

from typing import TYPE_CHECKING

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


a__												=						{
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__												=						[
			    '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''MobileBertForMaskedLM''',
			    '''MobileBertForMultipleChoice''',
			    '''MobileBertForNextSentencePrediction''',
			    '''MobileBertForPreTraining''',
			    '''MobileBertForQuestionAnswering''',
			    '''MobileBertForSequenceClassification''',
			    '''MobileBertForTokenClassification''',
			    '''MobileBertLayer''',
			    '''MobileBertModel''',
			    '''MobileBertPreTrainedModel''',
			    '''load_tf_weights_in_mobilebert''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__												=						[
			    '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFMobileBertForMaskedLM''',
			    '''TFMobileBertForMultipleChoice''',
			    '''TFMobileBertForNextSentencePrediction''',
			    '''TFMobileBertForPreTraining''',
			    '''TFMobileBertForQuestionAnswering''',
			    '''TFMobileBertForSequenceClassification''',
			    '''TFMobileBertForTokenClassification''',
			    '''TFMobileBertMainLayer''',
			    '''TFMobileBertModel''',
			    '''TFMobileBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_mobilebert import (
			    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileBertConfig,
			    MobileBertOnnxConfig,
			)
			from .tokenization_mobilebert import MobileBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mobilebert_fast import MobileBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilebert import (
						    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileBertForMaskedLM,
						    MobileBertForMultipleChoice,
						    MobileBertForNextSentencePrediction,
						    MobileBertForPreTraining,
						    MobileBertForQuestionAnswering,
						    MobileBertForSequenceClassification,
						    MobileBertForTokenClassification,
						    MobileBertLayer,
						    MobileBertModel,
						    MobileBertPreTrainedModel,
						    load_tf_weights_in_mobilebert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_mobilebert import (
						    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertMainLayer,
						    TFMobileBertModel,
						    TFMobileBertPreTrainedModel,
						)

else:
			import sys

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






def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



