

"""simple docstring"""





def        _lowerCAmelCase      (						UpperCAmelCase     :							int = 20				):


					'''simple docstring'''






					try:
										UpperCamelCase__		:    Tuple         =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.'''				)
					UpperCamelCase__		:    List[Any]         =0
					while 1:
										i += n * (n - 1)
										UpperCamelCase__		:    List[str]         =0
										for j in range(2      ,							UpperCAmelCase				):
															if i % j != 0:
																				UpperCamelCase__		:    List[str]         =1
																				break
										if nfound == 0:
															if i == 0:
																				UpperCamelCase__		:    str         =1
															return i
					return None


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





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Dict        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


