




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,
)


_lowerCAmelCase  :				Any						=				{
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				List[Any]						=				['GPT2TokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Union[str, Any]						=				[
      '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:
  _lowerCAmelCase  :				Union[str, Any]						=				[
      '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:
  _lowerCAmelCase  :				Optional[int]						=				['TFGPT2Tokenizer']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				int						=				['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

  _lowerCAmelCase  :				Union[str, Any]						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











from collections import defaultdict



def   lowerCAmelCase						(						_lowerCAmelCase							:				int = 5		):




 """simple docstring"""







 UpperCAmelCase__								=  defaultdict(_lowerCAmelCase		)
 UpperCAmelCase__								=  0

 while True:
  UpperCAmelCase__								=  get_digits(_lowerCAmelCase		)
  freqs[digits].append(_lowerCAmelCase		)

  if len(freqs[digits]		) == max_base:
   UpperCAmelCase__								=  freqs[digits][0] ** 3
   return base

  num += 1



def   lowerCAmelCase						(						_lowerCAmelCase							:				int		):




 """simple docstring"""







 return "".join(sorted(str(num**3		)		)		)


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





