )
							return factors


@lru_cache
def    lowercase_   (   _lowerCamelCase:	int      ) ->      int:



							'''simple docstring'''

							return len(unique_prime_factors(_lowerCamelCase      )      )


def    lowercase_   (   _lowerCamelCase:	list      ) ->      bool:



							'''simple docstring'''

							return len(set(_lowerCamelCase      )      ) in (0, 1)


def    lowercase_   (   _lowerCamelCase:	int      ) ->      list:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     2

							while True:
														# Increment each value of a generated range
														__lowerCamelCase					:		int		   =     [base + i for i in range(_lowerCamelCase      )]

														# Run elements through out unique_prime_factors function
														# Append our target number to the end.
														__lowerCamelCase					:		Dict		   =     [upf_len(_lowerCamelCase      ) for x in group]
														checker.append(_lowerCamelCase      )

														# If all numbers in the list are equal, return the group variable.
														if equality(_lowerCamelCase      ):
																					return group

														# Increment our base variable by 1
														base += 1


def    lowercase_   (   _lowerCamelCase:	int = 4      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     run(_lowerCamelCase      )
							return results[0] if len(_lowerCamelCase      ) else None


if __name__ == "__main__":
				print(solution())