
"""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__)





"""simple docstring"""


def        _lowerCamelCase(__UpperCamelCase = 20			)							->					int:

	try:
		_lowerCAmelCase								=int(__UpperCamelCase			)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""			)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""			)
	_lowerCAmelCase								=0
	while 1:
		i += n * (n - 1)
		_lowerCAmelCase								=0
		for j in range(2       ,					__UpperCamelCase			):
			if i % j != 0:
				_lowerCAmelCase								=1
				break
		if nfound == 0:
			if i == 0:
				_lowerCAmelCase								=1
			return i
	return None


if __name__ == "__main__":
			print(F"""{solution() = }""")



