


'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=							{
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							["""OpenAIGPTTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """OpenAIGPTDoubleHeadsModel""",
		    """OpenAIGPTForSequenceClassification""",
		    """OpenAIGPTLMHeadModel""",
		    """OpenAIGPTModel""",
		    """OpenAIGPTPreTrainedModel""",
		    """load_tf_weights_in_openai_gpt""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFOpenAIGPTDoubleHeadsModel""",
		    """TFOpenAIGPTForSequenceClassification""",
		    """TFOpenAIGPTLMHeadModel""",
		    """TFOpenAIGPTMainLayer""",
		    """TFOpenAIGPTModel""",
		    """TFOpenAIGPTPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

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

def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				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_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


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






