





"""simple docstring"""


from typing import TYPE_CHECKING

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


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

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     str							=				[
       """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:
   a__					:     List[Any]							=				[
       """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

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""


def 							a__			(  _UpperCAmelCase = 1_0_0_0	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				[]
 snake_case__    							=				[]
 snake_case__    							=				3
 snake_case__    							=				5
 snake_case__    							=				1
 while True:
  snake_case__    							=				z * temp
  if result < n:
   zmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   snake_case__    							=				1
   break
 while True:
  snake_case__    							=				x * temp
  if result < n:
   xmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   break
 snake_case__    							=				list(set(xmulti + zmulti	)	)
 return sum(_UpperCAmelCase	)


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

