
"""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,
)


_lowercase							=				{
    '''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:
					_lowercase							=				['''BertTokenizerFast''']

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

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

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







"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__ = 1_0      )						->str:
			if not isinstance(__magic_name__			,   __magic_name__      ) or n < 0:
						raise ValueError("Invalid input"      )
			__lowercase       =       1_0**n
			__lowercase       =       2_8_4_3_3 * (pow(2			,   7_8_3_0_4_5_7			,   __magic_name__      )) + 1
			return str(number % modulus      )


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

					testmod()
					print(F"{solution(10) = }")





