code: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A           =   {
      "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
      "tokenization_gpt2": ["GPT2Tokenizer"],
  }
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["GPT2TokenizerFast"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPT2DoubleHeadsModel",
           "GPT2ForQuestionAnswering",
           "GPT2ForSequenceClassification",
           "GPT2ForTokenClassification",
           "GPT2LMHeadModel",
           "GPT2Model",
           "GPT2PreTrainedModel",
           "load_tf_weights_in_gpt2",
       ]
  
  try:
       if not is_tf_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFGPT2DoubleHeadsModel",
           "TFGPT2ForSequenceClassification",
           "TFGPT2LMHeadModel",
           "TFGPT2MainLayer",
           "TFGPT2Model",
           "TFGPT2PreTrainedModel",
       ]
  
  try:
       if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["TFGPT2Tokenizer"]
  
  try:
       if not is_flax_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
  
  if TYPE_CHECKING:
       from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
       from .tokenization_gpta import GPTaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .tokenization_gpta_fast import GPTaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )
  
       try:
            if not is_tf_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )
  
       try:
            if not is_keras_nlp_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .tokenization_gpta_tf import TFGPTaTokenizer
  
       try:
            if not is_flax_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							int = 1_000			):
        return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)
  
  
  if __name__ == "__main__":
       print(F"""{solution() = }""")
  
  