
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a									=				{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMForMaskedLM',
        'LayoutLMForSequenceClassification',
        'LayoutLMForTokenClassification',
        'LayoutLMForQuestionAnswering',
        'LayoutLMModel',
        'LayoutLMPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFLayoutLMForMaskedLM',
        'TFLayoutLMForSequenceClassification',
        'TFLayoutLMForTokenClassification',
        'TFLayoutLMForQuestionAnswering',
        'TFLayoutLMMainLayer',
        'TFLayoutLMModel',
        'TFLayoutLMPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

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







UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

