import math
def 				__lowercase	(						lowerCamelCase__					)				->list[int]:

  '''simple docstring'''




  if not isinstance(lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     f"Input value of [number={number}] must be an integer"
    raise TypeError(lowerCamelCase__					)
  if number < 1:
    a__     		=     f"Input value of [number={number}] must be a positive integer"
    raise ValueError(lowerCamelCase__					)
  a__     		=     [number]
  while number != 1:
    if number % 2 == 0:
      a__     		=     math.floor(math.sqrt(lowerCamelCase__					)					)
    else:
      a__     		=     math.floor(
          math.sqrt(lowerCamelCase__					) * math.sqrt(lowerCamelCase__					) * math.sqrt(lowerCamelCase__					)					)
    sequence.append(lowerCamelCase__					)
  return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   						=    {
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    [
         """OpenLlamaForCausalLM""",
         """OpenLlamaModel""",
         """OpenLlamaPreTrainedModel""",
         """OpenLlamaForSequenceClassification""",
     ]


if TYPE_CHECKING:
     from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_open_llama import (
              OpenLlamaForCausalLM,
              OpenLlamaForSequenceClassification,
              OpenLlamaModel,
              OpenLlamaPreTrainedModel,
          )


else:
     import sys

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

