
'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A			      =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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







'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							int = 10_00):
				lowerCamelCase					:			Optional[Any]									= 2**power
				lowerCamelCase					:			str									= str(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= list(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0

				for i in list_num:
								sum_of_num += int(UpperCAmelCase__)

				return sum_of_num


if __name__ == "__main__":
					A			      =      int(input('Enter the power of 2: ').strip())
					print('2 ^ ', power, ' = ', 2**power)
					A			      =      solution(power)
					print('Sum of the digits is: ', result)
